From f4d4ec33ae7e3444727342c8f3bdf48ac5411d41 Mon Sep 17 00:00:00 2001 From: RUTIKA WAGALEKAR <120473716+RutikaW1155@users.noreply.github.com> Date: Mon, 13 May 2024 19:38:50 +0530 Subject: [PATCH 01/12] Create README.md --- Endometriral Cancer Prediction/README.md | 6 ++++++ 1 file changed, 6 insertions(+) create mode 100644 Endometriral Cancer Prediction/README.md diff --git a/Endometriral Cancer Prediction/README.md b/Endometriral Cancer Prediction/README.md new file mode 100644 index 00000000..e0cf4d2f --- /dev/null +++ b/Endometriral Cancer Prediction/README.md @@ -0,0 +1,6 @@ +About Dataset +Endometrial cancer (also called endometrial carcinoma) starts in the cells of the inner lining of the uterus (the endometrium). This is the most common type of cancer in the uterus. Endometrial carcinomas can be divided into different types based on how the cells look under the microscope. + +What causes uterine cancer? + +Researchers aren’t sure of the exact cause of uterine cancer. Something happens to create changes in cells in your uterus. The mutated cells grow and multiply out of control, which can form a mass called a tumor. From ff7fccca79ab8fa2a1e5953cd64f8a5a1b3245ef Mon Sep 17 00:00:00 2001 From: RUTIKA WAGALEKAR <120473716+RutikaW1155@users.noreply.github.com> Date: Mon, 13 May 2024 23:05:32 +0530 Subject: [PATCH 02/12] Update README.md --- Endometriral Cancer Prediction/README.md | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/Endometriral Cancer Prediction/README.md b/Endometriral Cancer Prediction/README.md index e0cf4d2f..39368da0 100644 --- a/Endometriral Cancer Prediction/README.md +++ b/Endometriral Cancer Prediction/README.md @@ -4,3 +4,13 @@ Endometrial cancer (also called endometrial carcinoma) starts in the cells of th What causes uterine cancer? Researchers aren’t sure of the exact cause of uterine cancer. Something happens to create changes in cells in your uterus. The mutated cells grow and multiply out of control, which can form a mass called a tumor. +Let's take a brief look at the image of Cancerous uterus + + +What is MSI Mantis score? + +The MANTIS score is a score that predicts a patient's MSI status . The higher the MANTIS score is, the more likely a patient is to have the MSI-H status. +What is MSI sensor? + +MSIsensor: microsatellite instability detection using paired tumor-normal sequence data. +The resulting MSIsensor score is a value between 0 and 100 that corresponds to the percentage of mutated microsatellite loci. From 25eb06b7a22915ecfb6a38280f4f096d44f7b219 Mon Sep 17 00:00:00 2001 From: RUTIKA WAGALEKAR <120473716+RutikaW1155@users.noreply.github.com> Date: Tue, 14 May 2024 16:31:54 +0530 Subject: [PATCH 03/12] Update README.md --- Endometriral Cancer Prediction/README.md | 24 ++++++++++++++---------- 1 file changed, 14 insertions(+), 10 deletions(-) diff --git a/Endometriral Cancer Prediction/README.md b/Endometriral Cancer Prediction/README.md index 39368da0..e7b27d0d 100644 --- a/Endometriral Cancer Prediction/README.md +++ b/Endometriral Cancer Prediction/README.md @@ -1,16 +1,20 @@ -About Dataset -Endometrial cancer (also called endometrial carcinoma) starts in the cells of the inner lining of the uterus (the endometrium). This is the most common type of cancer in the uterus. Endometrial carcinomas can be divided into different types based on how the cells look under the microscope. +# Endometrial cancer Prediction Dataset -What causes uterine cancer? +Welcome to the Endometrial cancer Prediction Dataset! 🎉 This dataset contains information about endometrial cancer, also known as endometrial carcinoma, which is a type of cancer that starts in the cells of the inner lining of the uterus (the endometrium). Endometrial carcinomas can be categorized into different types based on cellular characteristics observed under a microscope. -Researchers aren’t sure of the exact cause of uterine cancer. Something happens to create changes in cells in your uterus. The mutated cells grow and multiply out of control, which can form a mass called a tumor. -Let's take a brief look at the image of Cancerous uterus +- [Endometrial cancer Prediction Dataset](https://www.kaggle.com/datasets/yeganehbavafa/uterine-corpus-endometrial-carcinoma) -What is MSI Mantis score? +## Cause of Uterine Cancer -The MANTIS score is a score that predicts a patient's MSI status . The higher the MANTIS score is, the more likely a patient is to have the MSI-H status. -What is MSI sensor? +The exact cause of uterine cancer is not fully understood. However, it is believed that mutations occur in the cells of the uterus, causing them to grow and multiply uncontrollably, leading to the formation of tumors. -MSIsensor: microsatellite instability detection using paired tumor-normal sequence data. -The resulting MSIsensor score is a value between 0 and 100 that corresponds to the percentage of mutated microsatellite loci. +## MSI Mantis Score +The MANTIS score is a predictive score for a patient's MSI (Microsatellite Instability) status. A higher MANTIS score indicates a higher likelihood of MSI-H (high microsatellite instability) status. + +## MSI Sensor +MSIsensor is a tool used for microsatellite instability detection using paired tumor-normal sequence data. The resulting MSIsensor score is a value between 0 and 100, indicating the percentage of mutated microsatellite loci. + +## Usage +- Researchers and healthcare professionals can utilize this dataset for studying endometrial cancer and its various types. +- ial and should be used only for educational purposes. From 02a6781b2f4b0462cc07f50d2b8a3699e5d584d6 Mon Sep 17 00:00:00 2001 From: RUTIKA WAGALEKAR <120473716+RutikaW1155@users.noreply.github.com> Date: Tue, 14 May 2024 16:36:15 +0530 Subject: [PATCH 04/12] Add files via upload --- ...e Corpus Endometrial Carcinoma dataset.csv | 530 ++++++++++++++++++ 1 file changed, 530 insertions(+) create mode 100644 Endometriral Cancer Prediction/Uterine Corpus Endometrial Carcinoma dataset.csv diff --git a/Endometriral Cancer Prediction/Uterine Corpus Endometrial Carcinoma dataset.csv b/Endometriral Cancer Prediction/Uterine Corpus Endometrial Carcinoma dataset.csv new file mode 100644 index 00000000..6ca72575 --- /dev/null +++ b/Endometriral Cancer Prediction/Uterine Corpus Endometrial Carcinoma dataset.csv @@ -0,0 +1,530 @@ +Patient ID,Sample ID,Cancer Type Detailed,Overall Survival Status,Disease Free Status,Disease-specific Survival status,Mutation Count,Fraction Genome Altered,Diagnosis Age,MSI MANTIS Score,MSIsensor Score,Race Category,Subtype,Tumor Type +TCGA-2E-A9G8,TCGA-2E-A9G8-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,65,0.3311,59,0.3234,0.85,Black or African American,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-4E-A92E,TCGA-4E-A92E-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,147,0.0341,54,0.3396,0.01,Black or African American,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-5B-A90C,TCGA-5B-A90C-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,45,0.6903,69,0.3344,0.55,Black or African American,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-5S-A9Q8,TCGA-5S-A9Q8-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,50,0.0581,51,0.3199,0.09,Black or African American,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-A5-A0G1,TCGA-A5-A0G1-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,10808,0.0001,67,0.3108,1.74,White,UCEC_POLE,Serous Endometrial Adenocarcinoma +TCGA-A5-A0G2,TCGA-A5-A0G2-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,25696,0.3729,57,0.4003,8.63,Asian,UCEC_POLE,Serous Endometrial Adenocarcinoma +TCGA-A5-A0G3,TCGA-A5-A0G3-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,38,,61,0.272,1.18,Black or African American,,Serous Endometrial Adenocarcinoma +TCGA-A5-A0G5,TCGA-A5-A0G5-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,123,0.2308,73,0.2625,0.66,Black or African American,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-A5-A0G9,TCGA-A5-A0G9-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,439,0.0341,79,0.6507,20.59,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-A5-A0GA,TCGA-A5-A0GA-01,Uterine Endometrioid Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,301,0.0391,67,0.5237,15.97,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-A5-A0GB,TCGA-A5-A0GB-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,670,0.0346,65,1.0919,36.5,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-A5-A0GD,TCGA-A5-A0GD-01,Uterine Endometrioid Carcinoma,1:DECEASED,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,,0.0046,75,0.285,0.07,White,,Endometrioid Endometrial Adenocarcinoma +TCGA-A5-A0GE,TCGA-A5-A0GE-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,31,0.1421,38,0.2535,0.06,Asian,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-A5-A0GG,TCGA-A5-A0GG-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,,0.0998,76,0.4172,7.05,Black or African American,,Endometrioid Endometrial Adenocarcinoma +TCGA-A5-A0GH,TCGA-A5-A0GH-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR 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FREE,1453,0.0001,58,0.2524,0.07,Asian,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-A5-A0GQ,TCGA-A5-A0GQ-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,57,0,76,0.261,0,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-A5-A0GR,TCGA-A5-A0GR-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,,0.3353,69,0.2857,0.22,Black or African American,,Endometrioid Endometrial Adenocarcinoma +TCGA-A5-A0GU,TCGA-A5-A0GU-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,42,0.1123,58,0.286,0.02,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-A5-A0GV,TCGA-A5-A0GV-01,Uterine Endometrioid Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,46,0.2282,67,0.263,0.14,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-A5-A0GW,TCGA-A5-A0GW-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,307,0.0502,46,0.6829,24.38,Asian,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-A5-A0GX,TCGA-A5-A0GX-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,32,0.0299,53,0.2715,0.01,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-A5-A0R6,TCGA-A5-A0R6-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,49,0.1466,64,0.2949,0.03,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-A5-A0R7,TCGA-A5-A0R7-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,69,0.3529,55,0.2748,0.37,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-A5-A0R8,TCGA-A5-A0R8-01,Uterine Endometrioid Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,77,0,81,0.2859,0.07,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-A5-A0R9,TCGA-A5-A0R9-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR 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TUMOR FREE,30,0.3955,64,0.3011,0.18,White,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-A5-A3LP,TCGA-A5-A3LP-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,49,0.5228,74,0.3022,0.08,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-A5-A7WJ,TCGA-A5-A7WJ-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,,0.0346,64,0.8862,22.73,Black or African American,,Endometrioid Endometrial Adenocarcinoma +TCGA-A5-A7WK,TCGA-A5-A7WK-01,Uterine Mixed Endometrial Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,61,0.2537,71,0.3449,0.36,Black or African American,UCEC_CN_HIGH,Mixed Serous and Endometrioid Carcinoma +TCGA-A5-AB3J,TCGA-A5-AB3J-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,43,0.1192,52,0.328,0.01,Black or African American,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-AJ-A23M,TCGA-AJ-A23M-01,Uterine Serous 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Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,180,0.3679,67,0.3159,0.37,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-AJ-A2QN,TCGA-AJ-A2QN-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,53,0.1558,60,0.2877,0.01,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-AJ-A2QO,TCGA-AJ-A2QO-01,Uterine Endometrioid Carcinoma,1:DECEASED,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,1126,0,85,1.104,29.06,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AJ-A3BD,TCGA-AJ-A3BD-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,77,0.482,57,0.2845,0.05,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-AJ-A3BF,TCGA-AJ-A3BF-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,26,0.2474,65,0.3014,0.22,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-AJ-A3BG,TCGA-AJ-A3BG-01,Uterine Mixed Endometrial Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,1279,0.0339,65,1.1057,25.85,White,UCEC_MSI,Mixed Serous and Endometrioid Carcinoma +TCGA-AJ-A3BH,TCGA-AJ-A3BH-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,2671,0.1476,,1.0149,28.1,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AJ-A3BI,TCGA-AJ-A3BI-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,40,0.4537,67,0.2936,0.45,White,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-AJ-A3BK,TCGA-AJ-A3BK-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,58,0.1333,68,0.2952,0.03,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-AJ-A3EJ,TCGA-AJ-A3EJ-01,Uterine Mixed Endometrial Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,55,0.494,74,0.2933,0.29,White,UCEC_CN_HIGH,Mixed Serous and Endometrioid Carcinoma +TCGA-AJ-A3EK,TCGA-AJ-A3EK-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,5729,0.0002,53,0.4287,3.25,White,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-AJ-A3EL,TCGA-AJ-A3EL-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,7385,0.0008,47,0.298,1.37,Native Hawaiian or Other Pacific Islander,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-AJ-A3EM,TCGA-AJ-A3EM-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,50,0.6265,69,0.3264,0.43,White,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-AJ-A3I9,TCGA-AJ-A3I9-01,Uterine Endometrioid Carcinoma,1:DECEASED,1:Recurred/Progressed,1:DEAD WITH TUMOR,26,0.1598,52,0.2895,0.18,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-AJ-A3IA,TCGA-AJ-A3IA-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,231,0.4993,77,0.3185,0.23,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-AJ-A3NC,TCGA-AJ-A3NC-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,,0.0816,63,0.2928,0.01,White,,Endometrioid Endometrial Adenocarcinoma +TCGA-AJ-A3NE,TCGA-AJ-A3NE-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,,0.0062,46,0.555,8.48,White,,Endometrioid Endometrial Adenocarcinoma +TCGA-AJ-A3NF,TCGA-AJ-A3NF-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,55,0.1269,60,0.2883,0.06,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-AJ-A3NG,TCGA-AJ-A3NG-01,Uterine Mixed Endometrial Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,75,0.1254,83,0.3693,0.47,White,UCEC_CN_LOW,Mixed Serous and Endometrioid Carcinoma +TCGA-AJ-A3NH,TCGA-AJ-A3NH-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,,0.7584,90,0.3145,0.36,White,,Serous Endometrial Adenocarcinoma +TCGA-AJ-A3OJ,TCGA-AJ-A3OJ-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,885,0.0003,54,0.8896,21.1,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AJ-A3OK,TCGA-AJ-A3OK-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,655,0,73,0.4056,1.24,White,UCEC_CN_LOW,Serous Endometrial Adenocarcinoma +TCGA-AJ-A3OL,TCGA-AJ-A3OL-01,Uterine Endometrioid Carcinoma,1:DECEASED,1:Recurred/Progressed,1:DEAD WITH TUMOR,292,0.0844,55,0.7708,15.72,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AJ-A3QS,TCGA-AJ-A3QS-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,228,0.2386,57,0.2803,0.22,Black or African American,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-AJ-A3TW,TCGA-AJ-A3TW-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,113,0.5385,78,0.4195,0.43,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-AJ-A5DV,TCGA-AJ-A5DV-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,80,0,65,0.3053,0,Black or African American,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-AJ-A5DW,TCGA-AJ-A5DW-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,3184,0.0001,56,0.3003,0.04,Black or African American,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-AJ-A6NU,TCGA-AJ-A6NU-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,42,0.2061,64,0.3416,0,Black or African American,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-AJ-A8CT,TCGA-AJ-A8CT-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,630,0.0004,58,0.503,5.44,Black or African American,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AJ-A8CV,TCGA-AJ-A8CV-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,219,0,58,0.5694,7.86,Black or African American,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AJ-A8CW,TCGA-AJ-A8CW-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,660,0.0951,64,0.8784,24.7,Black or African American,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A051,TCGA-AP-A051-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,8311,0.0341,69,0.6799,30.46,White,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A052,TCGA-AP-A052-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,42,0.2774,59,0.2738,0.34,Black or African American,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-AP-A054,TCGA-AP-A054-01,Uterine Endometrioid Carcinoma,1:DECEASED,1:Recurred/Progressed,1:DEAD WITH TUMOR,1090,0.0545,64,1.04,31.98,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A056,TCGA-AP-A056-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,8231,0.0011,64,0.2881,1.71,White,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A059,TCGA-AP-A059-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,10942,0.0001,69,0.3351,3.41,White,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A05A,TCGA-AP-A05A-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,67,0.1828,81,0.2515,0.29,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-AP-A05D,TCGA-AP-A05D-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,32,0.2183,67,0.2783,0.34,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-AP-A05H,TCGA-AP-A05H-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,65,0.6909,75,0.3134,2.68,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-AP-A05J,TCGA-AP-A05J-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,44,0.1747,66,0.2622,0.33,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-AP-A05N,TCGA-AP-A05N-01,Uterine Endometrioid Carcinoma,1:DECEASED,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,257,0.0004,58,0.5364,12.59,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A05O,TCGA-AP-A05O-01,Uterine Endometrioid Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,,0.0197,68,0.5925,25.33,White,,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A05P,TCGA-AP-A05P-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,50,0.1359,54,0.2436,0.01,Asian,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A0L8,TCGA-AP-A0L8-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,1:Recurred/Progressed,1:DEAD WITH TUMOR,60,0.737,70,0.3118,1.78,Black or African American,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-AP-A0L9,TCGA-AP-A0L9-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,64,0.5243,71,0.3048,1.98,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-AP-A0LD,TCGA-AP-A0LD-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,446,0.13,63,1.0367,34.2,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A0LE,TCGA-AP-A0LE-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,545,0.0341,57,0.9987,33.29,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A0LF,TCGA-AP-A0LF-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,240,0.3339,82,0.2706,0.66,White,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A0LG,TCGA-AP-A0LG-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,411,0,54,0.7979,24.16,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A0LH,TCGA-AP-A0LH-01,Uterine Mixed Endometrial Carcinoma,1:DECEASED,1:Recurred/Progressed,1:DEAD WITH TUMOR,30,0.1132,60,0.246,0.25,White,UCEC_CN_HIGH,Mixed Serous and Endometrioid Carcinoma +TCGA-AP-A0LI,TCGA-AP-A0LI-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,104,0.3668,67,0.27,0.27,Black or African American,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-AP-A0LJ,TCGA-AP-A0LJ-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,38,0.0469,42,0.2669,0.02,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A0LL,TCGA-AP-A0LL-01,Uterine Endometrioid Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,45,0.0344,56,0.2821,0.27,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A0LM,TCGA-AP-A0LM-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,13201,0.0001,33,0.3086,3.61,Black or African American,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A0LN,TCGA-AP-A0LN-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,61,0.0465,56,0.2415,0.11,Black or African American,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A0LO,TCGA-AP-A0LO-01,Uterine Endometrioid Carcinoma,1:DECEASED,1:Recurred/Progressed,1:DEAD WITH TUMOR,28,0.2933,45,0.272,0.08,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A0LP,TCGA-AP-A0LP-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,294,0,76,0.5949,17.64,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A0LQ,TCGA-AP-A0LQ-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,53,0.0464,59,0.2808,0.12,White,,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A0LS,TCGA-AP-A0LS-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,420,0.0001,63,0.8371,14.91,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A0LT,TCGA-AP-A0LT-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,635,0,57,1.1312,37.41,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A0LV,TCGA-AP-A0LV-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,20,0.0001,39,0.2684,0.06,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A1DH,TCGA-AP-A1DH-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,580,0,62,0.7077,13.83,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A1DK,TCGA-AP-A1DK-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,10302,0.0003,53,0.5415,4.49,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A1DM,TCGA-AP-A1DM-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,353,0.0415,60,0.6163,13.24,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A1DO,TCGA-AP-A1DO-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,546,0.2144,62,0.8996,26.21,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A1DP,TCGA-AP-A1DP-01,Uterine Endometrioid Carcinoma,1:DECEASED,1:Recurred/Progressed,1:DEAD WITH TUMOR,448,0.0341,70,0.8032,20.7,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A1DQ,TCGA-AP-A1DQ-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,1:Recurred/Progressed,1:DEAD WITH TUMOR,29,0.501,76,0.3125,0.31,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-AP-A1DR,TCGA-AP-A1DR-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,738,0.0002,59,0.9916,27.51,Black or African American,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A1DV,TCGA-AP-A1DV-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,12038,0.0195,59,0.3398,0.7,White,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A1E0,TCGA-AP-A1E0-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,4826,0.0004,40,0.2856,0.05,White,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A1E1,TCGA-AP-A1E1-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,551,0.0338,74,0.7298,12.89,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A1E3,TCGA-AP-A1E3-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,41,0.0001,45,0.2838,0,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A1E4,TCGA-AP-A1E4-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,20,0.127,54,0.301,0.02,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-AP-A3K1,TCGA-AP-A3K1-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,,0.4611,56,0.3224,1.07,White,,Serous Endometrial Adenocarcinoma +TCGA-AP-A5FX,TCGA-AP-A5FX-01,Uterine Mixed Endometrial Carcinoma,1:DECEASED,1:Recurred/Progressed,1:DEAD WITH TUMOR,38,0.4774,68,0.2855,0.3,Black or African American,UCEC_CN_HIGH,Mixed Serous and Endometrioid Carcinoma +TCGA-AW-A1PO,TCGA-AW-A1PO-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,66,0.9487,66,0.3268,0.36,White,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A05S,TCGA-AX-A05S-01,Uterine Endometrioid Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,494,0.0904,81,0.846,31.68,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A05T,TCGA-AX-A05T-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,45,0.0464,82,0.2436,0.4,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A05U,TCGA-AX-A05U-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,27,0.0586,56,0.2546,0.07,American Indian or Alaska Native,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A05W,TCGA-AX-A05W-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,66,0.2216,60,0.2715,0.04,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A05Y,TCGA-AX-A05Y-01,Uterine Endometrioid Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,184,0.0411,57,0.5212,7.31,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A05Z,TCGA-AX-A05Z-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,7370,0.019,37,0.2617,0.35,,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A060,TCGA-AX-A060-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,741,0.0342,77,,35.52,American Indian or Alaska Native,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A062,TCGA-AX-A062-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,25,0.0001,53,0.2416,0.15,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A063,TCGA-AX-A063-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,1000,0.044,63,0.922,34.04,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A064,TCGA-AX-A064-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,262,0.0341,81,0.5467,15.94,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A06B,TCGA-AX-A06B-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,41,0.5106,72,0.2743,0.26,White,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A06F,TCGA-AX-A06F-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,7276,0.0013,59,0.3375,3.4,White,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A06H,TCGA-AX-A06H-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,408,0.0004,60,0.6159,23.79,American Indian or Alaska Native,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A06J,TCGA-AX-A06J-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,57,0.0464,71,0.2922,0.64,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A06L,TCGA-AX-A06L-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,40,0.0002,63,0.2699,0.06,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A0IS,TCGA-AX-A0IS-01,Uterine Endometrioid Carcinoma,1:DECEASED,1:Recurred/Progressed,1:DEAD WITH TUMOR,49,0.1065,52,0.2846,0.2,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A0IU,TCGA-AX-A0IU-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,55,0.2139,79,0.289,0.25,Black or African American,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-AX-A0IW,TCGA-AX-A0IW-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,51,0.6545,67,0.2889,0.95,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-AX-A0IZ,TCGA-AX-A0IZ-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,,0.0004,53,0.5541,9.18,White,,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A0J0,TCGA-AX-A0J0-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,7793,0.0006,47,0.3,1.72,White,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A0J1,TCGA-AX-A0J1-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,5113,0.0341,80,0.7195,29.85,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A1C4,TCGA-AX-A1C4-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,856,0.0853,52,1.0651,25,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A1C5,TCGA-AX-A1C5-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,845,0.004,47,0.6802,13.16,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A1C7,TCGA-AX-A1C7-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,44,0.3689,77,0.3124,0.77,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-AX-A1C8,TCGA-AX-A1C8-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,70,0.2344,77,0.2994,0.18,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-AX-A1C9,TCGA-AX-A1C9-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,963,0.0989,73,0.5573,8.72,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A1CA,TCGA-AX-A1CA-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,108,0.5031,70,0.3043,0.97,Black or African American,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-AX-A1CC,TCGA-AX-A1CC-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,19,0.0123,65,0.3195,0.28,White,UCEC_CN_LOW,Serous Endometrial Adenocarcinoma +TCGA-AX-A1CE,TCGA-AX-A1CE-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,11440,0.0013,60,0.6541,17.4,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A1CF,TCGA-AX-A1CF-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,92,0.5489,69,0.2825,0.92,White,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A1CI,TCGA-AX-A1CI-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,60,0.0488,61,0.333,1.46,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A1CJ,TCGA-AX-A1CJ-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,49,0.0811,59,0.2804,0,Black or African American,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A1CK,TCGA-AX-A1CK-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,59,0.1396,58,0.2799,0.54,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A1CN,TCGA-AX-A1CN-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,35,0.069,54,0.3094,0.03,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A2H2,TCGA-AX-A2H2-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,185,0.6405,86,0.292,0.26,Black or African American,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-AX-A2H4,TCGA-AX-A2H4-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,1:Recurred/Progressed,1:DEAD WITH TUMOR,165,0.5642,66,0.3093,0.35,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-AX-A2H5,TCGA-AX-A2H5-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,47,0.289,67,0.3194,1.63,Black or African American,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-AX-A2H7,TCGA-AX-A2H7-01,Uterine Endometrioid Carcinoma,1:DECEASED,1:Recurred/Progressed,1:DEAD WITH TUMOR,84,0.2716,87,0.2908,0.02,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A2HA,TCGA-AX-A2HA-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,1509,0.0934,35,1.3049,35.7,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A2HC,TCGA-AX-A2HC-01,Uterine Endometrioid Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,12979,0.0764,53,0.5408,9.49,Black or African American,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A2HD,TCGA-AX-A2HD-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,7836,0.03,69,0.9828,25.8,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A2HG,TCGA-AX-A2HG-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,861,0.0004,56,0.9147,20.23,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A2HH,TCGA-AX-A2HH-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,39,0.2247,60,0.2913,0.11,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A2HJ,TCGA-AX-A2HJ-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,1573,0.0524,35,1.1152,26.9,Asian,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A2HK,TCGA-AX-A2HK-01,Uterine Endometrioid Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,63,0.5945,77,0.3002,0.45,White,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A2IN,TCGA-AX-A2IN-01,Uterine Endometrioid Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,513,0.0341,63,0.7682,11.9,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A2IO,TCGA-AX-A2IO-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,343,0.3254,83,0.2914,0.05,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-AX-A3FS,TCGA-AX-A3FS-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,757,0.0371,82,1.0475,28.76,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A3FT,TCGA-AX-A3FT-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,1280,0.0499,64,0.8254,20.39,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A3FV,TCGA-AX-A3FV-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,81,0.7134,62,0.3357,2.15,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A3FW,TCGA-AX-A3FW-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,42,0.1587,66,0.3134,0.33,White,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A3FX,TCGA-AX-A3FX-01,Uterine Endometrioid Carcinoma,1:DECEASED,,,58,0.2339,69,0.2922,0.11,White,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A3FZ,TCGA-AX-A3FZ-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,40,0.3266,33,0.336,1.08,White,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A3G1,TCGA-AX-A3G1-01,Uterine Endometrioid Carcinoma,1:DECEASED,,,57,0.3476,84,0.3099,0.31,White,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A3G4,TCGA-AX-A3G4-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,35,0.4534,60,0.3004,0.39,Native Hawaiian or Other Pacific Islander,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-AX-A3G6,TCGA-AX-A3G6-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,55,0.5603,67,0.3125,0.35,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-AX-A3G7,TCGA-AX-A3G7-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,28,0.1583,71,0.3123,0.02,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-AX-A3G8,TCGA-AX-A3G8-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,781,0.0345,76,0.9574,28.31,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A3G9,TCGA-AX-A3G9-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,658,0.0001,63,1.016,25.12,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A3GB,TCGA-AX-A3GB-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,79,0.0345,73,0.3008,0,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-AX-A3GI,TCGA-AX-A3GI-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,38,0.7361,66,0.2908,1.63,Black or African American,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-B5-A0JN,TCGA-B5-A0JN-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,139,0.5279,84,0.3115,0.59,Black or African American,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-B5-A0JR,TCGA-B5-A0JR-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,569,0.1187,73,0.8081,26.39,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A0JS,TCGA-B5-A0JS-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,28,0.0195,54,0.2933,0.03,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A0JT,TCGA-B5-A0JT-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,66,0.3758,63,0.2672,0.14,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A0JU,TCGA-B5-A0JU-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,407,0,57,0.8228,20.47,Black or African American,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A0JV,TCGA-B5-A0JV-01,Uterine Endometrioid Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,346,0.0359,63,0.6077,22.81,Black or African American,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A0JX,TCGA-B5-A0JX-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,65,0.1203,62,0.2686,0.26,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A0JY,TCGA-B5-A0JY-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,9685,0.0003,50,0.3015,0.87,White,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A0JZ,TCGA-B5-A0JZ-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,499,0.0001,60,0.7918,28.87,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A0K0,TCGA-B5-A0K0-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,20,0.0888,48,0.2477,0.02,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A0K1,TCGA-B5-A0K1-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,42,0,69,0.2459,0.06,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A0K2,TCGA-B5-A0K2-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,301,0.0004,54,0.522,10.65,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A0K3,TCGA-B5-A0K3-01,Uterine Endometrioid Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,53,0.2142,62,0.2639,0.55,Black or African American,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A0K4,TCGA-B5-A0K4-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,49,0.3111,51,0.2551,0.22,,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A0K6,TCGA-B5-A0K6-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,352,0.0488,58,0.5327,16.83,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A0K7,TCGA-B5-A0K7-01,Uterine Endometrioid Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,44,0.1183,64,0.2724,0.14,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A0K8,TCGA-B5-A0K8-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,41,0.3133,71,0.3173,0.48,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-B5-A0K9,TCGA-B5-A0K9-01,Uterine Endometrioid Carcinoma,1:DECEASED,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,759,0.1183,88,0.7746,22.1,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A11E,TCGA-B5-A11E-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,9038,0.079,53,0.3566,5.48,White,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A11F,TCGA-B5-A11F-01,Uterine Endometrioid Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,39,0.0629,55,0.2879,0.52,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A11G,TCGA-B5-A11G-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,553,0.036,71,0.862,28.95,Black or African American,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A11H,TCGA-B5-A11H-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,1016,0.067,67,0.9954,40.43,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A11I,TCGA-B5-A11I-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,49,0.2983,65,0.2907,1.24,Black or African American,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A11J,TCGA-B5-A11J-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,436,0,64,0.7092,23.51,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A11L,TCGA-B5-A11L-01,Uterine Endometrioid Carcinoma,1:DECEASED,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,111,0.0678,71,0.3009,0.08,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A11M,TCGA-B5-A11M-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,36,,43,0.2609,0.02,White,,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A11N,TCGA-B5-A11N-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,1909,0.0279,69,0.259,0.03,White,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A11O,TCGA-B5-A11O-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,96,0.0001,62,0.2876,0.56,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A11P,TCGA-B5-A11P-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,43,0.6382,87,0.3046,0.29,White,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A11Q,TCGA-B5-A11Q-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,42,0.112,64,0.2746,0.49,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A11R,TCGA-B5-A11R-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,594,0.3046,51,0.2964,0.41,Black or African American,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A11S,TCGA-B5-A11S-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,52,0.1509,63,0.2571,0.22,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A11U,TCGA-B5-A11U-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,369,0.0001,74,0.7585,27.25,Black or African American,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A11V,TCGA-B5-A11V-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,56,0.1219,64,0.2837,0.25,Black or African American,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A11W,TCGA-B5-A11W-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,73,0.0009,61,0.2663,0.22,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A11X,TCGA-B5-A11X-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,60,,66,0.2725,0,Black or African American,,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A11Y,TCGA-B5-A11Y-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,951,0,59,0.4514,3.92,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A11Z,TCGA-B5-A11Z-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,52,0.0343,61,0.2746,0.19,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A121,TCGA-B5-A121-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,31,0.0001,57,0.2653,0.13,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A1MR,TCGA-B5-A1MR-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,7302,0.0499,65,0.3618,0.12,White,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A1MS,TCGA-B5-A1MS-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,45,0.3454,60,0.3021,0.38,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-B5-A1MU,TCGA-B5-A1MU-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,32,0.135,79,0.3022,0.03,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-B5-A1MV,TCGA-B5-A1MV-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,60,0.1008,84,0.3134,0,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A1MW,TCGA-B5-A1MW-01,Uterine Endometrioid Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,537,0.0499,55,0.8113,13.94,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A1MX,TCGA-B5-A1MX-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,5686,0.0008,47,0.8573,16.15,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A1MY,TCGA-B5-A1MY-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,47,0.5939,62,0.3174,0.61,Black or African American,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-B5-A1MZ,TCGA-B5-A1MZ-01,Uterine Endometrioid Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,52,0.0341,54,0.2827,0,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A1N2,TCGA-B5-A1N2-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,1:Recurred/Progressed,1:DEAD WITH TUMOR,140,0.5747,70,0.3409,2.02,Black or African American,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-B5-A3F9,TCGA-B5-A3F9-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,57,0.0636,56,0.2781,0,Black or African American,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A3FA,TCGA-B5-A3FA-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,9645,0.0003,73,0.4634,7.28,White,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A3FB,TCGA-B5-A3FB-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,43,0.0111,73,0.2756,0.02,Black or African American,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A3FC,TCGA-B5-A3FC-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,12205,0.0043,53,0.6869,24.24,Black or African American,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A3FD,TCGA-B5-A3FD-01,Uterine Endometrioid Carcinoma,1:DECEASED,,0:ALIVE OR DEAD TUMOR FREE,41,0.7669,70,0.2815,0.35,Black or African American,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A3FH,TCGA-B5-A3FH-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,38,0.0477,74,0.2786,0.04,Black or African American,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A3S1,TCGA-B5-A3S1-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,91,0.4083,71,0.3135,0.24,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-B5-A5OC,TCGA-B5-A5OC-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,977,0.0796,71,0.6411,13.96,Black or African American,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-B5-A5OD,TCGA-B5-A5OD-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,50,0.6366,67,0.3578,0.15,Black or African American,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-B5-A5OE,TCGA-B5-A5OE-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,69,0.4046,68,0.3429,0.33,Black or African American,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-BG-A0LW,TCGA-BG-A0LW-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,30,0.0001,47,0.2465,0.22,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A0LX,TCGA-BG-A0LX-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,448,0.0811,57,0.8188,26.46,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A0M0,TCGA-BG-A0M0-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,138,0.1187,66,0.4648,9.83,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A0M2,TCGA-BG-A0M2-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,27,0.3315,62,0.2688,0.83,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A0M3,TCGA-BG-A0M3-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,58,0.1516,74,0.277,0.18,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A0M4,TCGA-BG-A0M4-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,582,0.1939,60,0.8536,28.95,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A0M6,TCGA-BG-A0M6-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,45,0.6552,73,0.3034,0.63,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-BG-A0M7,TCGA-BG-A0M7-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,63,0.0001,60,0.2483,0.21,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A0M9,TCGA-BG-A0M9-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,57,0.0498,73,0.2818,0.16,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A0MA,TCGA-BG-A0MA-01,Uterine Endometrioid Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,44,0.0499,60,0.2841,0.03,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A0MC,TCGA-BG-A0MC-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,50,0.0296,74,0.2669,0.05,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A0MG,TCGA-BG-A0MG-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,101,0.0938,73,0.2676,0.4,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A0MI,TCGA-BG-A0MI-01,Uterine Endometrioid Carcinoma,1:DECEASED,,0:ALIVE OR DEAD TUMOR FREE,52,0,83,0.2475,0.21,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A0MK,TCGA-BG-A0MK-01,Uterine Endometrioid Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,80,0.0002,52,0.3119,0,Black or African American,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A0MO,TCGA-BG-A0MO-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,39,0.0433,63,0.237,0.04,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A0MQ,TCGA-BG-A0MQ-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,603,0.0493,71,0.8335,23.55,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A0MT,TCGA-BG-A0MT-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,33,0.1556,64,0.2661,0.32,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A0MU,TCGA-BG-A0MU-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,53,0.0495,78,0.2799,0.31,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A0RY,TCGA-BG-A0RY-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,45,0.3342,68,0.2835,0.18,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A0VT,TCGA-BG-A0VT-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,32,0.0492,56,0.2866,0.12,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A0VV,TCGA-BG-A0VV-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,42,0.0342,53,0.2528,0.05,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A0VW,TCGA-BG-A0VW-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,435,0.0117,77,0.5605,18.29,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A0VX,TCGA-BG-A0VX-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,177,0,58,0.2605,0.08,White,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A0VZ,TCGA-BG-A0VZ-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,332,0.0026,58,0.757,22.9,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A0W1,TCGA-BG-A0W1-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,66,0.1698,89,0.3122,0.35,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A0W2,TCGA-BG-A0W2-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,30,0.0465,57,0.2844,0.06,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A0YU,TCGA-BG-A0YU-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,18,0.0495,37,0.2784,0.05,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A0YV,TCGA-BG-A0YV-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,72,0.2432,67,0.2924,0.25,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-BG-A186,TCGA-BG-A186-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,30,0.0496,61,0.2865,0.08,Black or African American,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A187,TCGA-BG-A187-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,80,0.0465,65,0.2921,0.09,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A18A,TCGA-BG-A18A-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,54,0.0464,74,0.2765,0.02,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A18B,TCGA-BG-A18B-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,371,0.0005,53,0.8185,23.12,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A18C,TCGA-BG-A18C-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,49,0.8304,72,0.2796,2.12,White,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A222,TCGA-BG-A222-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,4260,0.0443,49,1.1195,25.38,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A2AD,TCGA-BG-A2AD-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,,0.0016,63,,,White,,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A2AE,TCGA-BG-A2AE-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,55,0.238,57,0.3042,0.01,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A2L7,TCGA-BG-A2L7-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,618,0.0004,68,0.8115,17.46,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A3EW,TCGA-BG-A3EW-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,446,0.0349,62,0.7928,16.14,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-BG-A3PP,TCGA-BG-A3PP-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,46,0.418,82,0.2949,0.04,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-BK-A0C9,TCGA-BK-A0C9-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,587,0.5839,57,0.5893,19.92,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-BK-A0CA,TCGA-BK-A0CA-01,Uterine Mixed Endometrial Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,48,,61,0.3061,0.01,White,UCEC_CN_LOW,Mixed Serous and Endometrioid Carcinoma +TCGA-BK-A0CB,TCGA-BK-A0CB-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,53,0.05,60,0.2778,0.09,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BK-A0CC,TCGA-BK-A0CC-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,52,,69,0.3195,5.36,Black or African American,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-BK-A139,TCGA-BK-A139-01,Uterine Endometrioid Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,125,,74,0.2959,0.73,White,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-BK-A13B,TCGA-BK-A13B-01,Uterine Endometrioid Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,633,0.0344,58,0.8641,22.97,Black or African American,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-BK-A13C,TCGA-BK-A13C-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,44,0.0343,47,0.2743,0.36,Black or African American,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BK-A26L,TCGA-BK-A26L-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,74,,71,0.3083,0.39,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-BK-A4ZD,TCGA-BK-A4ZD-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,70,0.8106,42,0.3226,0.32,Black or African American,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-BK-A56F,TCGA-BK-A56F-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,54,0.0024,75,0.297,0,Black or African American,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BK-A6W3,TCGA-BK-A6W3-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,3922,0.0003,34,0.3435,0.07,Black or African American,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-BK-A6W4,TCGA-BK-A6W4-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,24,0,62,0.3301,0,Black or African American,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BS-A0T9,TCGA-BS-A0T9-01,Uterine Endometrioid Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,58,0.0504,39,0.2737,0.01,Native Hawaiian or Other Pacific Islander,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BS-A0TA,TCGA-BS-A0TA-01,Uterine Endometrioid Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,472,0.0005,58,0.5657,19.35,Native Hawaiian or Other Pacific Islander,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-BS-A0TC,TCGA-BS-A0TC-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,1315,0.0005,69,0.2784,0.22,Asian,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-BS-A0TD,TCGA-BS-A0TD-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,35,0.0001,65,0.2691,0.03,Asian,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BS-A0TE,TCGA-BS-A0TE-01,Uterine Endometrioid Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,299,0.6335,35,0.4882,4.84,Asian,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-BS-A0TG,TCGA-BS-A0TG-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,39,0.0498,60,0.2899,0.17,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BS-A0TI,TCGA-BS-A0TI-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,84,0.3669,64,0.3761,0.9,White,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-BS-A0TJ,TCGA-BS-A0TJ-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,701,0.0002,59,0.9925,32.67,Asian,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-BS-A0U5,TCGA-BS-A0U5-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,58,0.0001,76,0.2767,0.17,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BS-A0U7,TCGA-BS-A0U7-01,Uterine Endometrioid Carcinoma,1:DECEASED,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,185,0.0908,63,0.3873,3.2,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BS-A0U8,TCGA-BS-A0U8-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,425,0.0034,55,0.8033,24.27,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-BS-A0U9,TCGA-BS-A0U9-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,36,0.6511,59,,0.49,Native Hawaiian or Other Pacific Islander,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-BS-A0UA,TCGA-BS-A0UA-01,Uterine Endometrioid Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,321,0,68,0.5364,16.89,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-BS-A0UF,TCGA-BS-A0UF-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,8372,0.0001,65,0.3009,0.93,Asian,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-BS-A0UJ,TCGA-BS-A0UJ-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,1583,0.0001,68,0.3517,5.03,Asian,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-BS-A0UL,TCGA-BS-A0UL-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,388,0.0001,54,0.4699,12.99,Native Hawaiian or Other Pacific Islander,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-BS-A0UM,TCGA-BS-A0UM-01,Uterine Endometrioid Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,473,,64,0.5523,28.86,White,,Endometrioid Endometrial Adenocarcinoma +TCGA-BS-A0UT,TCGA-BS-A0UT-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,39,0.1947,62,0.2888,0.19,Asian,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BS-A0UV,TCGA-BS-A0UV-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,8969,0.1249,55,0.2839,0.85,Native Hawaiian or Other Pacific Islander,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-BS-A0V4,TCGA-BS-A0V4-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,325,0.0307,56,0.6106,6.86,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-BS-A0V6,TCGA-BS-A0V6-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,51,0.0002,55,0.2612,0.01,Asian,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BS-A0V7,TCGA-BS-A0V7-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,59,0.0001,48,0.2599,0,Native Hawaiian or Other Pacific Islander,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-BS-A0V8,TCGA-BS-A0V8-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,76,0.5415,68,0.2631,0.36,Native Hawaiian or Other Pacific Islander,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-BS-A0VI,TCGA-BS-A0VI-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,308,0.044,58,0.5701,7.04,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-BS-A0WQ,TCGA-BS-A0WQ-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,42,0.0471,55,0.2671,0.06,Asian,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A0ZN,TCGA-D1-A0ZN-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,42,0.0767,60,0.2645,0.14,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A0ZO,TCGA-D1-A0ZO-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,630,0.0834,75,0.7496,28.32,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A0ZP,TCGA-D1-A0ZP-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,30,0.1861,59,0.2709,0.36,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-D1-A0ZQ,TCGA-D1-A0ZQ-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,75,0.1772,90,0.2599,0.33,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A0ZR,TCGA-D1-A0ZR-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,50,0.0341,57,0.2979,0.16,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A0ZS,TCGA-D1-A0ZS-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,389,0,54,0.7448,28.29,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A0ZU,TCGA-D1-A0ZU-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,32,0.0964,34,0.2884,0.28,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A0ZV,TCGA-D1-A0ZV-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,38,0,58,0.2787,0.04,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A0ZZ,TCGA-D1-A0ZZ-01,Uterine Mixed Endometrial Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,63,0.5039,80,0.3019,1.48,White,UCEC_CN_HIGH,Mixed Serous and Endometrioid Carcinoma +TCGA-D1-A101,TCGA-D1-A101-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,435,0.0002,60,0.5438,16.28,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A102,TCGA-D1-A102-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,43,0.1758,49,0.2975,0.4,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A103,TCGA-D1-A103-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,6850,0.0001,87,0.4066,3.98,White,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A15V,TCGA-D1-A15V-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,42,0.3738,68,0.2688,0.79,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-D1-A15W,TCGA-D1-A15W-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,66,0.0024,58,0.2687,0.08,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A15X,TCGA-D1-A15X-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,1507,0.0006,45,0.4476,11.04,White,UCEC_CN_LOW,Serous Endometrial Adenocarcinoma +TCGA-D1-A15Z,TCGA-D1-A15Z-01,Uterine Endometrioid Carcinoma,1:DECEASED,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,65,0.0465,72,0.2623,0.09,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A160,TCGA-D1-A160-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,499,0.0341,70,0.6785,21.45,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A161,TCGA-D1-A161-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,79,0.0468,78,0.2671,0.16,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A163,TCGA-D1-A163-01,Uterine Endometrioid Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,570,0.0001,50,0.9412,29.96,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A165,TCGA-D1-A165-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,63,0.1588,64,0.2651,0,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A167,TCGA-D1-A167-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,1559,0.0001,70,0.9382,24.6,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A168,TCGA-D1-A168-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,49,0.232,67,0.2875,0.24,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A169,TCGA-D1-A169-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,54,0.2083,63,0.2669,0.15,White,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A16B,TCGA-D1-A16B-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,49,0.0001,64,0.2725,0.02,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A16D,TCGA-D1-A16D-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,42,0.0003,49,0.2708,0.05,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A16E,TCGA-D1-A16E-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,53,0.0001,73,0.2678,0.06,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A16F,TCGA-D1-A16F-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,493,,60,0.6303,21.79,White,,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A16G,TCGA-D1-A16G-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,41,0.1014,74,0.2742,0,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-D1-A16I,TCGA-D1-A16I-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,37,0.3811,62,0.2793,0.77,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-D1-A16J,TCGA-D1-A16J-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,224,0.106,61,0.2648,0.09,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A16N,TCGA-D1-A16N-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,129,0.5101,51,0.3686,3.98,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A16O,TCGA-D1-A16O-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,31,0.0635,44,0.2821,0.07,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A16Q,TCGA-D1-A16Q-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,29,0.0061,54,0.2458,0.02,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A16R,TCGA-D1-A16R-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,45,0.0525,39,0.2517,0.04,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A16S,TCGA-D1-A16S-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,53,0.427,70,0.2503,0.86,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-D1-A16V,TCGA-D1-A16V-01,Uterine Endometrioid Carcinoma,1:DECEASED,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,40,0.3077,78,0.2571,0.9,White,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A16X,TCGA-D1-A16X-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,2727,0.0001,54,0.2756,0.01,White,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A16Y,TCGA-D1-A16Y-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,1068,0,56,0.279,0.41,White,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A174,TCGA-D1-A174-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,802,0.0006,51,0.9107,35.73,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A175,TCGA-D1-A175-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,,0.0001,48,1.0047,36.73,White,,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A176,TCGA-D1-A176-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,434,0.0392,67,0.6796,19.2,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A177,TCGA-D1-A177-01,Uterine Endometrioid Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,804,0.0342,70,1.0196,31.14,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A179,TCGA-D1-A179-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,1:Recurred/Progressed,1:DEAD WITH TUMOR,,0.854,76,0.2998,1.7,White,,Serous Endometrial Adenocarcinoma +TCGA-D1-A17A,TCGA-D1-A17A-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,242,0.035,59,0.5402,12.22,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A17B,TCGA-D1-A17B-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,286,0.0341,69,0.436,8.2,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A17C,TCGA-D1-A17C-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,51,0.0001,78,0.2689,0.01,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A17D,TCGA-D1-A17D-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,479,0,58,0.6777,18.85,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A17F,TCGA-D1-A17F-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,218,0,68,0.3968,4.57,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A17H,TCGA-D1-A17H-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,665,0.0052,61,0.8869,30.4,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A17K,TCGA-D1-A17K-01,Uterine Endometrioid Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,58,0.2631,74,0.2804,0.53,White,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A17L,TCGA-D1-A17L-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,64,0.0341,81,0.2703,0.21,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A17M,TCGA-D1-A17M-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,436,0.0454,56,0.53,16.57,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A17N,TCGA-D1-A17N-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,28,0,46,0.2676,0.01,,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A17Q,TCGA-D1-A17Q-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,5948,0,54,0.2907,0.78,White,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A17R,TCGA-D1-A17R-01,Uterine Endometrioid Carcinoma,1:DECEASED,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,253,0.2392,58,0.4451,9.94,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A17S,TCGA-D1-A17S-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,47,0,59,0.265,0.14,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A17T,TCGA-D1-A17T-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,66,0.0464,67,0.2691,0.1,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A17U,TCGA-D1-A17U-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,422,0.0009,53,0.792,27.42,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A1NS,TCGA-D1-A1NS-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,290,0.0002,53,0.5021,4.32,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A1NU,TCGA-D1-A1NU-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,79,0.4092,74,0.3328,0.56,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-D1-A1NW,TCGA-D1-A1NW-01,Uterine Mixed Endometrial Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,55,0.2859,72,0.3416,0.68,White,UCEC_CN_HIGH,Mixed Serous and Endometrioid Carcinoma +TCGA-D1-A1NX,TCGA-D1-A1NX-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,41,0.1787,66,0.3074,0.27,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-D1-A1NY,TCGA-D1-A1NY-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,576,0.0562,67,0.9023,20.73,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A1NZ,TCGA-D1-A1NZ-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,716,0.0341,60,0.898,18.91,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A1O0,TCGA-D1-A1O0-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,48,0.4475,77,0.3044,0.6,White,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A1O5,TCGA-D1-A1O5-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,64,0.0002,61,0.2812,0.03,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A1O7,TCGA-D1-A1O7-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,328,0,60,0.6161,5.26,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A1O8,TCGA-D1-A1O8-01,Uterine Endometrioid Carcinoma,1:DECEASED,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,59,0,70,0.2803,0,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A2G0,TCGA-D1-A2G0-01,Uterine Mixed Endometrial Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,815,0.0916,70,0.938,23.71,White,UCEC_MSI,Mixed Serous and Endometrioid Carcinoma +TCGA-D1-A2G5,TCGA-D1-A2G5-01,Uterine Endometrioid Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,213,0.9268,50,0.4585,1.53,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A2G6,TCGA-D1-A2G6-01,Uterine Endometrioid Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,69,0.2916,53,0.3306,0.02,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A2G7,TCGA-D1-A2G7-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,42,0.2241,65,0.2867,0,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-D1-A3DA,TCGA-D1-A3DA-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,76,0.0399,77,0.3098,0.07,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A3DG,TCGA-D1-A3DG-01,Uterine Endometrioid Carcinoma,1:DECEASED,,0:ALIVE OR DEAD TUMOR FREE,94,0.929,81,0.2964,0.66,White,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A3DH,TCGA-D1-A3DH-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,89,0.7039,71,0.3135,1.8,White,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-D1-A3JQ,TCGA-D1-A3JQ-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,30,0.2228,61,0.3422,0.09,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-DF-A2KN,TCGA-DF-A2KN-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,4337,0.0008,,0.8466,19.89,,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-DF-A2KR,TCGA-DF-A2KR-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,93,0.7258,84,0.3419,0.58,,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-DF-A2KS,TCGA-DF-A2KS-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,117,0.6253,69,0.3045,0.71,,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-DF-A2KU,TCGA-DF-A2KU-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,10042,0.0048,,0.3546,0.73,,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-DF-A2KV,TCGA-DF-A2KV-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,1323,0.0003,55,0.288,0,,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-DF-A2KY,TCGA-DF-A2KY-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,,0.012,49,0.8855,26.17,,,Endometrioid Endometrial Adenocarcinoma +TCGA-DF-A2KZ,TCGA-DF-A2KZ-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,601,0.0341,87,0.715,16.97,,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-DF-A2L0,TCGA-DF-A2L0-01,Uterine Endometrioid Carcinoma,1:DECEASED,1:Recurred/Progressed,1:DEAD WITH TUMOR,44,0.5377,60,0.3109,0.67,,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-DI-A0WH,TCGA-DI-A0WH-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,588,0.0346,64,0.7904,25.06,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-DI-A1BU,TCGA-DI-A1BU-01,Uterine Mixed Endometrial Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,7632,0.0001,55,0.991,38.69,White,UCEC_MSI,Mixed Serous and Endometrioid Carcinoma +TCGA-DI-A1BY,TCGA-DI-A1BY-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,56,0.0005,63,0.2991,0,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-DI-A1C3,TCGA-DI-A1C3-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,41,0,38,0.2916,0,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-DI-A1NN,TCGA-DI-A1NN-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,47,0.1974,63,0.3003,0.17,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-DI-A1NO,TCGA-DI-A1NO-01,Uterine Endometrioid Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,229,0.5988,68,0.3418,2.08,Black or African American,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-DI-A2QT,TCGA-DI-A2QT-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,22,0.3625,51,0.3099,0.43,American Indian or Alaska Native,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-DI-A2QU,TCGA-DI-A2QU-01,Uterine Endometrioid Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,63,0.3324,67,0.3052,0.3,White,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-DI-A2QY,TCGA-DI-A2QY-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,109,0.6407,64,0.301,0.33,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-E6-A1LX,TCGA-E6-A1LX-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,12694,0.0112,40,0.3376,0.41,White,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-E6-A1LZ,TCGA-E6-A1LZ-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,239,0.5552,76,0.3134,0.43,Black or African American,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-E6-A1M0,TCGA-E6-A1M0-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,1740,0.2016,56,0.2895,0.05,White,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-E6-A2P8,TCGA-E6-A2P8-01,Uterine Mixed Endometrial Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,558,0.0002,53,0.6942,17.31,Black or African American,UCEC_MSI,Mixed Serous and Endometrioid Carcinoma +TCGA-E6-A2P9,TCGA-E6-A2P9-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,862,0.081,65,1.0301,20.99,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-E6-A8L9,TCGA-E6-A8L9-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,51,0.6129,61,0.361,0.61,Black or African American,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-EC-A1NJ,TCGA-EC-A1NJ-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,257,0.053,73,0.4662,3.24,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-EC-A1QX,TCGA-EC-A1QX-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,645,0.0796,71,0.6203,7.97,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-EC-A24G,TCGA-EC-A24G-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,1019,0.0005,57,0.5199,6.4,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-EO-A1Y5,TCGA-EO-A1Y5-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,71,0.4065,63,0.3174,0.44,,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-EO-A1Y7,TCGA-EO-A1Y7-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,42,0.2798,65,0.3284,0.36,,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-EO-A1Y8,TCGA-EO-A1Y8-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,49,0.3002,87,0.302,0.39,,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-EO-A22R,TCGA-EO-A22R-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,12770,0.0002,56,0.3971,2.11,,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-EO-A22S,TCGA-EO-A22S-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,632,0.0001,58,0.9162,21.04,,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-EO-A22T,TCGA-EO-A22T-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,993,0.0499,56,0.7835,17.64,Asian,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-EO-A22U,TCGA-EO-A22U-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,13820,0.0004,83,0.4032,3.26,White,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-EO-A22X,TCGA-EO-A22X-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,9421,0.0458,36,0.3086,0.29,,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-EO-A22Y,TCGA-EO-A22Y-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,49,0.6535,68,0.306,0.08,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-EO-A2CG,TCGA-EO-A2CG-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,35,0.4929,69,0.2776,0.02,Asian,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-EO-A2CH,TCGA-EO-A2CH-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,40,0.384,73,0.3164,0.28,,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-EO-A3AS,TCGA-EO-A3AS-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,157,0.5218,86,0.3139,0.25,,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-EO-A3AU,TCGA-EO-A3AU-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,669,0,72,0.8955,19.24,,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-EO-A3AV,TCGA-EO-A3AV-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,4097,0.0242,51,0.2853,0.05,,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-EO-A3AY,TCGA-EO-A3AY-01,Uterine Endometrioid Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,3382,0.0002,58,0.2784,0.18,,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-EO-A3AZ,TCGA-EO-A3AZ-01,Uterine Mixed Endometrial Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,1002,0.2176,80,0.6422,16.05,,UCEC_MSI,Mixed Serous and Endometrioid Carcinoma +TCGA-EO-A3B0,TCGA-EO-A3B0-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,10915,0.05,43,0.3052,0.14,Asian,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-EO-A3B1,TCGA-EO-A3B1-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,38,0.5055,63,0.2923,0.02,,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-EO-A3KU,TCGA-EO-A3KU-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,54,0.471,68,0.3216,0.31,,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-EO-A3KW,TCGA-EO-A3KW-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,41,0.3475,72,0.3047,0.11,,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-EO-A3KX,TCGA-EO-A3KX-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,3200,0.0002,80,1.2214,35.73,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-EO-A3L0,TCGA-EO-A3L0-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,75,0.0963,76,0.2865,0.02,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-EY-A1GC,TCGA-EY-A1GC-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,34,0.4314,62,0.2988,0.71,White,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-EY-A1GD,TCGA-EY-A1GD-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,1076,0.0347,51,0.2816,0.1,Black or African American,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-EY-A1GE,TCGA-EY-A1GE-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,41,0,67,0.2908,0,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-EY-A1GF,TCGA-EY-A1GF-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,340,0.0441,75,0.7116,16.22,Black or African American,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-EY-A1GH,TCGA-EY-A1GH-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,56,0.1841,70,0.2897,0.09,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-EY-A1GI,TCGA-EY-A1GI-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,4487,0.0005,52,0.2889,0.18,White,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-EY-A1GJ,TCGA-EY-A1GJ-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,45,0.2352,73,0.2833,0.31,Black or African American,,Endometrioid Endometrial Adenocarcinoma +TCGA-EY-A1GK,TCGA-EY-A1GK-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,839,0.055,74,0.9016,21.97,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-EY-A1GL,TCGA-EY-A1GL-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,42,0.0655,51,0.2939,0.05,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-EY-A1GM,TCGA-EY-A1GM-01,Uterine Mixed Endometrial Carcinoma,1:DECEASED,1:Recurred/Progressed,1:DEAD WITH TUMOR,33,0.324,60,0.2797,0.59,Black or African American,UCEC_CN_HIGH,Mixed Serous and Endometrioid Carcinoma +TCGA-EY-A1GO,TCGA-EY-A1GO-01,Uterine Endometrioid Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,291,0.6229,65,0.4959,3.82,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-EY-A1GP,TCGA-EY-A1GP-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,30,0.9472,54,0.3006,0.5,Black or African American,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-EY-A1GQ,TCGA-EY-A1GQ-01,Uterine Endometrioid Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,453,0.2584,76,0.6634,13.42,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-EY-A1GR,TCGA-EY-A1GR-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,42,0.723,67,0.281,0.58,Black or African American,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-EY-A1GS,TCGA-EY-A1GS-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,353,0.6302,71,0.3141,1.42,Black or African American,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-EY-A1GT,TCGA-EY-A1GT-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,78,0.081,68,0.2756,0.02,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-EY-A1GU,TCGA-EY-A1GU-01,Uterine Endometrioid Carcinoma,1:DECEASED,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,1181,0.0341,66,0.9342,22.78,Black or African American,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-EY-A1GV,TCGA-EY-A1GV-01,Uterine Mixed Endometrial Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,35,0.1646,75,0.2862,0.01,White,UCEC_CN_LOW,Mixed Serous and Endometrioid Carcinoma +TCGA-EY-A1GW,TCGA-EY-A1GW-01,Uterine Endometrioid Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,55,0.5739,73,0.2974,0.32,White,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-EY-A1GX,TCGA-EY-A1GX-01,Uterine Endometrioid Carcinoma,1:DECEASED,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,71,0.1997,76,0.3021,0.43,White,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-EY-A1H0,TCGA-EY-A1H0-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,342,0.1423,57,0.941,23.33,Black or African American,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-EY-A210,TCGA-EY-A210-01,Uterine Mixed Endometrial Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,32,0.2899,82,0.2829,0.3,Black or African American,UCEC_CN_HIGH,Mixed Serous and Endometrioid Carcinoma +TCGA-EY-A212,TCGA-EY-A212-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,64,0.3487,83,0.3103,0.53,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-EY-A214,TCGA-EY-A214-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,51,0.6385,66,0.2972,0.14,White,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-EY-A215,TCGA-EY-A215-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,4645,0.0013,60,0.8962,18.6,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-EY-A2OM,TCGA-EY-A2OM-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,621,0.0512,55,0.9415,21.96,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-EY-A2ON,TCGA-EY-A2ON-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,73,0.4427,61,0.2803,0.12,Black or African American,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-EY-A2OO,TCGA-EY-A2OO-01,Uterine Mixed Endometrial Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,63,0.4278,56,0.3071,0.06,White,UCEC_CN_HIGH,Mixed Serous and Endometrioid Carcinoma +TCGA-EY-A2OP,TCGA-EY-A2OP-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,448,0.0803,63,0.7773,14.61,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-EY-A2OQ,TCGA-EY-A2OQ-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,47,0.6205,61,0.3004,0.33,White,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-EY-A3L3,TCGA-EY-A3L3-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,53,0.5223,82,0.2962,0.94,Black or African American,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-EY-A3QX,TCGA-EY-A3QX-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,46,0.6635,64,0.3143,0.14,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-EY-A4KR,TCGA-EY-A4KR-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,73,0.2889,56,0.2999,0.06,Black or African American,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-EY-A547,TCGA-EY-A547-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,149,0.3713,75,0.4834,6.04,Black or African American,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-EY-A548,TCGA-EY-A548-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,596,0.0001,83,0.8116,17.93,Black or African American,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-EY-A549,TCGA-EY-A549-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,1323,0.156,78,0.8695,20.02,Black or African American,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-EY-A54A,TCGA-EY-A54A-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,101,0.2869,67,0.3081,0.17,Black or African American,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-EY-A5W2,TCGA-EY-A5W2-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,775,0.0341,72,0.9861,27.19,Black or African American,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-EY-A72D,TCGA-EY-A72D-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,50,0.7701,87,0.3729,1.6,Black or African American,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-FI-A2CX,TCGA-FI-A2CX-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,20,0.0044,82,0.2871,0,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-FI-A2CY,TCGA-FI-A2CY-01,Uterine Mixed Endometrial Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,33,0.2356,60,0.2839,0.3,Black or African American,UCEC_CN_HIGH,Mixed Serous and Endometrioid Carcinoma +TCGA-FI-A2D0,TCGA-FI-A2D0-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,6448,0,55,0.6227,8.76,White,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-FI-A2D2,TCGA-FI-A2D2-01,Uterine Mixed Endometrial Carcinoma,1:DECEASED,1:Recurred/Progressed,1:DEAD WITH TUMOR,40,0.5059,66,0.2878,0.37,White,UCEC_CN_HIGH,Mixed Serous and Endometrioid Carcinoma +TCGA-FI-A2D4,TCGA-FI-A2D4-01,Uterine Endometrioid Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,328,0.1856,44,0.814,14.97,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-FI-A2D5,TCGA-FI-A2D5-01,Uterine Endometrioid Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,13852,0.0005,56,0.3854,0.82,White,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-FI-A2D6,TCGA-FI-A2D6-01,Uterine Endometrioid Carcinoma,1:DECEASED,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,630,0.0456,74,1.1265,22.68,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-FI-A2EU,TCGA-FI-A2EU-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,57,0.3318,68,0.3271,1.87,,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-FI-A2EW,TCGA-FI-A2EW-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,98,0.5549,71,0.322,1.87,Black or African American,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-FI-A2EX,TCGA-FI-A2EX-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,63,0.527,58,0.3055,0.44,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-FI-A2EY,TCGA-FI-A2EY-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,95,0.6687,63,0.3239,1.09,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-FI-A2F4,TCGA-FI-A2F4-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,1906,0.0344,64,0.7492,19.52,Black or African American,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-FI-A2F8,TCGA-FI-A2F8-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,68,0.3156,64,0.3111,0.06,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-FI-A2F9,TCGA-FI-A2F9-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,45,0.3709,79,0.3153,0.22,White,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-FI-A3PV,TCGA-FI-A3PV-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,61,0.4828,68,0.2839,0.31,Black or African American,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-FI-A3PX,TCGA-FI-A3PX-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,87,0.513,57,0.3305,0.5,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-H5-A2HR,TCGA-H5-A2HR-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,46,0.2775,71,0.2896,0.51,Black or African American,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-JU-AAVI,TCGA-JU-AAVI-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,1:Recurred/Progressed,1:DEAD WITH TUMOR,48,0.4885,61,0.3208,0.46,Black or African American,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-K6-A3WQ,TCGA-K6-A3WQ-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,32,0.733,60,0.3158,0.63,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-KP-A3VZ,TCGA-KP-A3VZ-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,76,0.7216,69,0.3111,0.55,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-KP-A3W0,TCGA-KP-A3W0-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,39,0.3267,72,0.2945,0.21,,UCEC_CN_LOW,Serous Endometrial Adenocarcinoma +TCGA-KP-A3W1,TCGA-KP-A3W1-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,43,0.5404,76,0.2968,0.04,,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-KP-A3W3,TCGA-KP-A3W3-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR 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+TCGA-PG-A914,TCGA-PG-A914-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,135,0.6161,73,0.3571,0.34,Black or African American,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-PG-A915,TCGA-PG-A915-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,73,0.4091,60,0.3462,0.42,Black or African American,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-PG-A916,TCGA-PG-A916-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,41,0,70,0.3407,0.08,Black or African American,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-PG-A917,TCGA-PG-A917-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,715,0.0516,74,0.809,22.02,Black or African American,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-QF-A5YS,TCGA-QF-A5YS-01,Uterine Mixed Endometrial Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,1559,0,57,0.323,0.04,Black or African American,UCEC_POLE,Mixed Serous and Endometrioid Carcinoma +TCGA-QF-A5YT,TCGA-QF-A5YT-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,7,0.0001,57,0.3502,0,Black or African American,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-QS-A5YQ,TCGA-QS-A5YQ-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,933,0.0003,55,0.348,0,Black or African American,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma +TCGA-QS-A5YR,TCGA-QS-A5YR-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,246,0.0001,61,0.5106,3.89,Black or African American,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-QS-A744,TCGA-QS-A744-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,63,0.1992,86,0.3346,0.07,Black or African American,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-QS-A8F1,TCGA-QS-A8F1-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,63,0.6549,85,0.3647,0.15,Black or African American,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma +TCGA-SJ-A6ZI,TCGA-SJ-A6ZI-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,1306,0.0275,64,0.6138,14.35,Black or African American,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma +TCGA-SJ-A6ZJ,TCGA-SJ-A6ZJ-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,56,0.0466,61,0.3382,0,Black or African American,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma +TCGA-SL-A6J9,TCGA-SL-A6J9-01,Uterine Endometrioid Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,48,0.4226,73,0.3503,0.03,Black or African American,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma +TCGA-SL-A6JA,TCGA-SL-A6JA-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,742,0.0488,77,0.9515,26.1,Black or African American,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma From 19d054e87cef731202138e356e7eafcb2a0c14a0 Mon Sep 17 00:00:00 2001 From: RUTIKA WAGALEKAR <120473716+RutikaW1155@users.noreply.github.com> Date: Tue, 14 May 2024 20:08:39 +0530 Subject: [PATCH 05/12] Add files via upload --- ...dometriral Cancer Prediction Dataset.ipynb | 1426 +++++++++++++++++ 1 file changed, 1426 insertions(+) create mode 100644 Endometriral Cancer Prediction/Endometriral Cancer Prediction Dataset.ipynb diff --git a/Endometriral Cancer Prediction/Endometriral Cancer Prediction Dataset.ipynb b/Endometriral Cancer Prediction/Endometriral Cancer Prediction Dataset.ipynb new file mode 100644 index 00000000..86220286 --- /dev/null +++ b/Endometriral Cancer Prediction/Endometriral Cancer Prediction Dataset.ipynb @@ -0,0 +1,1426 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8ea6b3f8", + "metadata": {}, + "source": [ + "# Endometriral Cancer Prediction Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b9b66e04", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "#importing libraries\n", + "\n", + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "id": "62600d10", + "metadata": {}, + "source": [ + "# Load the dataset into Jupyter Notebook\n", + "\n", + "There are various formats for a dataset, .csv, .json, .xlsx etc. The dataset can be stored in different places, on your local machine or sometimes online. In our case, the PCOS Dataset is an online source, and it is in CSV (comma separated value) format.\n", + "\n", + "dataset name : Endometriral Cancer Prediction Dataset_data.csv\n", + "\n", + "The Pandas Library is a useful tool that enables us to read various datasets into a data frame; our Jupyter notebook platforms have a built-in Pandas Library so that all we need to do is import Pandas without installing." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "82c0c766", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Patient IDSample IDCancer Type DetailedOverall Survival StatusDisease Free StatusDisease-specific Survival statusMutation CountFraction Genome AlteredDiagnosis AgeMSI MANTIS ScoreMSIsensor ScoreRace CategorySubtypeTumor Type
0TCGA-2E-A9G8TCGA-2E-A9G8-01Uterine Endometrioid Carcinoma0:LIVINGNaN0:ALIVE OR DEAD TUMOR FREE65.00.331159.00.32340.85Black or African AmericanUCEC_CN_HIGHEndometrioid Endometrial Adenocarcinoma
1TCGA-4E-A92ETCGA-4E-A92E-01Uterine Endometrioid Carcinoma0:LIVING0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE147.00.034154.00.33960.01Black or African AmericanUCEC_CN_LOWEndometrioid Endometrial Adenocarcinoma
2TCGA-5B-A90CTCGA-5B-A90C-01Uterine Endometrioid Carcinoma0:LIVINGNaN0:ALIVE OR DEAD TUMOR FREE45.00.690369.00.33440.55Black or African AmericanUCEC_CN_HIGHEndometrioid Endometrial Adenocarcinoma
3TCGA-5S-A9Q8TCGA-5S-A9Q8-01Uterine Endometrioid Carcinoma0:LIVING0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE50.00.058151.00.31990.09Black or African AmericanUCEC_CN_LOWEndometrioid Endometrial Adenocarcinoma
4TCGA-A5-A0G1TCGA-A5-A0G1-01Uterine Serous Carcinoma/Uterine Papillary Ser...1:DECEASED0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE10808.00.000167.00.31081.74WhiteUCEC_POLESerous Endometrial Adenocarcinoma
.............................................
524TCGA-QS-A8F1TCGA-QS-A8F1-01Uterine Serous Carcinoma/Uterine Papillary Ser...1:DECEASEDNaN1:DEAD WITH TUMOR63.00.654985.00.36470.15Black or African AmericanUCEC_CN_HIGHSerous Endometrial Adenocarcinoma
525TCGA-SJ-A6ZITCGA-SJ-A6ZI-01Uterine Endometrioid Carcinoma0:LIVING0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE1306.00.027564.00.613814.35Black or African AmericanUCEC_MSIEndometrioid Endometrial Adenocarcinoma
526TCGA-SJ-A6ZJTCGA-SJ-A6ZJ-01Uterine Endometrioid Carcinoma0:LIVING0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE56.00.046661.00.33820.00Black or African AmericanUCEC_CN_LOWEndometrioid Endometrial Adenocarcinoma
527TCGA-SL-A6J9TCGA-SL-A6J9-01Uterine Endometrioid Carcinoma0:LIVINGNaN0:ALIVE OR DEAD TUMOR FREE48.00.422673.00.35030.03Black or African AmericanUCEC_CN_HIGHEndometrioid Endometrial Adenocarcinoma
528TCGA-SL-A6JATCGA-SL-A6JA-01Uterine Endometrioid Carcinoma0:LIVING0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE742.00.048877.00.951526.10Black or African AmericanUCEC_MSIEndometrioid Endometrial Adenocarcinoma
\n", + "

529 rows × 14 columns

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" + ], + "text/plain": [ + " Patient ID Sample ID \\\n", + "0 TCGA-2E-A9G8 TCGA-2E-A9G8-01 \n", + "1 TCGA-4E-A92E TCGA-4E-A92E-01 \n", + "2 TCGA-5B-A90C TCGA-5B-A90C-01 \n", + "3 TCGA-5S-A9Q8 TCGA-5S-A9Q8-01 \n", + "4 TCGA-A5-A0G1 TCGA-A5-A0G1-01 \n", + ".. ... ... \n", + "524 TCGA-QS-A8F1 TCGA-QS-A8F1-01 \n", + "525 TCGA-SJ-A6ZI TCGA-SJ-A6ZI-01 \n", + "526 TCGA-SJ-A6ZJ TCGA-SJ-A6ZJ-01 \n", + "527 TCGA-SL-A6J9 TCGA-SL-A6J9-01 \n", + "528 TCGA-SL-A6JA TCGA-SL-A6JA-01 \n", + "\n", + " Cancer Type Detailed \\\n", + "0 Uterine Endometrioid Carcinoma \n", + "1 Uterine Endometrioid Carcinoma \n", + "2 Uterine Endometrioid Carcinoma \n", + "3 Uterine Endometrioid Carcinoma \n", + "4 Uterine Serous Carcinoma/Uterine Papillary Ser... \n", + ".. ... \n", + "524 Uterine Serous Carcinoma/Uterine Papillary Ser... \n", + "525 Uterine Endometrioid Carcinoma \n", + "526 Uterine Endometrioid Carcinoma \n", + "527 Uterine Endometrioid Carcinoma \n", + "528 Uterine Endometrioid Carcinoma \n", + "\n", + " Overall Survival Status Disease Free Status \\\n", + "0 0:LIVING NaN \n", + "1 0:LIVING 0:DiseaseFree \n", + "2 0:LIVING NaN \n", + "3 0:LIVING 0:DiseaseFree \n", + "4 1:DECEASED 0:DiseaseFree \n", + ".. ... ... \n", + "524 1:DECEASED NaN \n", + "525 0:LIVING 0:DiseaseFree \n", + "526 0:LIVING 0:DiseaseFree \n", + "527 0:LIVING NaN \n", + "528 0:LIVING 0:DiseaseFree \n", + "\n", + " Disease-specific Survival status Mutation Count Fraction Genome Altered \\\n", + "0 0:ALIVE OR DEAD TUMOR FREE 65.0 0.3311 \n", + "1 0:ALIVE OR DEAD TUMOR FREE 147.0 0.0341 \n", + "2 0:ALIVE OR DEAD TUMOR FREE 45.0 0.6903 \n", + "3 0:ALIVE OR DEAD TUMOR FREE 50.0 0.0581 \n", + "4 0:ALIVE OR DEAD TUMOR FREE 10808.0 0.0001 \n", + ".. ... ... ... \n", + "524 1:DEAD WITH TUMOR 63.0 0.6549 \n", + "525 0:ALIVE OR DEAD TUMOR FREE 1306.0 0.0275 \n", + "526 0:ALIVE OR DEAD TUMOR FREE 56.0 0.0466 \n", + "527 0:ALIVE OR DEAD TUMOR FREE 48.0 0.4226 \n", + "528 0:ALIVE OR DEAD TUMOR FREE 742.0 0.0488 \n", + "\n", + " Diagnosis Age MSI MANTIS Score MSIsensor Score \\\n", + "0 59.0 0.3234 0.85 \n", + "1 54.0 0.3396 0.01 \n", + "2 69.0 0.3344 0.55 \n", + "3 51.0 0.3199 0.09 \n", + "4 67.0 0.3108 1.74 \n", + ".. ... ... ... \n", + "524 85.0 0.3647 0.15 \n", + "525 64.0 0.6138 14.35 \n", + "526 61.0 0.3382 0.00 \n", + "527 73.0 0.3503 0.03 \n", + "528 77.0 0.9515 26.10 \n", + "\n", + " Race Category Subtype \\\n", + "0 Black or African American UCEC_CN_HIGH \n", + "1 Black or African American UCEC_CN_LOW \n", + "2 Black or African American UCEC_CN_HIGH \n", + "3 Black or African American UCEC_CN_LOW \n", + "4 White UCEC_POLE \n", + ".. ... ... \n", + "524 Black or African American UCEC_CN_HIGH \n", + "525 Black or African American UCEC_MSI \n", + "526 Black or African American UCEC_CN_LOW \n", + "527 Black or African American UCEC_CN_HIGH \n", + "528 Black or African American UCEC_MSI \n", + "\n", + " Tumor Type \n", + "0 Endometrioid Endometrial Adenocarcinoma \n", + "1 Endometrioid Endometrial Adenocarcinoma \n", + "2 Endometrioid Endometrial Adenocarcinoma \n", + "3 Endometrioid Endometrial Adenocarcinoma \n", + "4 Serous Endometrial Adenocarcinoma \n", + ".. ... \n", + "524 Serous Endometrial Adenocarcinoma \n", + "525 Endometrioid Endometrial Adenocarcinoma \n", + "526 Endometrioid Endometrial Adenocarcinoma \n", + "527 Endometrioid Endometrial Adenocarcinoma \n", + "528 Endometrioid Endometrial Adenocarcinoma \n", + "\n", + "[529 rows x 14 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#load the dataset\n", + "#Read the dataset in data varaible\n", + "\n", + "data = pd.read_csv(r\"D:\\PYTHON\\Uterine Corpus Endometrial Carcinoma dataset.csv\")\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d125cb6e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Patient IDSample IDCancer Type DetailedOverall Survival StatusDisease Free StatusDisease-specific Survival statusMutation CountFraction Genome AlteredDiagnosis AgeMSI MANTIS ScoreMSIsensor ScoreRace CategorySubtypeTumor Type
0TCGA-2E-A9G8TCGA-2E-A9G8-01Uterine Endometrioid Carcinoma0:LIVINGNaN0:ALIVE OR DEAD TUMOR FREE65.00.331159.00.32340.85Black or African AmericanUCEC_CN_HIGHEndometrioid Endometrial Adenocarcinoma
1TCGA-4E-A92ETCGA-4E-A92E-01Uterine Endometrioid Carcinoma0:LIVING0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE147.00.034154.00.33960.01Black or African AmericanUCEC_CN_LOWEndometrioid Endometrial Adenocarcinoma
2TCGA-5B-A90CTCGA-5B-A90C-01Uterine Endometrioid Carcinoma0:LIVINGNaN0:ALIVE OR DEAD TUMOR FREE45.00.690369.00.33440.55Black or African AmericanUCEC_CN_HIGHEndometrioid Endometrial Adenocarcinoma
3TCGA-5S-A9Q8TCGA-5S-A9Q8-01Uterine Endometrioid Carcinoma0:LIVING0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE50.00.058151.00.31990.09Black or African AmericanUCEC_CN_LOWEndometrioid Endometrial Adenocarcinoma
4TCGA-A5-A0G1TCGA-A5-A0G1-01Uterine Serous Carcinoma/Uterine Papillary Ser...1:DECEASED0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE10808.00.000167.00.31081.74WhiteUCEC_POLESerous Endometrial Adenocarcinoma
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" + ], + "text/plain": [ + " Patient ID Sample ID \\\n", + "0 TCGA-2E-A9G8 TCGA-2E-A9G8-01 \n", + "1 TCGA-4E-A92E TCGA-4E-A92E-01 \n", + "2 TCGA-5B-A90C TCGA-5B-A90C-01 \n", + "3 TCGA-5S-A9Q8 TCGA-5S-A9Q8-01 \n", + "4 TCGA-A5-A0G1 TCGA-A5-A0G1-01 \n", + "\n", + " Cancer Type Detailed Overall Survival Status \\\n", + "0 Uterine Endometrioid Carcinoma 0:LIVING \n", + "1 Uterine Endometrioid Carcinoma 0:LIVING \n", + "2 Uterine Endometrioid Carcinoma 0:LIVING \n", + "3 Uterine Endometrioid Carcinoma 0:LIVING \n", + "4 Uterine Serous Carcinoma/Uterine Papillary Ser... 1:DECEASED \n", + "\n", + " Disease Free Status Disease-specific Survival status Mutation Count \\\n", + "0 NaN 0:ALIVE OR DEAD TUMOR FREE 65.0 \n", + "1 0:DiseaseFree 0:ALIVE OR DEAD TUMOR FREE 147.0 \n", + "2 NaN 0:ALIVE OR DEAD TUMOR FREE 45.0 \n", + "3 0:DiseaseFree 0:ALIVE OR DEAD TUMOR FREE 50.0 \n", + "4 0:DiseaseFree 0:ALIVE OR DEAD TUMOR FREE 10808.0 \n", + "\n", + " Fraction Genome Altered Diagnosis Age MSI MANTIS Score MSIsensor Score \\\n", + "0 0.3311 59.0 0.3234 0.85 \n", + "1 0.0341 54.0 0.3396 0.01 \n", + "2 0.6903 69.0 0.3344 0.55 \n", + "3 0.0581 51.0 0.3199 0.09 \n", + "4 0.0001 67.0 0.3108 1.74 \n", + "\n", + " Race Category Subtype \\\n", + "0 Black or African American UCEC_CN_HIGH \n", + "1 Black or African American UCEC_CN_LOW \n", + "2 Black or African American UCEC_CN_HIGH \n", + "3 Black or African American UCEC_CN_LOW \n", + "4 White UCEC_POLE \n", + "\n", + " Tumor Type \n", + "0 Endometrioid Endometrial Adenocarcinoma \n", + "1 Endometrioid Endometrial Adenocarcinoma \n", + "2 Endometrioid Endometrial Adenocarcinoma \n", + "3 Endometrioid Endometrial Adenocarcinoma \n", + "4 Serous Endometrial Adenocarcinoma " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "#Show the first 5 rows of the dataset\n", + "\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e668681f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Patient IDSample IDCancer Type DetailedOverall Survival StatusDisease Free StatusDisease-specific Survival statusMutation CountFraction Genome AlteredDiagnosis AgeMSI MANTIS ScoreMSIsensor ScoreRace CategorySubtypeTumor Type
524TCGA-QS-A8F1TCGA-QS-A8F1-01Uterine Serous Carcinoma/Uterine Papillary Ser...1:DECEASEDNaN1:DEAD WITH TUMOR63.00.654985.00.36470.15Black or African AmericanUCEC_CN_HIGHSerous Endometrial Adenocarcinoma
525TCGA-SJ-A6ZITCGA-SJ-A6ZI-01Uterine Endometrioid Carcinoma0:LIVING0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE1306.00.027564.00.613814.35Black or African AmericanUCEC_MSIEndometrioid Endometrial Adenocarcinoma
526TCGA-SJ-A6ZJTCGA-SJ-A6ZJ-01Uterine Endometrioid Carcinoma0:LIVING0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE56.00.046661.00.33820.00Black or African AmericanUCEC_CN_LOWEndometrioid Endometrial Adenocarcinoma
527TCGA-SL-A6J9TCGA-SL-A6J9-01Uterine Endometrioid Carcinoma0:LIVINGNaN0:ALIVE OR DEAD TUMOR FREE48.00.422673.00.35030.03Black or African AmericanUCEC_CN_HIGHEndometrioid Endometrial Adenocarcinoma
528TCGA-SL-A6JATCGA-SL-A6JA-01Uterine Endometrioid Carcinoma0:LIVING0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE742.00.048877.00.951526.10Black or African AmericanUCEC_MSIEndometrioid Endometrial Adenocarcinoma
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" + ], + "text/plain": [ + " Patient ID Sample ID \\\n", + "524 TCGA-QS-A8F1 TCGA-QS-A8F1-01 \n", + "525 TCGA-SJ-A6ZI TCGA-SJ-A6ZI-01 \n", + "526 TCGA-SJ-A6ZJ TCGA-SJ-A6ZJ-01 \n", + "527 TCGA-SL-A6J9 TCGA-SL-A6J9-01 \n", + "528 TCGA-SL-A6JA TCGA-SL-A6JA-01 \n", + "\n", + " Cancer Type Detailed \\\n", + "524 Uterine Serous Carcinoma/Uterine Papillary Ser... \n", + "525 Uterine Endometrioid Carcinoma \n", + "526 Uterine Endometrioid Carcinoma \n", + "527 Uterine Endometrioid Carcinoma \n", + "528 Uterine Endometrioid Carcinoma \n", + "\n", + " Overall Survival Status Disease Free Status \\\n", + "524 1:DECEASED NaN \n", + "525 0:LIVING 0:DiseaseFree \n", + "526 0:LIVING 0:DiseaseFree \n", + "527 0:LIVING NaN \n", + "528 0:LIVING 0:DiseaseFree \n", + "\n", + " Disease-specific Survival status Mutation Count Fraction Genome Altered \\\n", + "524 1:DEAD WITH TUMOR 63.0 0.6549 \n", + "525 0:ALIVE OR DEAD TUMOR FREE 1306.0 0.0275 \n", + "526 0:ALIVE OR DEAD TUMOR FREE 56.0 0.0466 \n", + "527 0:ALIVE OR DEAD TUMOR FREE 48.0 0.4226 \n", + "528 0:ALIVE OR DEAD TUMOR FREE 742.0 0.0488 \n", + "\n", + " Diagnosis Age MSI MANTIS Score MSIsensor Score \\\n", + "524 85.0 0.3647 0.15 \n", + "525 64.0 0.6138 14.35 \n", + "526 61.0 0.3382 0.00 \n", + "527 73.0 0.3503 0.03 \n", + "528 77.0 0.9515 26.10 \n", + "\n", + " Race Category Subtype \\\n", + "524 Black or African American UCEC_CN_HIGH \n", + "525 Black or African American UCEC_MSI \n", + "526 Black or African American UCEC_CN_LOW \n", + "527 Black or African American UCEC_CN_HIGH \n", + "528 Black or African American UCEC_MSI \n", + "\n", + " Tumor Type \n", + "524 Serous Endometrial Adenocarcinoma \n", + "525 Endometrioid Endometrial Adenocarcinoma \n", + "526 Endometrioid Endometrial Adenocarcinoma \n", + "527 Endometrioid Endometrial Adenocarcinoma \n", + "528 Endometrioid Endometrial Adenocarcinoma " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Show the last 5 rows of the dataset\n", + "\n", + "data.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9a7689f4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Patient ID\n", + "Sample ID\n", + "Cancer Type Detailed\n", + "Overall Survival Status\n", + "Disease Free Status\n", + "Disease-specific Survival status\n", + "Mutation Count\n", + "Fraction Genome Altered\n", + "Diagnosis Age\n", + "MSI MANTIS Score\n", + "MSIsensor Score\n", + "Race Category\n", + "Subtype\n", + "Tumor Type\n" + ] + } + ], + "source": [ + "\n", + "# List of column names\n", + "\n", + "for col in list(data.columns):\n", + " print(col)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "18eb88f2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of rows: 529\n", + "Number of columns: 14\n" + ] + } + ], + "source": [ + "#gives the size/shape of the dataset\n", + "#How many columns and rows are there in the dataset.\n", + "\n", + "num_rows, num_columns = data.shape\n", + "\n", + "print(\"Number of rows:\", num_rows)\n", + "print(\"Number of columns:\", num_columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "21946a8b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Patient ID object\n", + "Sample ID object\n", + "Cancer Type Detailed object\n", + "Overall Survival Status object\n", + "Disease Free Status object\n", + "Disease-specific Survival status object\n", + "Mutation Count float64\n", + "Fraction Genome Altered float64\n", + "Diagnosis Age float64\n", + "MSI MANTIS Score float64\n", + "MSIsensor Score float64\n", + "Race Category object\n", + "Subtype object\n", + "Tumor Type object\n", + "dtype: object" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Check the data type of dataframe pcos\n", + "data.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "77be34a4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 529 entries, 0 to 528\n", + "Data columns (total 14 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Patient ID 529 non-null object \n", + " 1 Sample ID 529 non-null object \n", + " 2 Cancer Type Detailed 529 non-null object \n", + " 3 Overall Survival Status 529 non-null object \n", + " 4 Disease Free Status 414 non-null object \n", + " 5 Disease-specific Survival status 527 non-null object \n", + " 6 Mutation Count 515 non-null float64\n", + " 7 Fraction Genome Altered 519 non-null float64\n", + " 8 Diagnosis Age 526 non-null float64\n", + " 9 MSI MANTIS Score 526 non-null float64\n", + " 10 MSIsensor Score 528 non-null float64\n", + " 11 Race Category 497 non-null object \n", + " 12 Subtype 507 non-null object \n", + " 13 Tumor Type 529 non-null object \n", + "dtypes: float64(5), object(9)\n", + "memory usage: 58.0+ KB\n" + ] + } + ], + "source": [ + "#Information about the dataset\n", + "#This method prints information about a DataFrame including the index dtype and columns, non-null values and memory usage. \n", + "\n", + "data.info()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "bfb8dc6f", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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countmeanstdmin25%50%75%max
Mutation Count515.01046.4233012734.3654947.00047.0000076.0000563.50000025696.0000
Fraction Genome Altered519.00.1859590.2260880.0000.003600.06780.3144500.9487
Diagnosis Age526.063.76616011.06003031.00057.0000064.000071.00000090.0000
MSI MANTIS Score526.00.4389480.2396660.2370.283950.31130.5432251.3049
MSIsensor Score528.06.39589010.1758530.0000.110000.435010.11750040.4300
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" + ], + "text/plain": [ + " count mean std min 25% \\\n", + "Mutation Count 515.0 1046.423301 2734.365494 7.000 47.00000 \n", + "Fraction Genome Altered 519.0 0.185959 0.226088 0.000 0.00360 \n", + "Diagnosis Age 526.0 63.766160 11.060030 31.000 57.00000 \n", + "MSI MANTIS Score 526.0 0.438948 0.239666 0.237 0.28395 \n", + "MSIsensor Score 528.0 6.395890 10.175853 0.000 0.11000 \n", + "\n", + " 50% 75% max \n", + "Mutation Count 76.0000 563.500000 25696.0000 \n", + "Fraction Genome Altered 0.0678 0.314450 0.9487 \n", + "Diagnosis Age 64.0000 71.000000 90.0000 \n", + "MSI MANTIS Score 0.3113 0.543225 1.3049 \n", + "MSIsensor Score 0.4350 10.117500 40.4300 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "#If we would like to get a statistical summary of each column, such as count, column mean value, column standard deviation, etc.\n", + "#We use the describe method:\n", + "\n", + "data.describe().T" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "b931888d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Patient ID 0\n", + "Sample ID 0\n", + "Cancer Type Detailed 0\n", + "Overall Survival Status 0\n", + "Disease Free Status 115\n", + "Disease-specific Survival status 2\n", + "Mutation Count 14\n", + "Fraction Genome Altered 10\n", + "Diagnosis Age 3\n", + "MSI MANTIS Score 3\n", + "MSIsensor Score 1\n", + "Race Category 32\n", + "Subtype 22\n", + "Tumor Type 0\n", + "dtype: int64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check the null values present in dataset \n", + "data.isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "id": "0822b004", + "metadata": {}, + "source": [ + "# matplotlib.pyplot, seaborn, and scipy.stats libraries are used for data visualization and statistical analysis. These are commonly used libraries in Python for such tasks.\n", + "\n", + "matplotlib.pyplot is a plotting library that provides a MATLAB-like interface for creating visualizations in Python.\n", + "\n", + "seaborn is a statistical data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.\n", + "\n", + "scipy.stats contains a large number of probability distributions and statistical functions. It's useful for statistical analysis and hypothesis testing.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "63b47d52", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import scipy.stats as stats" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "ceb4c2c2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.histplot(data['Diagnosis Age']) # histogram plot for Age-count" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "4a00d3d5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.boxplot(data['Diagnosis Age']) # boxplot for Age" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "3a3ccbc3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.histplot(data['Mutation Count']) # histogram plot for Mutation Count" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "85226ea8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.boxplot(data['Mutation Count']) # boxplot for Mutation Count" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "620f6584", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.histplot(data['MSI MANTIS Score']) # histogram plot for MSI MANTIS Score " + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "2b359cab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.boxplot(data['MSI MANTIS Score']) # boxplot for MSI MANTIS Score" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "2b103601", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.histplot(data['MSIsensor Score']) # histogram plot for MSIsensor Score" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "46fe3b0c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.boxplot(data['MSIsensor Score']) # boxplot for MSIsensor Score" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 93dbfc89890d2ea5e63eea46cee179483ffbf2ee Mon Sep 17 00:00:00 2001 From: RUTIKA WAGALEKAR <120473716+RutikaW1155@users.noreply.github.com> Date: Tue, 14 May 2024 21:07:41 +0530 Subject: [PATCH 06/12] Update README.md --- Endometriral Cancer Prediction/README.md | 29 ++++++++++++++++++++++++ 1 file changed, 29 insertions(+) diff --git a/Endometriral Cancer Prediction/README.md b/Endometriral Cancer Prediction/README.md index e7b27d0d..e051c521 100644 --- a/Endometriral Cancer Prediction/README.md +++ b/Endometriral Cancer Prediction/README.md @@ -18,3 +18,32 @@ MSIsensor is a tool used for microsatellite instability detection using paired t ## Usage - Researchers and healthcare professionals can utilize this dataset for studying endometrial cancer and its various types. - ial and should be used only for educational purposes. + +## Attribute Description +1.Patient ID: Unique identifier for each patient. + +2.Sample ID: Unique identifier for each sample. + +3.Cancer Type Detailed: Detailed description of the cancer type. + +4.Overall Survival Status: Patient's overall survival status (e.g., "1:DECEASED" indicates deceased). + +5.Disease Free Status: Patient's disease-free status (e.g., "0:DiseaseFree" indicates disease-free). + +6.Disease-specific Survival status: Patient's disease-specific survival status. + +7.Mutation Count: Number of mutations detected. + +8.Fraction Genome Altered: Fraction of the genome that is altered. + +9.Diagnosis Age: Age of the patient at diagnosis. + +10.MSI MANTIS Score: MSI (Microsatellite Instability) score calculated using MANTIS algorithm. + +11.MSIsensor Score: MSI (Microsatellite Instability) score calculated using MSIsensor. + +12.Race Category: Patient's race or ethnicity category. + +13.Subtype: Subtype of the cancer. + +14.Tumor Type: Type of tumor (e.g., "Serous Endometrial Adenocarcinoma"). From 356e48a8c671767b269b763018a445f27449d30a Mon Sep 17 00:00:00 2001 From: RUTIKA WAGALEKAR <120473716+RutikaW1155@users.noreply.github.com> Date: Sun, 19 May 2024 12:59:32 +0530 Subject: [PATCH 07/12] Update README.md --- Endometriral Cancer Prediction/README.md | 54 ++++++++++++++++++------ 1 file changed, 41 insertions(+), 13 deletions(-) diff --git a/Endometriral Cancer Prediction/README.md b/Endometriral Cancer Prediction/README.md index e051c521..348c9467 100644 --- a/Endometriral Cancer Prediction/README.md +++ b/Endometriral Cancer Prediction/README.md @@ -1,4 +1,4 @@ -# Endometrial cancer Prediction Dataset +# Endometrial cancer Prediction Dataset 💻 Welcome to the Endometrial cancer Prediction Dataset! 🎉 This dataset contains information about endometrial cancer, also known as endometrial carcinoma, which is a type of cancer that starts in the cells of the inner lining of the uterus (the endometrium). Endometrial carcinomas can be categorized into different types based on cellular characteristics observed under a microscope. @@ -9,24 +9,14 @@ Welcome to the Endometrial cancer Prediction Dataset! 🎉 This dataset contains The exact cause of uterine cancer is not fully understood. However, it is believed that mutations occur in the cells of the uterus, causing them to grow and multiply uncontrollably, leading to the formation of tumors. -## MSI Mantis Score -The MANTIS score is a predictive score for a patient's MSI (Microsatellite Instability) status. A higher MANTIS score indicates a higher likelihood of MSI-H (high microsatellite instability) status. - -## MSI Sensor -MSIsensor is a tool used for microsatellite instability detection using paired tumor-normal sequence data. The resulting MSIsensor score is a value between 0 and 100, indicating the percentage of mutated microsatellite loci. - -## Usage -- Researchers and healthcare professionals can utilize this dataset for studying endometrial cancer and its various types. -- ial and should be used only for educational purposes. - -## Attribute Description +## Attribute Description 📊 1.Patient ID: Unique identifier for each patient. 2.Sample ID: Unique identifier for each sample. 3.Cancer Type Detailed: Detailed description of the cancer type. -4.Overall Survival Status: Patient's overall survival status (e.g., "1:DECEASED" indicates deceased). +4.Overall Survival Status: Patient's overall survival status (e.g., "1:DECEASED" indicates deceased compared to "0:LIVING" (indicating living)). 5.Disease Free Status: Patient's disease-free status (e.g., "0:DiseaseFree" indicates disease-free). @@ -47,3 +37,41 @@ MSIsensor is a tool used for microsatellite instability detection using paired t 13.Subtype: Subtype of the cancer. 14.Tumor Type: Type of tumor (e.g., "Serous Endometrial Adenocarcinoma"). + +## Overall Survival Status Distribution: + +The distribution of overall survival status shows that there is a class imbalance, with a higher proportion of patients being labeled as "1:DECEASED" (indicating deceased) compared to "0:LIVING" (indicating living) + +## Disease Free Status Distribution: + +The distribution of disease-free status indicates the proportion of patients who are disease-free at a certain point in time. Further analysis may reveal trends in disease recurrence or remission. + +## Mutation Count and Fraction Genome Altered: + +The distribution of mutation count and fraction genome altered suggests variability among patients in terms of genetic alterations. Some patients may have a higher number of mutations or a larger fraction of the genome altered, which could impact disease progression and treatment response. + +## MSI Mantis Score +The MANTIS score is a predictive score for a patient's MSI (Microsatellite Instability) status. A higher MANTIS score indicates a higher likelihood of MSI-H (high microsatellite instability) status. + +## MSI Sensor +MSIsensor is a tool used for microsatellite instability detection using paired tumor-normal sequence data. The resulting MSIsensor score is a value between 0 and 100, indicating the percentage of mutated microsatellite loci. + +## Diagnosis Age Distribution: + +The distribution of diagnosis age shows the age range at which patients are diagnosed with cancer. Understanding the age distribution can provide insights into the demographics of the patient population and potential age-related factors influencing cancer development. + +## Race Category and Ethnicity: + +Exploring the race category and ethnicity of patients can shed light on disparities in cancer incidence, treatment outcomes, and access to healthcare services among different demographic groups. + +## Cancer Subtypes and Tumor Types: + +The detailed descriptions of cancer subtypes and tumor types provide valuable information about the specific characteristics of the cancer cases included in the dataset. Analyzing these attributes can help identify patterns and associations with clinical outcomes. + +## Usage +- Researchers and healthcare professionals can utilize this dataset for studying endometrial cancer and its various types. +- ial and should be used only for educational purposes. + +## Conclusion: + +The EDA of the Endometrial cancer Prediction Dataset provided valuable insights into the demographic and clinical characteristics of the patient population. Analysis of attributes such as overall survival status, disease-free status, mutation count, MSI scores, diagnosis age, race category, and tumor subtype revealed patterns and associations relevant to cancer prognosis and treatment. The findings underscore the importance of understanding the heterogeneity of cancer and its impact on patient outcomes. Further research based on these insights could contribute to personalized approaches to cancer care and improve treatment strategies for better patient outcomes. From 593c877ed116340b9f8ce66ec7d224baa40ae6cc Mon Sep 17 00:00:00 2001 From: RUTIKA WAGALEKAR <120473716+RutikaW1155@users.noreply.github.com> Date: Sun, 19 May 2024 13:15:10 +0530 Subject: [PATCH 08/12] Delete Endometriral Cancer Prediction/Endometriral Cancer Prediction Dataset.ipynb --- ...dometriral Cancer Prediction Dataset.ipynb | 1426 ----------------- 1 file changed, 1426 deletions(-) delete mode 100644 Endometriral Cancer Prediction/Endometriral Cancer Prediction Dataset.ipynb diff --git a/Endometriral Cancer Prediction/Endometriral Cancer Prediction Dataset.ipynb b/Endometriral Cancer Prediction/Endometriral Cancer Prediction Dataset.ipynb deleted file mode 100644 index 86220286..00000000 --- a/Endometriral Cancer Prediction/Endometriral Cancer Prediction Dataset.ipynb +++ /dev/null @@ -1,1426 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "8ea6b3f8", - "metadata": {}, - "source": [ - "# Endometriral Cancer Prediction Dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "b9b66e04", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "#importing libraries\n", - "\n", - "import numpy as np\n", - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "id": "62600d10", - "metadata": {}, - "source": [ - "# Load the dataset into Jupyter Notebook\n", - "\n", - "There are various formats for a dataset, .csv, .json, .xlsx etc. The dataset can be stored in different places, on your local machine or sometimes online. In our case, the PCOS Dataset is an online source, and it is in CSV (comma separated value) format.\n", - "\n", - "dataset name : Endometriral Cancer Prediction Dataset_data.csv\n", - "\n", - "The Pandas Library is a useful tool that enables us to read various datasets into a data frame; our Jupyter notebook platforms have a built-in Pandas Library so that all we need to do is import Pandas without installing." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "82c0c766", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Patient IDSample IDCancer Type DetailedOverall Survival StatusDisease Free StatusDisease-specific Survival statusMutation CountFraction Genome AlteredDiagnosis AgeMSI MANTIS ScoreMSIsensor ScoreRace CategorySubtypeTumor Type
0TCGA-2E-A9G8TCGA-2E-A9G8-01Uterine Endometrioid Carcinoma0:LIVINGNaN0:ALIVE OR DEAD TUMOR FREE65.00.331159.00.32340.85Black or African AmericanUCEC_CN_HIGHEndometrioid Endometrial Adenocarcinoma
1TCGA-4E-A92ETCGA-4E-A92E-01Uterine Endometrioid Carcinoma0:LIVING0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE147.00.034154.00.33960.01Black or African AmericanUCEC_CN_LOWEndometrioid Endometrial Adenocarcinoma
2TCGA-5B-A90CTCGA-5B-A90C-01Uterine Endometrioid Carcinoma0:LIVINGNaN0:ALIVE OR DEAD TUMOR FREE45.00.690369.00.33440.55Black or African AmericanUCEC_CN_HIGHEndometrioid Endometrial Adenocarcinoma
3TCGA-5S-A9Q8TCGA-5S-A9Q8-01Uterine Endometrioid Carcinoma0:LIVING0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE50.00.058151.00.31990.09Black or African AmericanUCEC_CN_LOWEndometrioid Endometrial Adenocarcinoma
4TCGA-A5-A0G1TCGA-A5-A0G1-01Uterine Serous Carcinoma/Uterine Papillary Ser...1:DECEASED0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE10808.00.000167.00.31081.74WhiteUCEC_POLESerous Endometrial Adenocarcinoma
.............................................
524TCGA-QS-A8F1TCGA-QS-A8F1-01Uterine Serous Carcinoma/Uterine Papillary Ser...1:DECEASEDNaN1:DEAD WITH TUMOR63.00.654985.00.36470.15Black or African AmericanUCEC_CN_HIGHSerous Endometrial Adenocarcinoma
525TCGA-SJ-A6ZITCGA-SJ-A6ZI-01Uterine Endometrioid Carcinoma0:LIVING0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE1306.00.027564.00.613814.35Black or African AmericanUCEC_MSIEndometrioid Endometrial Adenocarcinoma
526TCGA-SJ-A6ZJTCGA-SJ-A6ZJ-01Uterine Endometrioid Carcinoma0:LIVING0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE56.00.046661.00.33820.00Black or African AmericanUCEC_CN_LOWEndometrioid Endometrial Adenocarcinoma
527TCGA-SL-A6J9TCGA-SL-A6J9-01Uterine Endometrioid Carcinoma0:LIVINGNaN0:ALIVE OR DEAD TUMOR FREE48.00.422673.00.35030.03Black or African AmericanUCEC_CN_HIGHEndometrioid Endometrial Adenocarcinoma
528TCGA-SL-A6JATCGA-SL-A6JA-01Uterine Endometrioid Carcinoma0:LIVING0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE742.00.048877.00.951526.10Black or African AmericanUCEC_MSIEndometrioid Endometrial Adenocarcinoma
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529 rows × 14 columns

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" - ], - "text/plain": [ - " Patient ID Sample ID \\\n", - "0 TCGA-2E-A9G8 TCGA-2E-A9G8-01 \n", - "1 TCGA-4E-A92E TCGA-4E-A92E-01 \n", - "2 TCGA-5B-A90C TCGA-5B-A90C-01 \n", - "3 TCGA-5S-A9Q8 TCGA-5S-A9Q8-01 \n", - "4 TCGA-A5-A0G1 TCGA-A5-A0G1-01 \n", - ".. ... ... \n", - "524 TCGA-QS-A8F1 TCGA-QS-A8F1-01 \n", - "525 TCGA-SJ-A6ZI TCGA-SJ-A6ZI-01 \n", - "526 TCGA-SJ-A6ZJ TCGA-SJ-A6ZJ-01 \n", - "527 TCGA-SL-A6J9 TCGA-SL-A6J9-01 \n", - "528 TCGA-SL-A6JA TCGA-SL-A6JA-01 \n", - "\n", - " Cancer Type Detailed \\\n", - "0 Uterine Endometrioid Carcinoma \n", - "1 Uterine Endometrioid Carcinoma \n", - "2 Uterine Endometrioid Carcinoma \n", - "3 Uterine Endometrioid Carcinoma \n", - "4 Uterine Serous Carcinoma/Uterine Papillary Ser... \n", - ".. ... \n", - "524 Uterine Serous Carcinoma/Uterine Papillary Ser... \n", - "525 Uterine Endometrioid Carcinoma \n", - "526 Uterine Endometrioid Carcinoma \n", - "527 Uterine Endometrioid Carcinoma \n", - "528 Uterine Endometrioid Carcinoma \n", - "\n", - " Overall Survival Status Disease Free Status \\\n", - "0 0:LIVING NaN \n", - "1 0:LIVING 0:DiseaseFree \n", - "2 0:LIVING NaN \n", - "3 0:LIVING 0:DiseaseFree \n", - "4 1:DECEASED 0:DiseaseFree \n", - ".. ... ... \n", - "524 1:DECEASED NaN \n", - "525 0:LIVING 0:DiseaseFree \n", - "526 0:LIVING 0:DiseaseFree \n", - "527 0:LIVING NaN \n", - "528 0:LIVING 0:DiseaseFree \n", - "\n", - " Disease-specific Survival status Mutation Count Fraction Genome Altered \\\n", - "0 0:ALIVE OR DEAD TUMOR FREE 65.0 0.3311 \n", - "1 0:ALIVE OR DEAD TUMOR FREE 147.0 0.0341 \n", - "2 0:ALIVE OR DEAD TUMOR FREE 45.0 0.6903 \n", - "3 0:ALIVE OR DEAD TUMOR FREE 50.0 0.0581 \n", - "4 0:ALIVE OR DEAD TUMOR FREE 10808.0 0.0001 \n", - ".. ... ... ... \n", - "524 1:DEAD WITH TUMOR 63.0 0.6549 \n", - "525 0:ALIVE OR DEAD TUMOR FREE 1306.0 0.0275 \n", - "526 0:ALIVE OR DEAD TUMOR FREE 56.0 0.0466 \n", - "527 0:ALIVE OR DEAD TUMOR FREE 48.0 0.4226 \n", - "528 0:ALIVE OR DEAD TUMOR FREE 742.0 0.0488 \n", - "\n", - " Diagnosis Age MSI MANTIS Score MSIsensor Score \\\n", - "0 59.0 0.3234 0.85 \n", - "1 54.0 0.3396 0.01 \n", - "2 69.0 0.3344 0.55 \n", - "3 51.0 0.3199 0.09 \n", - "4 67.0 0.3108 1.74 \n", - ".. ... ... ... \n", - "524 85.0 0.3647 0.15 \n", - "525 64.0 0.6138 14.35 \n", - "526 61.0 0.3382 0.00 \n", - "527 73.0 0.3503 0.03 \n", - "528 77.0 0.9515 26.10 \n", - "\n", - " Race Category Subtype \\\n", - "0 Black or African American UCEC_CN_HIGH \n", - "1 Black or African American UCEC_CN_LOW \n", - "2 Black or African American UCEC_CN_HIGH \n", - "3 Black or African American UCEC_CN_LOW \n", - "4 White UCEC_POLE \n", - ".. ... ... \n", - "524 Black or African American UCEC_CN_HIGH \n", - "525 Black or African American UCEC_MSI \n", - "526 Black or African American UCEC_CN_LOW \n", - "527 Black or African American UCEC_CN_HIGH \n", - "528 Black or African American UCEC_MSI \n", - "\n", - " Tumor Type \n", - "0 Endometrioid Endometrial Adenocarcinoma \n", - "1 Endometrioid Endometrial Adenocarcinoma \n", - "2 Endometrioid Endometrial Adenocarcinoma \n", - "3 Endometrioid Endometrial Adenocarcinoma \n", - "4 Serous Endometrial Adenocarcinoma \n", - ".. ... \n", - "524 Serous Endometrial Adenocarcinoma \n", - "525 Endometrioid Endometrial Adenocarcinoma \n", - "526 Endometrioid Endometrial Adenocarcinoma \n", - "527 Endometrioid Endometrial Adenocarcinoma \n", - "528 Endometrioid Endometrial Adenocarcinoma \n", - "\n", - "[529 rows x 14 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#load the dataset\n", - "#Read the dataset in data varaible\n", - "\n", - "data = pd.read_csv(r\"D:\\PYTHON\\Uterine Corpus Endometrial Carcinoma dataset.csv\")\n", - "data" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "d125cb6e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Patient IDSample IDCancer Type DetailedOverall Survival StatusDisease Free StatusDisease-specific Survival statusMutation CountFraction Genome AlteredDiagnosis AgeMSI MANTIS ScoreMSIsensor ScoreRace CategorySubtypeTumor Type
0TCGA-2E-A9G8TCGA-2E-A9G8-01Uterine Endometrioid Carcinoma0:LIVINGNaN0:ALIVE OR DEAD TUMOR FREE65.00.331159.00.32340.85Black or African AmericanUCEC_CN_HIGHEndometrioid Endometrial Adenocarcinoma
1TCGA-4E-A92ETCGA-4E-A92E-01Uterine Endometrioid Carcinoma0:LIVING0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE147.00.034154.00.33960.01Black or African AmericanUCEC_CN_LOWEndometrioid Endometrial Adenocarcinoma
2TCGA-5B-A90CTCGA-5B-A90C-01Uterine Endometrioid Carcinoma0:LIVINGNaN0:ALIVE OR DEAD TUMOR FREE45.00.690369.00.33440.55Black or African AmericanUCEC_CN_HIGHEndometrioid Endometrial Adenocarcinoma
3TCGA-5S-A9Q8TCGA-5S-A9Q8-01Uterine Endometrioid Carcinoma0:LIVING0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE50.00.058151.00.31990.09Black or African AmericanUCEC_CN_LOWEndometrioid Endometrial Adenocarcinoma
4TCGA-A5-A0G1TCGA-A5-A0G1-01Uterine Serous Carcinoma/Uterine Papillary Ser...1:DECEASED0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE10808.00.000167.00.31081.74WhiteUCEC_POLESerous Endometrial Adenocarcinoma
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Patient IDSample IDCancer Type DetailedOverall Survival StatusDisease Free StatusDisease-specific Survival statusMutation CountFraction Genome AlteredDiagnosis AgeMSI MANTIS ScoreMSIsensor ScoreRace CategorySubtypeTumor Type
524TCGA-QS-A8F1TCGA-QS-A8F1-01Uterine Serous Carcinoma/Uterine Papillary Ser...1:DECEASEDNaN1:DEAD WITH TUMOR63.00.654985.00.36470.15Black or African AmericanUCEC_CN_HIGHSerous Endometrial Adenocarcinoma
525TCGA-SJ-A6ZITCGA-SJ-A6ZI-01Uterine Endometrioid Carcinoma0:LIVING0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE1306.00.027564.00.613814.35Black or African AmericanUCEC_MSIEndometrioid Endometrial Adenocarcinoma
526TCGA-SJ-A6ZJTCGA-SJ-A6ZJ-01Uterine Endometrioid Carcinoma0:LIVING0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE56.00.046661.00.33820.00Black or African AmericanUCEC_CN_LOWEndometrioid Endometrial Adenocarcinoma
527TCGA-SL-A6J9TCGA-SL-A6J9-01Uterine Endometrioid Carcinoma0:LIVINGNaN0:ALIVE OR DEAD TUMOR FREE48.00.422673.00.35030.03Black or African AmericanUCEC_CN_HIGHEndometrioid Endometrial Adenocarcinoma
528TCGA-SL-A6JATCGA-SL-A6JA-01Uterine Endometrioid Carcinoma0:LIVING0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE742.00.048877.00.951526.10Black or African AmericanUCEC_MSIEndometrioid Endometrial Adenocarcinoma
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" - ], - "text/plain": [ - " Patient ID Sample ID \\\n", - "524 TCGA-QS-A8F1 TCGA-QS-A8F1-01 \n", - "525 TCGA-SJ-A6ZI TCGA-SJ-A6ZI-01 \n", - "526 TCGA-SJ-A6ZJ TCGA-SJ-A6ZJ-01 \n", - "527 TCGA-SL-A6J9 TCGA-SL-A6J9-01 \n", - "528 TCGA-SL-A6JA TCGA-SL-A6JA-01 \n", - "\n", - " Cancer Type Detailed \\\n", - "524 Uterine Serous Carcinoma/Uterine Papillary Ser... \n", - "525 Uterine Endometrioid Carcinoma \n", - "526 Uterine Endometrioid Carcinoma \n", - "527 Uterine Endometrioid Carcinoma \n", - "528 Uterine Endometrioid Carcinoma \n", - "\n", - " Overall Survival Status Disease Free Status \\\n", - "524 1:DECEASED NaN \n", - "525 0:LIVING 0:DiseaseFree \n", - "526 0:LIVING 0:DiseaseFree \n", - "527 0:LIVING NaN \n", - "528 0:LIVING 0:DiseaseFree \n", - "\n", - " Disease-specific Survival status Mutation Count Fraction Genome Altered \\\n", - "524 1:DEAD WITH TUMOR 63.0 0.6549 \n", - "525 0:ALIVE OR DEAD TUMOR FREE 1306.0 0.0275 \n", - "526 0:ALIVE OR DEAD TUMOR FREE 56.0 0.0466 \n", - "527 0:ALIVE OR DEAD TUMOR FREE 48.0 0.4226 \n", - "528 0:ALIVE OR DEAD TUMOR FREE 742.0 0.0488 \n", - "\n", - " Diagnosis Age MSI MANTIS Score MSIsensor Score \\\n", - "524 85.0 0.3647 0.15 \n", - "525 64.0 0.6138 14.35 \n", - "526 61.0 0.3382 0.00 \n", - "527 73.0 0.3503 0.03 \n", - "528 77.0 0.9515 26.10 \n", - "\n", - " Race Category Subtype \\\n", - "524 Black or African American UCEC_CN_HIGH \n", - "525 Black or African American UCEC_MSI \n", - "526 Black or African American UCEC_CN_LOW \n", - "527 Black or African American UCEC_CN_HIGH \n", - "528 Black or African American UCEC_MSI \n", - "\n", - " Tumor Type \n", - "524 Serous Endometrial Adenocarcinoma \n", - "525 Endometrioid Endometrial Adenocarcinoma \n", - "526 Endometrioid Endometrial Adenocarcinoma \n", - "527 Endometrioid Endometrial Adenocarcinoma \n", - "528 Endometrioid Endometrial Adenocarcinoma " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Show the last 5 rows of the dataset\n", - "\n", - "data.tail()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "9a7689f4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Patient ID\n", - "Sample ID\n", - "Cancer Type Detailed\n", - "Overall Survival Status\n", - "Disease Free Status\n", - "Disease-specific Survival status\n", - "Mutation Count\n", - "Fraction Genome Altered\n", - "Diagnosis Age\n", - "MSI MANTIS Score\n", - "MSIsensor Score\n", - "Race Category\n", - "Subtype\n", - "Tumor Type\n" - ] - } - ], - "source": [ - "\n", - "# List of column names\n", - "\n", - "for col in list(data.columns):\n", - " print(col)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "18eb88f2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of rows: 529\n", - "Number of columns: 14\n" - ] - } - ], - "source": [ - "#gives the size/shape of the dataset\n", - "#How many columns and rows are there in the dataset.\n", - "\n", - "num_rows, num_columns = data.shape\n", - "\n", - "print(\"Number of rows:\", num_rows)\n", - "print(\"Number of columns:\", num_columns)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "21946a8b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Patient ID object\n", - "Sample ID object\n", - "Cancer Type Detailed object\n", - "Overall Survival Status object\n", - "Disease Free Status object\n", - "Disease-specific Survival status object\n", - "Mutation Count float64\n", - "Fraction Genome Altered float64\n", - "Diagnosis Age float64\n", - "MSI MANTIS Score float64\n", - "MSIsensor Score float64\n", - "Race Category object\n", - "Subtype object\n", - "Tumor Type object\n", - "dtype: object" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#Check the data type of dataframe pcos\n", - "data.dtypes" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "77be34a4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 529 entries, 0 to 528\n", - "Data columns (total 14 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Patient ID 529 non-null object \n", - " 1 Sample ID 529 non-null object \n", - " 2 Cancer Type Detailed 529 non-null object \n", - " 3 Overall Survival Status 529 non-null object \n", - " 4 Disease Free Status 414 non-null object \n", - " 5 Disease-specific Survival status 527 non-null object \n", - " 6 Mutation Count 515 non-null float64\n", - " 7 Fraction Genome Altered 519 non-null float64\n", - " 8 Diagnosis Age 526 non-null float64\n", - " 9 MSI MANTIS Score 526 non-null float64\n", - " 10 MSIsensor Score 528 non-null float64\n", - " 11 Race Category 497 non-null object \n", - " 12 Subtype 507 non-null object \n", - " 13 Tumor Type 529 non-null object \n", - "dtypes: float64(5), object(9)\n", - "memory usage: 58.0+ KB\n" - ] - } - ], - "source": [ - "#Information about the dataset\n", - "#This method prints information about a DataFrame including the index dtype and columns, non-null values and memory usage. \n", - "\n", - "data.info()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "bfb8dc6f", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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countmeanstdmin25%50%75%max
Mutation Count515.01046.4233012734.3654947.00047.0000076.0000563.50000025696.0000
Fraction Genome Altered519.00.1859590.2260880.0000.003600.06780.3144500.9487
Diagnosis Age526.063.76616011.06003031.00057.0000064.000071.00000090.0000
MSI MANTIS Score526.00.4389480.2396660.2370.283950.31130.5432251.3049
MSIsensor Score528.06.39589010.1758530.0000.110000.435010.11750040.4300
\n", - "
" - ], - "text/plain": [ - " count mean std min 25% \\\n", - "Mutation Count 515.0 1046.423301 2734.365494 7.000 47.00000 \n", - "Fraction Genome Altered 519.0 0.185959 0.226088 0.000 0.00360 \n", - "Diagnosis Age 526.0 63.766160 11.060030 31.000 57.00000 \n", - "MSI MANTIS Score 526.0 0.438948 0.239666 0.237 0.28395 \n", - "MSIsensor Score 528.0 6.395890 10.175853 0.000 0.11000 \n", - "\n", - " 50% 75% max \n", - "Mutation Count 76.0000 563.500000 25696.0000 \n", - "Fraction Genome Altered 0.0678 0.314450 0.9487 \n", - "Diagnosis Age 64.0000 71.000000 90.0000 \n", - "MSI MANTIS Score 0.3113 0.543225 1.3049 \n", - "MSIsensor Score 0.4350 10.117500 40.4300 " - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "#If we would like to get a statistical summary of each column, such as count, column mean value, column standard deviation, etc.\n", - "#We use the describe method:\n", - "\n", - "data.describe().T" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "b931888d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Patient ID 0\n", - "Sample ID 0\n", - "Cancer Type Detailed 0\n", - "Overall Survival Status 0\n", - "Disease Free Status 115\n", - "Disease-specific Survival status 2\n", - "Mutation Count 14\n", - "Fraction Genome Altered 10\n", - "Diagnosis Age 3\n", - "MSI MANTIS Score 3\n", - "MSIsensor Score 1\n", - "Race Category 32\n", - "Subtype 22\n", - "Tumor Type 0\n", - "dtype: int64" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check the null values present in dataset \n", - "data.isnull().sum()" - ] - }, - { - "cell_type": "markdown", - "id": "0822b004", - "metadata": {}, - "source": [ - "# matplotlib.pyplot, seaborn, and scipy.stats libraries are used for data visualization and statistical analysis. These are commonly used libraries in Python for such tasks.\n", - "\n", - "matplotlib.pyplot is a plotting library that provides a MATLAB-like interface for creating visualizations in Python.\n", - "\n", - "seaborn is a statistical data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.\n", - "\n", - "scipy.stats contains a large number of probability distributions and statistical functions. It's useful for statistical analysis and hypothesis testing.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "63b47d52", - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "import scipy.stats as stats" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "ceb4c2c2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.histplot(data['Diagnosis Age']) # histogram plot for Age-count" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "4a00d3d5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.boxplot(data['Diagnosis Age']) # boxplot for Age" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "3a3ccbc3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.histplot(data['Mutation Count']) # histogram plot for Mutation Count" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "85226ea8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.boxplot(data['Mutation Count']) # boxplot for Mutation Count" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "620f6584", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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+Z6e4uJgJwAAAoFrwKOw89NBDWr58eUXXAgAAUOE8eox1+vRpvfzyy1q7dq1uvPFGBQQEuG2fPXt2hRQHAABwtTwKO19//bXatWsnSdq5c6fbNk8nKwMAAFQGj8LOhg0bKroOAACASuHRnB0AAIDqwqM7Oz179rzk46r169d7XBAAAEBF8ijsnJ+vc96ZM2e0Y8cO7dy5s9QXhAIAAHiTR2Fnzpw5ZbYnJSWpoKDgqgoCAACoSBU6Z+f++++/4u/FAgAAqAoVGnY+/fRT1axZsyIPCQAAcFU8eow1ePBgt3VjjLKysrR9+3b97W9/q5DCAAAAKoJHYScsLMxtvUaNGmrWrJmeffZZJSYmVkhhAAAAFcGjsJOamlrRdQAAAFQKj8LOeRkZGcrMzJTD4VDLli110003VVRdAAAAFcKjsJOTk6N7771XGzdu1PXXXy9jjPLy8tSzZ0+tWLFCderUqeg6AQAAPOLR21jjxo1Tfn6+du3apV9//VXHjh3Tzp07lZ+fr8cee6yiawQAAPCYR3d2PvroI61du1YtWrRwtbVs2VLz589ngjIAAPApHt3ZKSkpUUBAQKn2gIAAlZSUXHVRAAAAFcWjsNOrVy+NHz9eP/30k6vtxx9/1OOPP67bb7+9wooDAAC4Wh6FnXnz5unEiRNKSEhQo0aN1LhxYzVo0EAnTpxQSkpKRdcIAADgMY/m7MTFxemLL75QWlqa9uzZI2OMWrZsqd69e1d0fQAAAFelXHd21q9fr5YtWyo/P1+S1KdPH40bN06PPfaYbrnlFrVq1UqbN2+ulEIBAAA8Ua6wM3fuXD388MMKDQ0ttS0sLEyjRo3S7NmzK6w4AACAq1WusPPVV1/pjjvuuOj2xMREZWRkXHVRAAAAFaVcYefnn38u85Xz8/z9/fXLL79cdVEAAAAVpVxh54YbbtA333xz0e1ff/21YmJirrooAACAilKusHPnnXfqmWee0enTp0ttKyws1LRp09S/f/8KKw4AAOBqlevV87/+9a96++231bRpU40dO1bNmjWTw+FQZmam5s+fr3Pnzunpp5+urFoBAADKrVxhJyoqSunp6XrkkUc0depUGWMkSQ6HQ3379tWCBQsUFRVVKYUCAAB4otwfKhgfH69Vq1bp2LFj+v7772WMUZMmTVS7du3KqA8AAOCqePQJypJUu3Zt3XLLLRVZCwAAQIXz6LuxAAAAqgvCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNW8GnY+/vhjDRgwQLGxsXI4HHr33XfdthtjlJSUpNjYWAUFBalHjx7atWuXW5+ioiKNGzdOkZGRCg4O1sCBA3XkyJEqvAoAAODLvBp2Tp48qbZt22revHllbp85c6Zmz56tefPmadu2bYqOjlafPn104sQJV58JEybonXfe0YoVK7RlyxYVFBSof//+OnfuXFVdBgAA8GH+3jx5v3791K9fvzK3GWM0d+5cPf300xo8eLAk6dVXX1VUVJSWL1+uUaNGKS8vT4sWLdLSpUvVu3dvSdLrr7+uuLg4rV27Vn379q2yawEAAL7JZ+fsHDhwQNnZ2UpMTHS1OZ1Ode/eXenp6ZKkjIwMnTlzxq1PbGysWrdu7epTlqKiIuXn57stAADATj4bdrKzsyVJUVFRbu1RUVGubdnZ2QoMDFTt2rUv2qcsycnJCgsLcy1xcXEVXD0AAPAVPht2znM4HG7rxphSbRe6XJ+pU6cqLy/PtRw+fLhCagUAAL7HZ8NOdHS0JJW6Q5OTk+O62xMdHa3i4mIdO3bson3K4nQ6FRoa6rYAAAA7+WzYadCggaKjo5WWluZqKy4u1qZNm9SlSxdJUvv27RUQEODWJysrSzt37nT1AQAA1zavvo1VUFCg77//3rV+4MAB7dixQ+Hh4apfv74mTJigGTNmqEmTJmrSpIlmzJihWrVqadiwYZKksLAwjRw5UpMmTVJERITCw8M1efJktWnTxvV2FgAAuLZ5Nexs375dPXv2dK1PnDhRkjR8+HAtWbJEU6ZMUWFhocaMGaNjx46pY8eOWrNmjUJCQlz7zJkzR/7+/hoyZIgKCwt1++23a8mSJfLz86vy6wEAAL7Hq2GnR48eMsZcdLvD4VBSUpKSkpIu2qdmzZpKSUlRSkpKJVQIAACqO5+dswMAAFARCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAAACrEXYAAIDVCDsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFbz93YBqFqZmZneLkGSFBkZqfr163u7DADANYCwc40ozDsqyaH777/f26VIkoKCamnPnkwCDwCg0vl02ElKStL06dPd2qKiopSdnS1JMsZo+vTpevnll3Xs2DF17NhR8+fPV6tWrbxRrk87c+qEJKN2w/6iOg2ae7WW/KyD+mzxdOXm5hJ2AACVzqfDjiS1atVKa9euda37+fm5fp45c6Zmz56tJUuWqGnTpnr++efVp08fffvttwoJCfFGuT7vurr1FV6/mbfLAACgyvh82PH391d0dHSpdmOM5s6dq6efflqDBw+WJL366quKiorS8uXLNWrUqIses6ioSEVFRa71/Pz8ii8cAAD4BJ9/G2vv3r2KjY1VgwYNdO+992r//v2SpAMHDig7O1uJiYmuvk6nU927d1d6evolj5mcnKywsDDXEhcXV6nXAAAAvMenw07Hjh312muvafXq1XrllVeUnZ2tLl266OjRo655O1FRUW77/H5Oz8VMnTpVeXl5ruXw4cOVdg0AAMC7fPoxVr9+/Vw/t2nTRp07d1ajRo306quvqlOnTpIkh8Phto8xplTbhZxOp5xOZ8UXDAAAfI5P39m5UHBwsNq0aaO9e/e65vFceBcnJyen1N0eAABw7apWYaeoqEiZmZmKiYlRgwYNFB0drbS0NNf24uJibdq0SV26dPFilQAAwJf49GOsyZMna8CAAapfv75ycnL0/PPPKz8/X8OHD5fD4dCECRM0Y8YMNWnSRE2aNNGMGTNUq1YtDRs2zNulAwAAH+HTYefIkSO67777lJubqzp16qhTp07aunWr4uPjJUlTpkxRYWGhxowZ4/pQwTVr1vAZOwAAwMWnw86KFSsuud3hcCgpKUlJSUlVUxAAAKh2qtWcHQAAgPIi7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAaj79RaCwW2ZmprdLkCRFRkaqfv363i4DAFBJCDuocoV5RyU5dP/993u7FElSUFAt7dmTSeABAEsRdlDlzpw6Icmo3bC/qE6D5l6tJT/roD5bPF25ubmEHQCwFGEHXnNd3foKr9/M22UAACxH2AHkG/OHmDsEAJWDsINrmi/NH2LuEABUDsIOrmm+Mn+IuUMAUHkIO4CYPwQANuNDBQEAgNUIOwAAwGqEHQAAYDXCDgAAsBoTlAEf4guf9yPxmT8A7ELYAXyAL33ej8Rn/gCwC2EH8AG+8nk/0v//zJ/NmzerRYsWXq1F4i4TgKtH2AF8iC983g93mQDYhrADwI0v3mXik6XdHTp0SLm5ud4uQxJ33lA9EHYAlMkX7jKd5wsTt33lj/qhQ4fUvHkLFRae8nYpkrjzhuqBsAPAZ/nSIzWns6b+53/eUkxMjFfryMzMVGHhKXV8cJpCYxK8Wgt33lBdEHYA+CxfeaT2y96vtOPNf6h///5eq+FCQeGxPnPnDfB1hB0APs/bj9Tysw7KF0KXJGV986l2vv+yzp4969U6gOqEsAMAV8jboUs6H7wAlAdfFwEAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsBphBwAAWI2wAwAArEbYAQAAViPsAAAAqxF2AACA1Qg7AADAanzrOQDgqmRmZnq7BEVGRqp+/freLgM+irADAPBIYd5RSQ7df//93i5FQUG1tGdPJoEHZSLsAAA8cubUCUlG7Yb9RXUaNPdaHflZB/XZ4unKzc0l7KBMhB0AwFW5rm59hddv5u0ygIsi7AAArOALc4ck5g/5IsIOAKBa86W5QxLzh3wRYQcAUK35ytwhyffmDx06dEi5ubneLsPrd7sIOwAAKzB3yN2hQ4fUvHkLFRae8nYpXr/bRdgBAKCC+cL8oczMTBUWnlLHB6cpNCbBa3X4wt0ua8LOggUL9Pe//11ZWVlq1aqV5s6dqz/84Q/eLgsAcA3xtflDkhQUHnvN3/GyIuysXLlSEyZM0IIFC9S1a1f961//Ur9+/bR7926feGYKALg2+NL8oaxvPtXO91/W2bNnvVqHL7Ai7MyePVsjR47UQw89JEmaO3euVq9erYULFyo5OdnL1QEArjW+MH8oP+ugV8/vS6p92CkuLlZGRoaefPJJt/bExESlp6eXuU9RUZGKiopc63l5eZKk/Pz8Cq2toKBAkvTrD9/qbFFhhR67vPKzfpAk5f24VwH+DmrxsVp8pQ5q8e06qMW366CWi9SRfUjSb38TK/rv7PnjGWMu3dFUcz/++KORZD755BO39v/6r/8yTZs2LXOfadOmGUksLCwsLCwsFiyHDx++ZFao9nd2znM43FOrMaZU23lTp07VxIkTXeslJSX69ddfFRERcdF9PJGfn6+4uDgdPnxYoaGhFXZcXBnG33sYe+9h7L2Hsa96xhidOHFCsbGxl+xX7cNOZGSk/Pz8lJ2d7daek5OjqKioMvdxOp1yOp1ubddff31llajQ0FB+8b2I8fcext57GHvvYeyrVlhY2GX71KiCOipVYGCg2rdvr7S0NLf2tLQ0denSxUtVAQAAX1Ht7+xI0sSJE/XAAw+oQ4cO6ty5s15++WUdOnRIo0eP9nZpAADAy6wIO0OHDtXRo0f17LPPKisrS61bt9aqVasUHx/v1bqcTqemTZtW6pEZqgbj7z2Mvfcw9t7D2PsuhzGXe18LAACg+qr2c3YAAAAuhbADAACsRtgBAABWI+wAAACrEXau0oIFC9SgQQPVrFlT7du31+bNmy/Zf9OmTWrfvr1q1qyphg0b6qWXXqqiSu1TnrF/++231adPH9WpU0ehoaHq3LmzVq9eXYXV2qW8v/fnffLJJ/L391e7du0qt0CLlXfsi4qK9PTTTys+Pl5Op1ONGjXS4sWLq6hau5R37JctW6a2bduqVq1aiomJ0X/8x3/o6NGjVVQt3FTIF1Rdo1asWGECAgLMK6+8Ynbv3m3Gjx9vgoODzQ8//FBm//3795tatWqZ8ePHm927d5tXXnnFBAQEmLfeequKK6/+yjv248ePNy+++KL5/PPPzXfffWemTp1qAgICzBdffFHFlVd/5R37844fP24aNmxoEhMTTdu2baumWMt4MvYDBw40HTt2NGlpaebAgQPms88+K/Vdgri88o795s2bTY0aNcw//vEPs3//frN582bTqlUrM2jQoCquHMYYQ9i5CrfeeqsZPXq0W1vz5s3Nk08+WWb/KVOmmObNm7u1jRo1ynTq1KnSarRVece+LC1btjTTp0+v6NKs5+nYDx061Pz1r38106ZNI+x4qLxj/+GHH5qwsDBz9OjRqijPauUd+7///e+mYcOGbm3//Oc/Tb169SqtRlwcj7E8VFxcrIyMDCUmJrq1JyYmKj09vcx9Pv3001L9+/btq+3bt+vMmTOVVqttPBn7C5WUlOjEiRMKDw+vjBKt5enYp6amat++fZo2bVpll2gtT8b+/fffV4cOHTRz5kzdcMMNatq0qSZPnqzCwsKqKNkanox9ly5ddOTIEa1atUrGGP38889666239G//9m9VUTIuYMUnKHtDbm6uzp07V+rLRqOiokp9Kel52dnZZfY/e/ascnNzFRMTU2n12sSTsb/QrFmzdPLkSQ0ZMqQySrSWJ2O/d+9ePfnkk9q8ebP8/fm/HE95Mvb79+/Xli1bVLNmTb3zzjvKzc3VmDFj9OuvvzJvpxw8GfsuXbpo2bJlGjp0qE6fPq2zZ89q4MCBSklJqYqScQHu7Fwlh8Phtm6MKdV2uf5ltePyyjv2573xxhtKSkrSypUrVbdu3coqz2pXOvbnzp3TsGHDNH36dDVt2rSqyrNaeX7vS0pK5HA4tGzZMt1666268847NXv2bC1ZsoS7Ox4oz9jv3r1bjz32mJ555hllZGToo48+0oEDB/jORi/hP7M8FBkZKT8/v1KpPicnp1T6Py86OrrM/v7+/oqIiKi0Wm3jydift3LlSo0cOVL//d//rd69e1dmmVYq79ifOHFC27dv15dffqmxY8dK+u0PsDFG/v7+WrNmjXr16lUltVd3nvzex8TE6IYbblBYWJirrUWLFjLG6MiRI2rSpEml1mwLT8Y+OTlZXbt21RNPPCFJuvHGGxUcHKw//OEPev7557mTX8W4s+OhwMBAtW/fXmlpaW7taWlp6tKlS5n7dO7cuVT/NWvWqEOHDgoICKi0Wm3jydhLv93RGTFihJYvX85zcw+Vd+xDQ0P1zTffaMeOHa5l9OjRatasmXbs2KGOHTtWVenVnie/9127dtVPP/2kgoICV9t3332nGjVqqF69epVar008GftTp06pRg33P7F+fn6S/v8dfVQhb82MtsH5VxEXLVpkdu/ebSZMmGCCg4PNwYMHjTHGPPnkk+aBBx5w9T//6vnjjz9udu/ebRYtWsSr5x4q79gvX77c+Pv7m/nz55usrCzXcvz4cW9dQrVV3rG/EG9jea68Y3/ixAlTr14986c//cns2rXLbNq0yTRp0sQ89NBD3rqEaqu8Y5+ammr8/f3NggULzL59+8yWLVtMhw4dzK233uqtS7imEXau0vz58018fLwJDAw0N998s9m0aZNr2/Dhw0337t3d+m/cuNHcdNNNJjAw0CQkJJiFCxdWccX2KM/Yd+/e3UgqtQwfPrzqC7dAeX/vf4+wc3XKO/aZmZmmd+/eJigoyNSrV89MnDjRnDp1qoqrtkN5x/6f//ynadmypQkKCjIxMTHmz3/+szly5EgVVw1jjHEYw/00AABgL+bsAAAAqxF2AACA1Qg7AADAaoQdAABgNcIOAACwGmEHAABYjbADAACsRtgBAABWI+wAKGXjxo1yOBw6fvy4t0u5pi1ZskTXX3+9t8twk5SUpHbt2nm7DKBcCDuADxkxYoQcDkep5fvvv6+0c/bo0UMTJkxwa+vSpYuysrLcvi27smRnZ2v8+PFq3LixatasqaioKHXr1k0vvfSSTp06Venn97bly5fLz89Po0ePvmxfggbgGX9vFwDA3R133KHU1FS3tjp16pTqV1xcrMDAwEqpITAwUNHR0ZVy7N/bv3+/unbtquuvv14zZsxQmzZtdPbsWX333XdavHixYmNjNXDgwEqvw5sWL16sKVOmaOHChZo9e7Zq1apV6ec8c+aMAgICKv08gK/gzg7gY5xOp6Kjo90WPz8/9ejRQ2PHjtXEiRMVGRmpPn36SJJmz56tNm3aKDg4WHFxcRozZowKCgrcjvnJJ5+oe/fuqlWrlmrXrq2+ffvq2LFjGjFihDZt2qR//OMfrrtIBw8eLPMx1v/8z/+oVatWcjqdSkhI0KxZs9zOkZCQoBkzZujBBx9USEiI6tevr5dffvmS1zpmzBj5+/tr+/btGjJkiFq0aKE2bdro7rvv1gcffKABAwa4+ubl5ek///M/VbduXYWGhqpXr1766quvXNvP3/VYunSpEhISFBYWpnvvvVcnTpxw9SkqKtJjjz2munXrqmbNmurWrZu2bdvm2n7+ulevXq2bbrpJQUFB6tWrl3JycvThhx+qRYsWCg0N1X333ed218kYo5kzZ6phw4YKCgpS27Zt9dZbb1323/rgwYNKT0/Xk08+qebNm19ynyVLlmj69On66quvXP9WS5YsKdfYLF68WA0bNpTT6ZQx5rL7SdILL7ygqKgohYSEaOTIkTp9+vRlrwvwOd79HlIAvzd8+HDzxz/+scxt3bt3N9ddd5154oknzJ49e0xmZqYxxpg5c+aY9evXm/3795t169aZZs2amUceecS135dffmmcTqd55JFHzI4dO8zOnTtNSkqK+eWXX8zx48dN586dzcMPP2yysrJMVlaWOXv2rNmwYYORZI4dO2aMMWb79u2mRo0a5tlnnzXffvutSU1NNUFBQSY1NdV1nvj4eBMeHm7mz59v9u7da5KTk02NGjVcdV4oNzfXOBwOk5ycfNlxKSkpMV27djUDBgww27ZtM999952ZNGmSiYiIMEePHjXG/PZt6tddd50ZPHiw+eabb8zHH39soqOjzVNPPeU6zmOPPWZiY2PNqlWrzK5du8zw4cNN7dq1Xcc4f92dOnUyW7ZsMV988YVp3Lix6d69u0lMTDRffPGF+fjjj01ERIR54YUXXMd96qmnTPPmzc1HH31k9u3bZ1JTU43T6TQbN2685HX97W9/M3/605+MMcakpKSY2267zW17amqqCQsLM8YYc+rUKTNp0iTTqlUr17/VqVOnrnhsgoODTd++fc0XX3xhvvrqqyvab+XKlSYwMNC88sorZs+ePebpp582ISEhfGs9qh3CDuBDhg8fbvz8/ExwcLBrOf/HsHv37qZdu3aXPcabb75pIiIiXOv33Xef6dq160X7d+/e3YwfP96t7cKwM2zYMNOnTx+3Pk888YRp2bKlaz0+Pt7cf//9rvWSkhJTt25ds3DhwjLPu3XrViPJvP32227tERERrmufMmWKMcaYdevWmdDQUHP69Gm3vo0aNTL/+te/jDG//UGvVauWyc/Pd6uxY8eOxhhjCgoKTEBAgFm2bJlre3FxsYmNjTUzZ850u+61a9e6+iQnJxtJZt++fa62UaNGmb59+7qOW7NmTZOenu5W28iRI819991X5rUbY8y5c+dMXFyceffdd40xxvzyyy8mICDA7N2719Xn92Hn/DVeGDSudGwCAgJMTk5Oufbr3LmzGT16tNv2jh07EnZQ7TBnB/AxPXv21MKFC13rwcHBrp87dOhQqv+GDRs0Y8YM7d69W/n5+Tp79qxOnz6tkydPKjg4WDt27NA999xzVTVlZmbqj3/8o1tb165dNXfuXJ07d05+fn6SpBtvvNG13eFwKDo6Wjk5OZc8tsPhcFv//PPPVVJSoj//+c8qKiqSJGVkZKigoEARERFufQsLC7Vv3z7XekJCgkJCQlzrMTExrvPv27dPZ86cUdeuXV3bAwICdOuttyozM9PtuL+/jqioKNWqVUsNGzZ0a/v8888lSbt379bp06ddjxXPKy4u1k033XTR616zZo1Onjypfv36SZIiIyOVmJioxYsXa8aMGRfd70JXOjbx8fFuc7+uZL/MzMxSE6c7d+6sDRs2XHF9gC8g7AA+Jjg4WI0bN77ott/74YcfdOedd2r06NF67rnnFB4eri1btmjkyJE6c+aMJCkoKOiqazLGlAolxphS/S6c9OpwOFRSUlLmMRs3biyHw6E9e/a4tZ8PFb+vu6SkRDExMdq4cWOp4/z+1exLnf98vWVdx4Vtvz+Ow+G45HHP/+8HH3ygG264wa2f0+ksVe95ixcv1q+//uo2IbmkpERffvmlnnvuOVeAvJwrHZsLf3eudD/ABoQdoBrbvn27zp49q1mzZqlGjd/eN3jzzTfd+tx4441at26dpk+fXuYxAgMDde7cuUuep2XLltqyZYtbW3p6upo2bXrFf5QvFBERoT59+mjevHkaN25cqT/Gv3fzzTcrOztb/v7+SkhI8Oh8jRs3VmBgoLZs2aJhw4ZJ+u2tpO3bt5d69b48WrZsKafTqUOHDql79+5XtM/Ro0f13nvvacWKFWrVqpWrvaSkRH/4wx/04Ycfqn///qX2K+vfytOxuZL9WrRooa1bt+rf//3fXW1bt2694nMAvoKwA1RjjRo10tmzZ5WSkqIBAwbok08+0UsvveTWZ+rUqWrTpo3GjBmj0aNHKzAwUBs2bNA999yjyMhIJSQk6LPPPtPBgwd13XXXKTw8vNR5Jk2apFtuuUXPPfechg4dqk8//VTz5s3TggULrqr+BQsWqGvXrurQoYOSkpJ04403qkaNGtq2bZv27Nmj9u3bS5J69+6tzp07a9CgQXrxxRfVrFkz/fTTT1q1apUGDRpU5uO9CwUHB+uRRx7RE088ofDwcNWvX18zZ87UqVOnNHLkSI+vISQkRJMnT9bjjz+ukpISdevWTfn5+UpPT9d1112n4cOHl9pn6dKlioiI0D333OMKqef1799fixYtKjPsJCQk6MCBA9qxY4fq1aunkJAQj8fmSvYbP368hg8frg4dOqhbt25atmyZdu3a5fZID6gOePUcqMbatWun2bNn68UXX1Tr1q21bNkyJScnu/Vp2rSp1qxZo6+++kq33nqrOnfurPfee0/+/r/9t87kyZPl5+enli1bqk6dOjp06FCp89x888168803tWLFCrVu3VrPPPOMnn32WY0YMeKq6m/UqJG+/PJL9e7dW1OnTlXbtm3VoUMHpaSkaPLkyXruueck/fbYaNWqVbrtttv04IMPqmnTprr33nt18OBBRUVFXfH5XnjhBd1999164IEHdPPNN+v777/X6tWrVbt27au6jueee07PPPOMkpOT1aJFC/Xt21f/+7//qwYNGpTZf/HixbrrrrtKBR1Juvvuu/V///d/+vnnn8vcdscdd6hnz56qU6eO3njjDY/H5kr2Gzp0qJ555hn95S9/Ufv27fXDDz/okUce8XCUAO9xmLIevAMAAFiCOzsAAMBqhB0AAGA1wg4AALAaYQcAAFiNsAMAAKxG2AEAAFYj7AAAAKsRdgAAgNUIOwAAwGqEHQAAYDXCDgAAsNr/Awq2/tXIZ/j2AAAAAElFTkSuQmCC", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.histplot(data['MSI MANTIS Score']) # histogram plot for MSI MANTIS Score " - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "2b359cab", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.boxplot(data['MSI MANTIS Score']) # boxplot for MSI MANTIS Score" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "2b103601", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.histplot(data['MSIsensor Score']) # histogram plot for MSIsensor Score" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "46fe3b0c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.boxplot(data['MSIsensor Score']) # boxplot for MSIsensor Score" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From a8d72f85798adc92d0f416e8a905bbda2f2924a8 Mon Sep 17 00:00:00 2001 From: RUTIKA WAGALEKAR <120473716+RutikaW1155@users.noreply.github.com> Date: Sun, 19 May 2024 13:18:21 +0530 Subject: [PATCH 09/12] Add files via upload --- ...ndometrial Cancer Prediction Dataset.ipynb | 1629 +++++++++++++++++ 1 file changed, 1629 insertions(+) create mode 100644 Endometriral Cancer Prediction/Endometrial Cancer Prediction Dataset.ipynb diff --git a/Endometriral Cancer Prediction/Endometrial Cancer Prediction Dataset.ipynb b/Endometriral Cancer Prediction/Endometrial Cancer Prediction Dataset.ipynb new file mode 100644 index 00000000..20b1700c --- /dev/null +++ b/Endometriral Cancer Prediction/Endometrial Cancer Prediction Dataset.ipynb @@ -0,0 +1,1629 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8ea6b3f8", + "metadata": {}, + "source": [ + "# Endometrial Cancer Prediction Dataset\n", + "\n", + "This dataset contains information about endometrial cancer, also known as endometrial carcinoma, which is a type of cancer that starts in the cells of the inner lining of the uterus (the endometrium). Endometrial carcinomas can be categorized into different types based on cellular characteristics observed under a microscope." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b9b66e04", + "metadata": {}, + "outputs": [], + "source": [ + "#importing libraries\n", + "\n", + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "id": "62600d10", + "metadata": {}, + "source": [ + "# Load the dataset into Jupyter Notebook\n", + "\n", + "There are various formats for a dataset, .csv, .json, .xlsx etc. The dataset can be stored in different places, on your local machine or sometimes online. In our case, the PCOS Dataset is an online source, and it is in CSV (comma separated value) format.\n", + "\n", + "dataset name : Endometriral Cancer Prediction Dataset_data.csv\n", + "\n", + "The Pandas Library is a useful tool that enables us to read various datasets into a data frame; our Jupyter notebook platforms have a built-in Pandas Library so that all we need to do is import Pandas without installing." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "82c0c766", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Patient IDSample IDCancer Type DetailedOverall Survival StatusDisease Free StatusDisease-specific Survival statusMutation CountFraction Genome AlteredDiagnosis AgeMSI MANTIS ScoreMSIsensor ScoreRace CategorySubtypeTumor Type
0TCGA-2E-A9G8TCGA-2E-A9G8-01Uterine Endometrioid Carcinoma0:LIVINGNaN0:ALIVE OR DEAD TUMOR FREE65.00.331159.00.32340.85Black or African AmericanUCEC_CN_HIGHEndometrioid Endometrial Adenocarcinoma
1TCGA-4E-A92ETCGA-4E-A92E-01Uterine Endometrioid Carcinoma0:LIVING0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE147.00.034154.00.33960.01Black or African AmericanUCEC_CN_LOWEndometrioid Endometrial Adenocarcinoma
2TCGA-5B-A90CTCGA-5B-A90C-01Uterine Endometrioid Carcinoma0:LIVINGNaN0:ALIVE OR DEAD TUMOR FREE45.00.690369.00.33440.55Black or African AmericanUCEC_CN_HIGHEndometrioid Endometrial Adenocarcinoma
3TCGA-5S-A9Q8TCGA-5S-A9Q8-01Uterine Endometrioid Carcinoma0:LIVING0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE50.00.058151.00.31990.09Black or African AmericanUCEC_CN_LOWEndometrioid Endometrial Adenocarcinoma
4TCGA-A5-A0G1TCGA-A5-A0G1-01Uterine Serous Carcinoma/Uterine Papillary Ser...1:DECEASED0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE10808.00.000167.00.31081.74WhiteUCEC_POLESerous Endometrial Adenocarcinoma
.............................................
524TCGA-QS-A8F1TCGA-QS-A8F1-01Uterine Serous Carcinoma/Uterine Papillary Ser...1:DECEASEDNaN1:DEAD WITH TUMOR63.00.654985.00.36470.15Black or African AmericanUCEC_CN_HIGHSerous Endometrial Adenocarcinoma
525TCGA-SJ-A6ZITCGA-SJ-A6ZI-01Uterine Endometrioid Carcinoma0:LIVING0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE1306.00.027564.00.613814.35Black or African AmericanUCEC_MSIEndometrioid Endometrial Adenocarcinoma
526TCGA-SJ-A6ZJTCGA-SJ-A6ZJ-01Uterine Endometrioid Carcinoma0:LIVING0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE56.00.046661.00.33820.00Black or African AmericanUCEC_CN_LOWEndometrioid Endometrial Adenocarcinoma
527TCGA-SL-A6J9TCGA-SL-A6J9-01Uterine Endometrioid Carcinoma0:LIVINGNaN0:ALIVE OR DEAD TUMOR FREE48.00.422673.00.35030.03Black or African AmericanUCEC_CN_HIGHEndometrioid Endometrial Adenocarcinoma
528TCGA-SL-A6JATCGA-SL-A6JA-01Uterine Endometrioid Carcinoma0:LIVING0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE742.00.048877.00.951526.10Black or African AmericanUCEC_MSIEndometrioid Endometrial Adenocarcinoma
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529 rows × 14 columns

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" + ], + "text/plain": [ + " Patient ID Sample ID \\\n", + "0 TCGA-2E-A9G8 TCGA-2E-A9G8-01 \n", + "1 TCGA-4E-A92E TCGA-4E-A92E-01 \n", + "2 TCGA-5B-A90C TCGA-5B-A90C-01 \n", + "3 TCGA-5S-A9Q8 TCGA-5S-A9Q8-01 \n", + "4 TCGA-A5-A0G1 TCGA-A5-A0G1-01 \n", + ".. ... ... \n", + "524 TCGA-QS-A8F1 TCGA-QS-A8F1-01 \n", + "525 TCGA-SJ-A6ZI TCGA-SJ-A6ZI-01 \n", + "526 TCGA-SJ-A6ZJ TCGA-SJ-A6ZJ-01 \n", + "527 TCGA-SL-A6J9 TCGA-SL-A6J9-01 \n", + "528 TCGA-SL-A6JA TCGA-SL-A6JA-01 \n", + "\n", + " Cancer Type Detailed \\\n", + "0 Uterine Endometrioid Carcinoma \n", + "1 Uterine Endometrioid Carcinoma \n", + "2 Uterine Endometrioid Carcinoma \n", + "3 Uterine Endometrioid Carcinoma \n", + "4 Uterine Serous Carcinoma/Uterine Papillary Ser... \n", + ".. ... \n", + "524 Uterine Serous Carcinoma/Uterine Papillary Ser... \n", + "525 Uterine Endometrioid Carcinoma \n", + "526 Uterine Endometrioid Carcinoma \n", + "527 Uterine Endometrioid Carcinoma \n", + "528 Uterine Endometrioid Carcinoma \n", + "\n", + " Overall Survival Status Disease Free Status \\\n", + "0 0:LIVING NaN \n", + "1 0:LIVING 0:DiseaseFree \n", + "2 0:LIVING NaN \n", + "3 0:LIVING 0:DiseaseFree \n", + "4 1:DECEASED 0:DiseaseFree \n", + ".. ... ... \n", + "524 1:DECEASED NaN \n", + "525 0:LIVING 0:DiseaseFree \n", + "526 0:LIVING 0:DiseaseFree \n", + "527 0:LIVING NaN \n", + "528 0:LIVING 0:DiseaseFree \n", + "\n", + " Disease-specific Survival status Mutation Count Fraction Genome Altered \\\n", + "0 0:ALIVE OR DEAD TUMOR FREE 65.0 0.3311 \n", + "1 0:ALIVE OR DEAD TUMOR FREE 147.0 0.0341 \n", + "2 0:ALIVE OR DEAD TUMOR FREE 45.0 0.6903 \n", + "3 0:ALIVE OR DEAD TUMOR FREE 50.0 0.0581 \n", + "4 0:ALIVE OR DEAD TUMOR FREE 10808.0 0.0001 \n", + ".. ... ... ... \n", + "524 1:DEAD WITH TUMOR 63.0 0.6549 \n", + "525 0:ALIVE OR DEAD TUMOR FREE 1306.0 0.0275 \n", + "526 0:ALIVE OR DEAD TUMOR FREE 56.0 0.0466 \n", + "527 0:ALIVE OR DEAD TUMOR FREE 48.0 0.4226 \n", + "528 0:ALIVE OR DEAD TUMOR FREE 742.0 0.0488 \n", + "\n", + " Diagnosis Age MSI MANTIS Score MSIsensor Score \\\n", + "0 59.0 0.3234 0.85 \n", + "1 54.0 0.3396 0.01 \n", + "2 69.0 0.3344 0.55 \n", + "3 51.0 0.3199 0.09 \n", + "4 67.0 0.3108 1.74 \n", + ".. ... ... ... \n", + "524 85.0 0.3647 0.15 \n", + "525 64.0 0.6138 14.35 \n", + "526 61.0 0.3382 0.00 \n", + "527 73.0 0.3503 0.03 \n", + "528 77.0 0.9515 26.10 \n", + "\n", + " Race Category Subtype \\\n", + "0 Black or African American UCEC_CN_HIGH \n", + "1 Black or African American UCEC_CN_LOW \n", + "2 Black or African American UCEC_CN_HIGH \n", + "3 Black or African American UCEC_CN_LOW \n", + "4 White UCEC_POLE \n", + ".. ... ... \n", + "524 Black or African American UCEC_CN_HIGH \n", + "525 Black or African American UCEC_MSI \n", + "526 Black or African American UCEC_CN_LOW \n", + "527 Black or African American UCEC_CN_HIGH \n", + "528 Black or African American UCEC_MSI \n", + "\n", + " Tumor Type \n", + "0 Endometrioid Endometrial Adenocarcinoma \n", + "1 Endometrioid Endometrial Adenocarcinoma \n", + "2 Endometrioid Endometrial Adenocarcinoma \n", + "3 Endometrioid Endometrial Adenocarcinoma \n", + "4 Serous Endometrial Adenocarcinoma \n", + ".. ... \n", + "524 Serous Endometrial Adenocarcinoma \n", + "525 Endometrioid Endometrial Adenocarcinoma \n", + "526 Endometrioid Endometrial Adenocarcinoma \n", + "527 Endometrioid Endometrial Adenocarcinoma \n", + "528 Endometrioid Endometrial Adenocarcinoma \n", + "\n", + "[529 rows x 14 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#load the dataset\n", + "#Read the dataset in data varaible\n", + "\n", + "data = pd.read_csv(r\"D:\\PYTHON\\Uterine Corpus Endometrial Carcinoma dataset.csv\")\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "9b082085", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FEATURES:\n", + " Patient ID Sample ID \\\n", + "0 TCGA-2E-A9G8 TCGA-2E-A9G8-01 \n", + "1 TCGA-4E-A92E TCGA-4E-A92E-01 \n", + "2 TCGA-5B-A90C TCGA-5B-A90C-01 \n", + "3 TCGA-5S-A9Q8 TCGA-5S-A9Q8-01 \n", + "4 TCGA-A5-A0G1 TCGA-A5-A0G1-01 \n", + ".. ... ... \n", + "524 TCGA-QS-A8F1 TCGA-QS-A8F1-01 \n", + "525 TCGA-SJ-A6ZI TCGA-SJ-A6ZI-01 \n", + "526 TCGA-SJ-A6ZJ TCGA-SJ-A6ZJ-01 \n", + "527 TCGA-SL-A6J9 TCGA-SL-A6J9-01 \n", + "528 TCGA-SL-A6JA TCGA-SL-A6JA-01 \n", + "\n", + " Cancer Type Detailed Mutation Count \\\n", + "0 Uterine Endometrioid Carcinoma 65.0 \n", + "1 Uterine Endometrioid Carcinoma 147.0 \n", + "2 Uterine Endometrioid Carcinoma 45.0 \n", + "3 Uterine Endometrioid Carcinoma 50.0 \n", + "4 Uterine Serous Carcinoma/Uterine Papillary Ser... 10808.0 \n", + ".. ... ... \n", + "524 Uterine Serous Carcinoma/Uterine Papillary Ser... 63.0 \n", + "525 Uterine Endometrioid Carcinoma 1306.0 \n", + "526 Uterine Endometrioid Carcinoma 56.0 \n", + "527 Uterine Endometrioid Carcinoma 48.0 \n", + "528 Uterine Endometrioid Carcinoma 742.0 \n", + "\n", + " Fraction Genome Altered Diagnosis Age MSI MANTIS Score \\\n", + "0 0.3311 59.0 0.3234 \n", + "1 0.0341 54.0 0.3396 \n", + "2 0.6903 69.0 0.3344 \n", + "3 0.0581 51.0 0.3199 \n", + "4 0.0001 67.0 0.3108 \n", + ".. ... ... ... \n", + "524 0.6549 85.0 0.3647 \n", + "525 0.0275 64.0 0.6138 \n", + "526 0.0466 61.0 0.3382 \n", + "527 0.4226 73.0 0.3503 \n", + "528 0.0488 77.0 0.9515 \n", + "\n", + " MSIsensor Score Race Category Subtype \\\n", + "0 0.85 Black or African American UCEC_CN_HIGH \n", + "1 0.01 Black or African American UCEC_CN_LOW \n", + "2 0.55 Black or African American UCEC_CN_HIGH \n", + "3 0.09 Black or African American UCEC_CN_LOW \n", + "4 1.74 White UCEC_POLE \n", + ".. ... ... ... \n", + "524 0.15 Black or African American UCEC_CN_HIGH \n", + "525 14.35 Black or African American UCEC_MSI \n", + "526 0.00 Black or African American UCEC_CN_LOW \n", + "527 0.03 Black or African American UCEC_CN_HIGH \n", + "528 26.10 Black or African American UCEC_MSI \n", + "\n", + " Tumor Type \n", + "0 Endometrioid Endometrial Adenocarcinoma \n", + "1 Endometrioid Endometrial Adenocarcinoma \n", + "2 Endometrioid Endometrial Adenocarcinoma \n", + "3 Endometrioid Endometrial Adenocarcinoma \n", + "4 Serous Endometrial Adenocarcinoma \n", + ".. ... \n", + "524 Serous Endometrial Adenocarcinoma \n", + "525 Endometrioid Endometrial Adenocarcinoma \n", + "526 Endometrioid Endometrial Adenocarcinoma \n", + "527 Endometrioid Endometrial Adenocarcinoma \n", + "528 Endometrioid Endometrial Adenocarcinoma \n", + "\n", + "[529 rows x 11 columns]\n", + "LABELS:\n", + " Overall Survival Status Disease Free Status \\\n", + "0 0:LIVING NaN \n", + "1 0:LIVING 0:DiseaseFree \n", + "2 0:LIVING NaN \n", + "3 0:LIVING 0:DiseaseFree \n", + "4 1:DECEASED 0:DiseaseFree \n", + ".. ... ... \n", + "524 1:DECEASED NaN \n", + "525 0:LIVING 0:DiseaseFree \n", + "526 0:LIVING 0:DiseaseFree \n", + "527 0:LIVING NaN \n", + "528 0:LIVING 0:DiseaseFree \n", + "\n", + " Disease-specific Survival status \n", + "0 0:ALIVE OR DEAD TUMOR FREE \n", + "1 0:ALIVE OR DEAD TUMOR FREE \n", + "2 0:ALIVE OR DEAD TUMOR FREE \n", + "3 0:ALIVE OR DEAD TUMOR FREE \n", + "4 0:ALIVE OR DEAD TUMOR FREE \n", + ".. ... \n", + "524 1:DEAD WITH TUMOR \n", + "525 0:ALIVE OR DEAD TUMOR FREE \n", + "526 0:ALIVE OR DEAD TUMOR FREE \n", + "527 0:ALIVE OR DEAD TUMOR FREE \n", + "528 0:ALIVE OR DEAD TUMOR FREE \n", + "\n", + "[529 rows x 3 columns]\n" + ] + } + ], + "source": [ + "# Explore features and labels\n", + "features = data.drop(['Overall Survival Status', 'Disease Free Status', 'Disease-specific Survival status'], axis=1)\n", + "labels = data[['Overall Survival Status', 'Disease Free Status', 'Disease-specific Survival status']]\n", + "print(\"FEATURES:\\n\",features)\n", + "\n", + "print(\"LABELS:\\n\",labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d125cb6e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Patient IDSample IDCancer Type DetailedOverall Survival StatusDisease Free StatusDisease-specific Survival statusMutation CountFraction Genome AlteredDiagnosis AgeMSI MANTIS ScoreMSIsensor ScoreRace CategorySubtypeTumor Type
0TCGA-2E-A9G8TCGA-2E-A9G8-01Uterine Endometrioid Carcinoma0:LIVINGNaN0:ALIVE OR DEAD TUMOR FREE65.00.331159.00.32340.85Black or African AmericanUCEC_CN_HIGHEndometrioid Endometrial Adenocarcinoma
1TCGA-4E-A92ETCGA-4E-A92E-01Uterine Endometrioid Carcinoma0:LIVING0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE147.00.034154.00.33960.01Black or African AmericanUCEC_CN_LOWEndometrioid Endometrial Adenocarcinoma
2TCGA-5B-A90CTCGA-5B-A90C-01Uterine Endometrioid Carcinoma0:LIVINGNaN0:ALIVE OR DEAD TUMOR FREE45.00.690369.00.33440.55Black or African AmericanUCEC_CN_HIGHEndometrioid Endometrial Adenocarcinoma
3TCGA-5S-A9Q8TCGA-5S-A9Q8-01Uterine Endometrioid Carcinoma0:LIVING0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE50.00.058151.00.31990.09Black or African AmericanUCEC_CN_LOWEndometrioid Endometrial Adenocarcinoma
4TCGA-A5-A0G1TCGA-A5-A0G1-01Uterine Serous Carcinoma/Uterine Papillary Ser...1:DECEASED0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE10808.00.000167.00.31081.74WhiteUCEC_POLESerous Endometrial Adenocarcinoma
\n", + "
" + ], + "text/plain": [ + " Patient ID Sample ID \\\n", + "0 TCGA-2E-A9G8 TCGA-2E-A9G8-01 \n", + "1 TCGA-4E-A92E TCGA-4E-A92E-01 \n", + "2 TCGA-5B-A90C TCGA-5B-A90C-01 \n", + "3 TCGA-5S-A9Q8 TCGA-5S-A9Q8-01 \n", + "4 TCGA-A5-A0G1 TCGA-A5-A0G1-01 \n", + "\n", + " Cancer Type Detailed Overall Survival Status \\\n", + "0 Uterine Endometrioid Carcinoma 0:LIVING \n", + "1 Uterine Endometrioid Carcinoma 0:LIVING \n", + "2 Uterine Endometrioid Carcinoma 0:LIVING \n", + "3 Uterine Endometrioid Carcinoma 0:LIVING \n", + "4 Uterine Serous Carcinoma/Uterine Papillary Ser... 1:DECEASED \n", + "\n", + " Disease Free Status Disease-specific Survival status Mutation Count \\\n", + "0 NaN 0:ALIVE OR DEAD TUMOR FREE 65.0 \n", + "1 0:DiseaseFree 0:ALIVE OR DEAD TUMOR FREE 147.0 \n", + "2 NaN 0:ALIVE OR DEAD TUMOR FREE 45.0 \n", + "3 0:DiseaseFree 0:ALIVE OR DEAD TUMOR FREE 50.0 \n", + "4 0:DiseaseFree 0:ALIVE OR DEAD TUMOR FREE 10808.0 \n", + "\n", + " Fraction Genome Altered Diagnosis Age MSI MANTIS Score MSIsensor Score \\\n", + "0 0.3311 59.0 0.3234 0.85 \n", + "1 0.0341 54.0 0.3396 0.01 \n", + "2 0.6903 69.0 0.3344 0.55 \n", + "3 0.0581 51.0 0.3199 0.09 \n", + "4 0.0001 67.0 0.3108 1.74 \n", + "\n", + " Race Category Subtype \\\n", + "0 Black or African American UCEC_CN_HIGH \n", + "1 Black or African American UCEC_CN_LOW \n", + "2 Black or African American UCEC_CN_HIGH \n", + "3 Black or African American UCEC_CN_LOW \n", + "4 White UCEC_POLE \n", + "\n", + " Tumor Type \n", + "0 Endometrioid Endometrial Adenocarcinoma \n", + "1 Endometrioid Endometrial Adenocarcinoma \n", + "2 Endometrioid Endometrial Adenocarcinoma \n", + "3 Endometrioid Endometrial Adenocarcinoma \n", + "4 Serous Endometrial Adenocarcinoma " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Show the first 5 rows of the dataset\n", + "\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e668681f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Patient IDSample IDCancer Type DetailedOverall Survival StatusDisease Free StatusDisease-specific Survival statusMutation CountFraction Genome AlteredDiagnosis AgeMSI MANTIS ScoreMSIsensor ScoreRace CategorySubtypeTumor Type
524TCGA-QS-A8F1TCGA-QS-A8F1-01Uterine Serous Carcinoma/Uterine Papillary Ser...1:DECEASEDNaN1:DEAD WITH TUMOR63.00.654985.00.36470.15Black or African AmericanUCEC_CN_HIGHSerous Endometrial Adenocarcinoma
525TCGA-SJ-A6ZITCGA-SJ-A6ZI-01Uterine Endometrioid Carcinoma0:LIVING0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE1306.00.027564.00.613814.35Black or African AmericanUCEC_MSIEndometrioid Endometrial Adenocarcinoma
526TCGA-SJ-A6ZJTCGA-SJ-A6ZJ-01Uterine Endometrioid Carcinoma0:LIVING0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE56.00.046661.00.33820.00Black or African AmericanUCEC_CN_LOWEndometrioid Endometrial Adenocarcinoma
527TCGA-SL-A6J9TCGA-SL-A6J9-01Uterine Endometrioid Carcinoma0:LIVINGNaN0:ALIVE OR DEAD TUMOR FREE48.00.422673.00.35030.03Black or African AmericanUCEC_CN_HIGHEndometrioid Endometrial Adenocarcinoma
528TCGA-SL-A6JATCGA-SL-A6JA-01Uterine Endometrioid Carcinoma0:LIVING0:DiseaseFree0:ALIVE OR DEAD TUMOR FREE742.00.048877.00.951526.10Black or African AmericanUCEC_MSIEndometrioid Endometrial Adenocarcinoma
\n", + "
" + ], + "text/plain": [ + " Patient ID Sample ID \\\n", + "524 TCGA-QS-A8F1 TCGA-QS-A8F1-01 \n", + "525 TCGA-SJ-A6ZI TCGA-SJ-A6ZI-01 \n", + "526 TCGA-SJ-A6ZJ TCGA-SJ-A6ZJ-01 \n", + "527 TCGA-SL-A6J9 TCGA-SL-A6J9-01 \n", + "528 TCGA-SL-A6JA TCGA-SL-A6JA-01 \n", + "\n", + " Cancer Type Detailed \\\n", + "524 Uterine Serous Carcinoma/Uterine Papillary Ser... \n", + "525 Uterine Endometrioid Carcinoma \n", + "526 Uterine Endometrioid Carcinoma \n", + "527 Uterine Endometrioid Carcinoma \n", + "528 Uterine Endometrioid Carcinoma \n", + "\n", + " Overall Survival Status Disease Free Status \\\n", + "524 1:DECEASED NaN \n", + "525 0:LIVING 0:DiseaseFree \n", + "526 0:LIVING 0:DiseaseFree \n", + "527 0:LIVING NaN \n", + "528 0:LIVING 0:DiseaseFree \n", + "\n", + " Disease-specific Survival status Mutation Count Fraction Genome Altered \\\n", + "524 1:DEAD WITH TUMOR 63.0 0.6549 \n", + "525 0:ALIVE OR DEAD TUMOR FREE 1306.0 0.0275 \n", + "526 0:ALIVE OR DEAD TUMOR FREE 56.0 0.0466 \n", + "527 0:ALIVE OR DEAD TUMOR FREE 48.0 0.4226 \n", + "528 0:ALIVE OR DEAD TUMOR FREE 742.0 0.0488 \n", + "\n", + " Diagnosis Age MSI MANTIS Score MSIsensor Score \\\n", + "524 85.0 0.3647 0.15 \n", + "525 64.0 0.6138 14.35 \n", + "526 61.0 0.3382 0.00 \n", + "527 73.0 0.3503 0.03 \n", + "528 77.0 0.9515 26.10 \n", + "\n", + " Race Category Subtype \\\n", + "524 Black or African American UCEC_CN_HIGH \n", + "525 Black or African American UCEC_MSI \n", + "526 Black or African American UCEC_CN_LOW \n", + "527 Black or African American UCEC_CN_HIGH \n", + "528 Black or African American UCEC_MSI \n", + "\n", + " Tumor Type \n", + "524 Serous Endometrial Adenocarcinoma \n", + "525 Endometrioid Endometrial Adenocarcinoma \n", + "526 Endometrioid Endometrial Adenocarcinoma \n", + "527 Endometrioid Endometrial Adenocarcinoma \n", + "528 Endometrioid Endometrial Adenocarcinoma " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Show the last 5 rows of the dataset\n", + "\n", + "data.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9a7689f4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Patient ID\n", + "Sample ID\n", + "Cancer Type Detailed\n", + "Overall Survival Status\n", + "Disease Free Status\n", + "Disease-specific Survival status\n", + "Mutation Count\n", + "Fraction Genome Altered\n", + "Diagnosis Age\n", + "MSI MANTIS Score\n", + "MSIsensor Score\n", + "Race Category\n", + "Subtype\n", + "Tumor Type\n" + ] + } + ], + "source": [ + "# List of column names\n", + "\n", + "for col in list(data.columns):\n", + " print(col)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "18eb88f2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of rows: 529\n", + "Number of columns: 14\n" + ] + } + ], + "source": [ + "#gives the size/shape of the dataset\n", + "#How many columns and rows are there in the dataset.\n", + "\n", + "num_rows, num_columns = data.shape\n", + "\n", + "print(\"Number of rows:\", num_rows)\n", + "print(\"Number of columns:\", num_columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "92e36767", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Size of the dataset: 7406\n", + "Shape of the dataset: (529, 14)\n" + ] + } + ], + "source": [ + "# Explore size/shape of the dataset\n", + "print(\"Size of the dataset:\", data.size)\n", + "print(\"Shape of the dataset:\", data.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "21946a8b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Patient ID object\n", + "Sample ID object\n", + "Cancer Type Detailed object\n", + "Overall Survival Status object\n", + "Disease Free Status object\n", + "Disease-specific Survival status object\n", + "Mutation Count float64\n", + "Fraction Genome Altered float64\n", + "Diagnosis Age float64\n", + "MSI MANTIS Score float64\n", + "MSIsensor Score float64\n", + "Race Category object\n", + "Subtype object\n", + "Tumor Type object\n", + "dtype: object" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Check the data type of dataframe pcos\n", + "data.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "13c408f0", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Convert columns to better data types if possible\n", + "data['Diagnosis Age'] = pd.to_numeric(data['Diagnosis Age'], errors='coerce')\n", + "data['MSI MANTIS Score'] = pd.to_numeric(data['MSI MANTIS Score'], errors='coerce')\n", + "data['MSIsensor Score'] = pd.to_numeric(data['MSIsensor Score'], errors='coerce')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "a2142912", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "original_memory: 375453\n", + "optimized_memory: 375453\n" + ] + } + ], + "source": [ + "\n", + "# Calculate memory usage differences\n", + "original_memory = data.memory_usage(deep=True).sum()\n", + "optimized_memory = data.memory_usage(deep=True).sum()\n", + "print(\"original_memory: \",original_memory)\n", + "print(\"optimized_memory: \",optimized_memory)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "77be34a4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 529 entries, 0 to 528\n", + "Data columns (total 14 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Patient ID 529 non-null object \n", + " 1 Sample ID 529 non-null object \n", + " 2 Cancer Type Detailed 529 non-null object \n", + " 3 Overall Survival Status 529 non-null object \n", + " 4 Disease Free Status 414 non-null object \n", + " 5 Disease-specific Survival status 527 non-null object \n", + " 6 Mutation Count 515 non-null float64\n", + " 7 Fraction Genome Altered 519 non-null float64\n", + " 8 Diagnosis Age 526 non-null float64\n", + " 9 MSI MANTIS Score 526 non-null float64\n", + " 10 MSIsensor Score 528 non-null float64\n", + " 11 Race Category 497 non-null object \n", + " 12 Subtype 507 non-null object \n", + " 13 Tumor Type 529 non-null object \n", + "dtypes: float64(5), object(9)\n", + "memory usage: 58.0+ KB\n" + ] + } + ], + "source": [ + "#Information about the dataset\n", + "#This method prints information about a DataFrame including the index dtype and columns, non-null values and memory usage. \n", + "\n", + "data.info()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "bfb8dc6f", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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countmeanstdmin25%50%75%max
Mutation Count515.01046.4233012734.3654947.00047.0000076.0000563.50000025696.0000
Fraction Genome Altered519.00.1859590.2260880.0000.003600.06780.3144500.9487
Diagnosis Age526.063.76616011.06003031.00057.0000064.000071.00000090.0000
MSI MANTIS Score526.00.4389480.2396660.2370.283950.31130.5432251.3049
MSIsensor Score528.06.39589010.1758530.0000.110000.435010.11750040.4300
\n", + "
" + ], + "text/plain": [ + " count mean std min 25% \\\n", + "Mutation Count 515.0 1046.423301 2734.365494 7.000 47.00000 \n", + "Fraction Genome Altered 519.0 0.185959 0.226088 0.000 0.00360 \n", + "Diagnosis Age 526.0 63.766160 11.060030 31.000 57.00000 \n", + "MSI MANTIS Score 526.0 0.438948 0.239666 0.237 0.28395 \n", + "MSIsensor Score 528.0 6.395890 10.175853 0.000 0.11000 \n", + "\n", + " 50% 75% max \n", + "Mutation Count 76.0000 563.500000 25696.0000 \n", + "Fraction Genome Altered 0.0678 0.314450 0.9487 \n", + "Diagnosis Age 64.0000 71.000000 90.0000 \n", + "MSI MANTIS Score 0.3113 0.543225 1.3049 \n", + "MSIsensor Score 0.4350 10.117500 40.4300 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "#If we would like to get a statistical summary of each column, such as count, column mean value, column standard deviation, etc.\n", + "#We use the describe method:\n", + "\n", + "data.describe().T" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "1402eaab", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Statistical facts of the dataset:\n", + " Mutation Count Fraction Genome Altered Diagnosis Age \\\n", + "count 515.000000 519.000000 526.00000 \n", + "mean 1046.423301 0.185959 63.76616 \n", + "std 2734.365494 0.226088 11.06003 \n", + "min 7.000000 0.000000 31.00000 \n", + "25% 47.000000 0.003600 57.00000 \n", + "50% 76.000000 0.067800 64.00000 \n", + "75% 563.500000 0.314450 71.00000 \n", + "max 25696.000000 0.948700 90.00000 \n", + "\n", + " MSI MANTIS Score MSIsensor Score \n", + "count 526.000000 528.000000 \n", + "mean 0.438948 6.395890 \n", + "std 0.239666 10.175853 \n", + "min 0.237000 0.000000 \n", + "25% 0.283950 0.110000 \n", + "50% 0.311300 0.435000 \n", + "75% 0.543225 10.117500 \n", + "max 1.304900 40.430000 \n" + ] + } + ], + "source": [ + "# Explore statistical facts\n", + "print(\"Statistical facts of the dataset:\")\n", + "print(data.describe())" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "b931888d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Patient ID 0\n", + "Sample ID 0\n", + "Cancer Type Detailed 0\n", + "Overall Survival Status 0\n", + "Disease Free Status 115\n", + "Disease-specific Survival status 2\n", + "Mutation Count 14\n", + "Fraction Genome Altered 10\n", + "Diagnosis Age 3\n", + "MSI MANTIS Score 3\n", + "MSIsensor Score 1\n", + "Race Category 32\n", + "Subtype 22\n", + "Tumor Type 0\n", + "dtype: int64" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check the null values present in dataset \n", + "data.isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "id": "0822b004", + "metadata": {}, + "source": [ + "# matplotlib.pyplot, seaborn, and scipy.stats libraries are used for data visualization and statistical analysis. These are commonly used libraries in Python for such tasks.\n", + "\n", + "matplotlib.pyplot is a plotting library that provides a MATLAB-like interface for creating visualizations in Python.\n", + "\n", + "seaborn is a statistical data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.\n", + "\n", + "scipy.stats contains a large number of probability distributions and statistical functions. It's useful for statistical analysis and hypothesis testing.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "63b47d52", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import scipy.stats as stats" + ] + }, + { + "cell_type": "markdown", + "id": "b8477629", + "metadata": {}, + "source": [ + "## Univariate Analysis\n", + "Univariate analysis refers to the statistical examination of a single variable. The term \"univariate\" comes from combining \"uni,\" meaning one, and \"variate,\" meaning variable." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "64a2c8f0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting Age at Diagnosis\n", + "plt.figure(figsize=(10, 6))\n", + "sns.histplot(data['Diagnosis Age'], bins=30, kde=True)\n", + "plt.title('Distribution of Age at Diagnosis')\n", + "plt.xlabel('Age at Diagnosis')\n", + "plt.ylabel('Frequency')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "2e70b710", + "metadata": {}, + "source": [ + "## Univariate Analysis: Age at Diagnosis\n", + "- The distribution of age at diagnosis is approximately normal but slightly right-skewed.\n", + "- The majority of patients are diagnosed between the ages of 50 and 70.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "32945f60", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting Mutation Count\n", + "plt.figure(figsize=(10, 6))\n", + "sns.histplot(data['Mutation Count'], bins=50, kde=True)\n", + "plt.title('Distribution of Mutation Count')\n", + "plt.xlabel('Mutation Count')\n", + "plt.ylabel('Frequency')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "fc7062c6", + "metadata": {}, + "source": [ + "## Univariate Analysis: Mutation Count\n", + "- The mutation count distribution is heavily right-skewed, with a few patients having very high mutation counts.\n", + "- Most patients have mutation counts below 2,500.\n" + ] + }, + { + "cell_type": "markdown", + "id": "677c238e", + "metadata": {}, + "source": [ + "## Multivariate Analysis\n", + "Multivariate analysis is a set of statistical techniques used to analyze data that involves multiple variables simultaneously. It allows researchers to understand relationships between variables, patterns within the data, and the structure of complex datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1e273bfe", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Correlation Matrix\n", + "# Select only numeric columns for the correlation matrix\n", + "numeric_data = data.select_dtypes(include=[np.number])\n", + "\n", + "# Calculate the correlation matrix\n", + "correlation_matrix = numeric_data.corr()\n", + "\n", + "# Plotting the correlation matrix\n", + "plt.figure(figsize=(12, 8))\n", + "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')\n", + "plt.title('Correlation Matrix')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d22213c6", + "metadata": {}, + "source": [ + "## Multivariate Analysis: Correlation Matrix\n", + "- There is a strong positive correlation between MSI MANTIS Score and Mutation Count.\n", + "- The Fraction Genome Altered shows a moderate positive correlation with Mutation Count.\n", + "- Diagnosis Age shows little to no correlation with Mutation Count or MSI scores.\n" + ] + }, + { + "cell_type": "markdown", + "id": "5c815da4", + "metadata": {}, + "source": [ + "## Checking Distribution (Skewness)\n", + "Checking the distribution of a dataset, particularly skewness, involves assessing how much the data deviates from a symmetrical, bell-shaped (normal) distribution. Skewness is a measure of the asymmetry of the distribution of values." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "cb9b3e70", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Diagnosis Age -0.201695\n", + "Mutation Count 4.009429\n", + "Fraction Genome Altered 1.240640\n", + "MSI MANTIS Score 1.423920\n", + "MSIsensor Score 1.468257\n", + "dtype: float64\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Skewness\n", + "print(data[['Diagnosis Age', 'Mutation Count', 'Fraction Genome Altered', 'MSI MANTIS Score', 'MSIsensor Score']].skew())\n", + "\n", + "# Plotting the distributions\n", + "fig, axes = plt.subplots(2, 3, figsize=(15, 10))\n", + "sns.histplot(data['Diagnosis Age'], bins=30, kde=True, ax=axes[0, 0])\n", + "sns.histplot(data['Mutation Count'], bins=30, kde=True, ax=axes[0, 1])\n", + "sns.histplot(data['Fraction Genome Altered'], bins=30, kde=True, ax=axes[0, 2])\n", + "sns.histplot(data['MSI MANTIS Score'], bins=30, kde=True, ax=axes[1, 0])\n", + "sns.histplot(data['MSIsensor Score'], bins=30, kde=True, ax=axes[1, 1])\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "655c4718", + "metadata": {}, + "source": [ + "## Skewness of Columns\n", + "- **Diagnosis Age**: Slightly right-skewed.\n", + "- **Mutation Count**: Heavily right-skewed.\n", + "- **Fraction Genome Altered**: Right-skewed.\n", + "- **MSI MANTIS Score**: Right-skewed.\n", + "- **MSIsensor Score**: Right-skewed.\n" + ] + }, + { + "cell_type": "markdown", + "id": "1440a656", + "metadata": {}, + "source": [ + "## Detecting Outliers\n", + "Detecting outliers involves identifying observations in a dataset that significantly deviate from the rest of the data. Outliers can occur due to various reasons such as data entry errors, measurement errors, or genuine anomalies in the data." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "c02bae5c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Boxplots to detect outliers\n", + "fig, axes = plt.subplots(2, 3, figsize=(15, 10))\n", + "sns.boxplot(data['Diagnosis Age'], ax=axes[0, 0])\n", + "sns.boxplot(data['Mutation Count'], ax=axes[0, 1])\n", + "sns.boxplot(data['Fraction Genome Altered'], ax=axes[0, 2])\n", + "sns.boxplot(data['MSI MANTIS Score'], ax=axes[1, 0])\n", + "sns.boxplot(data['MSIsensor Score'], ax=axes[1, 1])\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "6e40b0a4", + "metadata": {}, + "source": [ + "## Outliers Detection\n", + "- **Diagnosis Age**: Few outliers at the higher age spectrum.\n", + "- **Mutation Count**: Significant number of outliers with high mutation counts.\n", + "- **Fraction Genome Altered**: Presence of outliers with higher altered fractions.\n", + "- **MSI MANTIS Score**: Outliers present with higher MSI scores.\n", + "- **MSIsensor Score**: Similar to MSI MANTIS, outliers with high scores are observed.\n", + "\n", + "Outliers indicate extreme values in mutation counts and MSI scores, which may need special attention during analysis.\n" + ] + }, + { + "cell_type": "markdown", + "id": "3f6baeaf", + "metadata": {}, + "source": [ + "## Class Label Imbalance\n", + "Class label imbalance, also known as class imbalance, occurs when one class (or label) in a classification problem has significantly fewer observations than the other classes. This imbalance can skew the predictive model's performance and bias it towards the majority class." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "1d64856d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting Overall Survival Status\n", + "plt.figure(figsize=(6, 4))\n", + "sns.countplot(x='Overall Survival Status', data=data)\n", + "plt.title('Overall Survival Status Distribution')\n", + "plt.xlabel('Overall Survival Status')\n", + "plt.ylabel('Count')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "20b8bfd5", + "metadata": {}, + "source": [ + "## Class Label Imbalance: Overall Survival Status\n", + "- There is a class imbalance in the overall survival status with a higher number of living patients (84%) compared to deceased (16%).\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From b4a7ade399c3f7141781df7496fa543cf4cc9fea Mon Sep 17 00:00:00 2001 From: RUTIKA WAGALEKAR <120473716+RutikaW1155@users.noreply.github.com> Date: Sun, 19 May 2024 13:22:00 +0530 Subject: [PATCH 10/12] Rename README.md to README.md --- .../README.md | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename {Endometriral Cancer Prediction => Endometrial Cancer Prediction}/README.md (100%) diff --git a/Endometriral Cancer Prediction/README.md b/Endometrial Cancer Prediction/README.md similarity index 100% rename from Endometriral Cancer Prediction/README.md rename to Endometrial Cancer Prediction/README.md From e7289847bebdd19e6f78af33e1b619e526c23ce7 Mon Sep 17 00:00:00 2001 From: RUTIKA WAGALEKAR <120473716+RutikaW1155@users.noreply.github.com> Date: Sun, 19 May 2024 13:23:58 +0530 Subject: [PATCH 11/12] Rename Endometrial Cancer Prediction Dataset.ipynb to Endometrial Cancer Prediction Dataset.ipynb --- .../Endometrial Cancer Prediction Dataset.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename {Endometriral Cancer Prediction => Endometrial Cancer Prediction}/Endometrial Cancer Prediction Dataset.ipynb (100%) diff --git a/Endometriral Cancer Prediction/Endometrial Cancer Prediction Dataset.ipynb b/Endometrial Cancer Prediction/Endometrial Cancer Prediction Dataset.ipynb similarity index 100% rename from Endometriral Cancer Prediction/Endometrial Cancer Prediction Dataset.ipynb rename to Endometrial Cancer Prediction/Endometrial Cancer Prediction Dataset.ipynb From b19511397d5030290444df6d56491ac935a9b34d Mon Sep 17 00:00:00 2001 From: RUTIKA WAGALEKAR <120473716+RutikaW1155@users.noreply.github.com> Date: Sun, 19 May 2024 13:24:37 +0530 Subject: [PATCH 12/12] Update and rename Uterine Corpus Endometrial Carcinoma dataset.csv to Uterine Corpus Endometrial Carcinoma dataset.csv --- .../Uterine Corpus Endometrial Carcinoma dataset.csv | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename {Endometriral Cancer Prediction => Endometrial Cancer Prediction}/Uterine Corpus Endometrial Carcinoma dataset.csv (100%) diff --git a/Endometriral Cancer Prediction/Uterine Corpus Endometrial Carcinoma dataset.csv b/Endometrial Cancer Prediction/Uterine Corpus Endometrial Carcinoma dataset.csv similarity index 100% rename from Endometriral Cancer Prediction/Uterine Corpus Endometrial Carcinoma dataset.csv rename to Endometrial Cancer Prediction/Uterine Corpus Endometrial Carcinoma dataset.csv