diff --git a/Endometrial Cancer Prediction/Endometrial Cancer Prediction Dataset.ipynb b/Endometrial Cancer Prediction/Endometrial Cancer Prediction Dataset.ipynb
new file mode 100644
index 00000000..fc075d71
--- /dev/null
+++ b/Endometrial Cancer Prediction/Endometrial Cancer Prediction Dataset.ipynb	
@@ -0,0 +1,1615 @@
+{
+ "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": "code",
+   "execution_count": 5,
+   "id": "82c0c766",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Patient ID</th>\n",
+       "      <th>Sample ID</th>\n",
+       "      <th>Cancer Type Detailed</th>\n",
+       "      <th>Overall Survival Status</th>\n",
+       "      <th>Disease Free Status</th>\n",
+       "      <th>Disease-specific Survival status</th>\n",
+       "      <th>Mutation Count</th>\n",
+       "      <th>Fraction Genome Altered</th>\n",
+       "      <th>Diagnosis Age</th>\n",
+       "      <th>MSI MANTIS Score</th>\n",
+       "      <th>MSIsensor Score</th>\n",
+       "      <th>Race Category</th>\n",
+       "      <th>Subtype</th>\n",
+       "      <th>Tumor Type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>TCGA-2E-A9G8</td>\n",
+       "      <td>TCGA-2E-A9G8-01</td>\n",
+       "      <td>Uterine Endometrioid Carcinoma</td>\n",
+       "      <td>0:LIVING</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>0.3311</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>0.3234</td>\n",
+       "      <td>0.85</td>\n",
+       "      <td>Black or African American</td>\n",
+       "      <td>UCEC_CN_HIGH</td>\n",
+       "      <td>Endometrioid Endometrial Adenocarcinoma</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>TCGA-4E-A92E</td>\n",
+       "      <td>TCGA-4E-A92E-01</td>\n",
+       "      <td>Uterine Endometrioid Carcinoma</td>\n",
+       "      <td>0:LIVING</td>\n",
+       "      <td>0:DiseaseFree</td>\n",
+       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
+       "      <td>147.0</td>\n",
+       "      <td>0.0341</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>0.3396</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>Black or African American</td>\n",
+       "      <td>UCEC_CN_LOW</td>\n",
+       "      <td>Endometrioid Endometrial Adenocarcinoma</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>TCGA-5B-A90C</td>\n",
+       "      <td>TCGA-5B-A90C-01</td>\n",
+       "      <td>Uterine Endometrioid Carcinoma</td>\n",
+       "      <td>0:LIVING</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>0.6903</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>0.3344</td>\n",
+       "      <td>0.55</td>\n",
+       "      <td>Black or African American</td>\n",
+       "      <td>UCEC_CN_HIGH</td>\n",
+       "      <td>Endometrioid Endometrial Adenocarcinoma</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>TCGA-5S-A9Q8</td>\n",
+       "      <td>TCGA-5S-A9Q8-01</td>\n",
+       "      <td>Uterine Endometrioid Carcinoma</td>\n",
+       "      <td>0:LIVING</td>\n",
+       "      <td>0:DiseaseFree</td>\n",
+       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>0.0581</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>0.3199</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>Black or African American</td>\n",
+       "      <td>UCEC_CN_LOW</td>\n",
+       "      <td>Endometrioid Endometrial Adenocarcinoma</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>TCGA-A5-A0G1</td>\n",
+       "      <td>TCGA-A5-A0G1-01</td>\n",
+       "      <td>Uterine Serous Carcinoma/Uterine Papillary Ser...</td>\n",
+       "      <td>1:DECEASED</td>\n",
+       "      <td>0:DiseaseFree</td>\n",
+       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
+       "      <td>10808.0</td>\n",
+       "      <td>0.0001</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>0.3108</td>\n",
+       "      <td>1.74</td>\n",
+       "      <td>White</td>\n",
+       "      <td>UCEC_POLE</td>\n",
+       "      <td>Serous Endometrial Adenocarcinoma</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>524</th>\n",
+       "      <td>TCGA-QS-A8F1</td>\n",
+       "      <td>TCGA-QS-A8F1-01</td>\n",
+       "      <td>Uterine Serous Carcinoma/Uterine Papillary Ser...</td>\n",
+       "      <td>1:DECEASED</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1:DEAD WITH TUMOR</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>0.6549</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>0.3647</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>Black or African American</td>\n",
+       "      <td>UCEC_CN_HIGH</td>\n",
+       "      <td>Serous Endometrial Adenocarcinoma</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>525</th>\n",
+       "      <td>TCGA-SJ-A6ZI</td>\n",
+       "      <td>TCGA-SJ-A6ZI-01</td>\n",
+       "      <td>Uterine Endometrioid Carcinoma</td>\n",
+       "      <td>0:LIVING</td>\n",
+       "      <td>0:DiseaseFree</td>\n",
+       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
+       "      <td>1306.0</td>\n",
+       "      <td>0.0275</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>0.6138</td>\n",
+       "      <td>14.35</td>\n",
+       "      <td>Black or African American</td>\n",
+       "      <td>UCEC_MSI</td>\n",
+       "      <td>Endometrioid Endometrial Adenocarcinoma</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>526</th>\n",
+       "      <td>TCGA-SJ-A6ZJ</td>\n",
+       "      <td>TCGA-SJ-A6ZJ-01</td>\n",
+       "      <td>Uterine Endometrioid Carcinoma</td>\n",
+       "      <td>0:LIVING</td>\n",
+       "      <td>0:DiseaseFree</td>\n",
+       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>0.0466</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>0.3382</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>Black or African American</td>\n",
+       "      <td>UCEC_CN_LOW</td>\n",
+       "      <td>Endometrioid Endometrial Adenocarcinoma</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>527</th>\n",
+       "      <td>TCGA-SL-A6J9</td>\n",
+       "      <td>TCGA-SL-A6J9-01</td>\n",
+       "      <td>Uterine Endometrioid Carcinoma</td>\n",
+       "      <td>0:LIVING</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>0.4226</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>0.3503</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>Black or African American</td>\n",
+       "      <td>UCEC_CN_HIGH</td>\n",
+       "      <td>Endometrioid Endometrial Adenocarcinoma</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>528</th>\n",
+       "      <td>TCGA-SL-A6JA</td>\n",
+       "      <td>TCGA-SL-A6JA-01</td>\n",
+       "      <td>Uterine Endometrioid Carcinoma</td>\n",
+       "      <td>0:LIVING</td>\n",
+       "      <td>0:DiseaseFree</td>\n",
+       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
+       "      <td>742.0</td>\n",
+       "      <td>0.0488</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>0.9515</td>\n",
+       "      <td>26.10</td>\n",
+       "      <td>Black or African American</td>\n",
+       "      <td>UCEC_MSI</td>\n",
+       "      <td>Endometrioid Endometrial Adenocarcinoma</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>529 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "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\"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": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Patient ID</th>\n",
+       "      <th>Sample ID</th>\n",
+       "      <th>Cancer Type Detailed</th>\n",
+       "      <th>Overall Survival Status</th>\n",
+       "      <th>Disease Free Status</th>\n",
+       "      <th>Disease-specific Survival status</th>\n",
+       "      <th>Mutation Count</th>\n",
+       "      <th>Fraction Genome Altered</th>\n",
+       "      <th>Diagnosis Age</th>\n",
+       "      <th>MSI MANTIS Score</th>\n",
+       "      <th>MSIsensor Score</th>\n",
+       "      <th>Race Category</th>\n",
+       "      <th>Subtype</th>\n",
+       "      <th>Tumor Type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>TCGA-2E-A9G8</td>\n",
+       "      <td>TCGA-2E-A9G8-01</td>\n",
+       "      <td>Uterine Endometrioid Carcinoma</td>\n",
+       "      <td>0:LIVING</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>0.3311</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>0.3234</td>\n",
+       "      <td>0.85</td>\n",
+       "      <td>Black or African American</td>\n",
+       "      <td>UCEC_CN_HIGH</td>\n",
+       "      <td>Endometrioid Endometrial Adenocarcinoma</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>TCGA-4E-A92E</td>\n",
+       "      <td>TCGA-4E-A92E-01</td>\n",
+       "      <td>Uterine Endometrioid Carcinoma</td>\n",
+       "      <td>0:LIVING</td>\n",
+       "      <td>0:DiseaseFree</td>\n",
+       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
+       "      <td>147.0</td>\n",
+       "      <td>0.0341</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>0.3396</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>Black or African American</td>\n",
+       "      <td>UCEC_CN_LOW</td>\n",
+       "      <td>Endometrioid Endometrial Adenocarcinoma</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>TCGA-5B-A90C</td>\n",
+       "      <td>TCGA-5B-A90C-01</td>\n",
+       "      <td>Uterine Endometrioid Carcinoma</td>\n",
+       "      <td>0:LIVING</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>0.6903</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>0.3344</td>\n",
+       "      <td>0.55</td>\n",
+       "      <td>Black or African American</td>\n",
+       "      <td>UCEC_CN_HIGH</td>\n",
+       "      <td>Endometrioid Endometrial Adenocarcinoma</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>TCGA-5S-A9Q8</td>\n",
+       "      <td>TCGA-5S-A9Q8-01</td>\n",
+       "      <td>Uterine Endometrioid Carcinoma</td>\n",
+       "      <td>0:LIVING</td>\n",
+       "      <td>0:DiseaseFree</td>\n",
+       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>0.0581</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>0.3199</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>Black or African American</td>\n",
+       "      <td>UCEC_CN_LOW</td>\n",
+       "      <td>Endometrioid Endometrial Adenocarcinoma</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>TCGA-A5-A0G1</td>\n",
+       "      <td>TCGA-A5-A0G1-01</td>\n",
+       "      <td>Uterine Serous Carcinoma/Uterine Papillary Ser...</td>\n",
+       "      <td>1:DECEASED</td>\n",
+       "      <td>0:DiseaseFree</td>\n",
+       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
+       "      <td>10808.0</td>\n",
+       "      <td>0.0001</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>0.3108</td>\n",
+       "      <td>1.74</td>\n",
+       "      <td>White</td>\n",
+       "      <td>UCEC_POLE</td>\n",
+       "      <td>Serous Endometrial Adenocarcinoma</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "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": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Patient ID</th>\n",
+       "      <th>Sample ID</th>\n",
+       "      <th>Cancer Type Detailed</th>\n",
+       "      <th>Overall Survival Status</th>\n",
+       "      <th>Disease Free Status</th>\n",
+       "      <th>Disease-specific Survival status</th>\n",
+       "      <th>Mutation Count</th>\n",
+       "      <th>Fraction Genome Altered</th>\n",
+       "      <th>Diagnosis Age</th>\n",
+       "      <th>MSI MANTIS Score</th>\n",
+       "      <th>MSIsensor Score</th>\n",
+       "      <th>Race Category</th>\n",
+       "      <th>Subtype</th>\n",
+       "      <th>Tumor Type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>524</th>\n",
+       "      <td>TCGA-QS-A8F1</td>\n",
+       "      <td>TCGA-QS-A8F1-01</td>\n",
+       "      <td>Uterine Serous Carcinoma/Uterine Papillary Ser...</td>\n",
+       "      <td>1:DECEASED</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1:DEAD WITH TUMOR</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>0.6549</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>0.3647</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>Black or African American</td>\n",
+       "      <td>UCEC_CN_HIGH</td>\n",
+       "      <td>Serous Endometrial Adenocarcinoma</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>525</th>\n",
+       "      <td>TCGA-SJ-A6ZI</td>\n",
+       "      <td>TCGA-SJ-A6ZI-01</td>\n",
+       "      <td>Uterine Endometrioid Carcinoma</td>\n",
+       "      <td>0:LIVING</td>\n",
+       "      <td>0:DiseaseFree</td>\n",
+       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
+       "      <td>1306.0</td>\n",
+       "      <td>0.0275</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>0.6138</td>\n",
+       "      <td>14.35</td>\n",
+       "      <td>Black or African American</td>\n",
+       "      <td>UCEC_MSI</td>\n",
+       "      <td>Endometrioid Endometrial Adenocarcinoma</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>526</th>\n",
+       "      <td>TCGA-SJ-A6ZJ</td>\n",
+       "      <td>TCGA-SJ-A6ZJ-01</td>\n",
+       "      <td>Uterine Endometrioid Carcinoma</td>\n",
+       "      <td>0:LIVING</td>\n",
+       "      <td>0:DiseaseFree</td>\n",
+       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>0.0466</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>0.3382</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>Black or African American</td>\n",
+       "      <td>UCEC_CN_LOW</td>\n",
+       "      <td>Endometrioid Endometrial Adenocarcinoma</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>527</th>\n",
+       "      <td>TCGA-SL-A6J9</td>\n",
+       "      <td>TCGA-SL-A6J9-01</td>\n",
+       "      <td>Uterine Endometrioid Carcinoma</td>\n",
+       "      <td>0:LIVING</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>0.4226</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>0.3503</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>Black or African American</td>\n",
+       "      <td>UCEC_CN_HIGH</td>\n",
+       "      <td>Endometrioid Endometrial Adenocarcinoma</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>528</th>\n",
+       "      <td>TCGA-SL-A6JA</td>\n",
+       "      <td>TCGA-SL-A6JA-01</td>\n",
+       "      <td>Uterine Endometrioid Carcinoma</td>\n",
+       "      <td>0:LIVING</td>\n",
+       "      <td>0:DiseaseFree</td>\n",
+       "      <td>0:ALIVE OR DEAD TUMOR FREE</td>\n",
+       "      <td>742.0</td>\n",
+       "      <td>0.0488</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>0.9515</td>\n",
+       "      <td>26.10</td>\n",
+       "      <td>Black or African American</td>\n",
+       "      <td>UCEC_MSI</td>\n",
+       "      <td>Endometrioid Endometrial Adenocarcinoma</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "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": [
+      "<class 'pandas.core.frame.DataFrame'>\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": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Mutation Count</th>\n",
+       "      <td>515.0</td>\n",
+       "      <td>1046.423301</td>\n",
+       "      <td>2734.365494</td>\n",
+       "      <td>7.000</td>\n",
+       "      <td>47.00000</td>\n",
+       "      <td>76.0000</td>\n",
+       "      <td>563.500000</td>\n",
+       "      <td>25696.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fraction Genome Altered</th>\n",
+       "      <td>519.0</td>\n",
+       "      <td>0.185959</td>\n",
+       "      <td>0.226088</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.00360</td>\n",
+       "      <td>0.0678</td>\n",
+       "      <td>0.314450</td>\n",
+       "      <td>0.9487</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Diagnosis Age</th>\n",
+       "      <td>526.0</td>\n",
+       "      <td>63.766160</td>\n",
+       "      <td>11.060030</td>\n",
+       "      <td>31.000</td>\n",
+       "      <td>57.00000</td>\n",
+       "      <td>64.0000</td>\n",
+       "      <td>71.000000</td>\n",
+       "      <td>90.0000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MSI MANTIS Score</th>\n",
+       "      <td>526.0</td>\n",
+       "      <td>0.438948</td>\n",
+       "      <td>0.239666</td>\n",
+       "      <td>0.237</td>\n",
+       "      <td>0.28395</td>\n",
+       "      <td>0.3113</td>\n",
+       "      <td>0.543225</td>\n",
+       "      <td>1.3049</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MSIsensor Score</th>\n",
+       "      <td>528.0</td>\n",
+       "      <td>6.395890</td>\n",
+       "      <td>10.175853</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.11000</td>\n",
+       "      <td>0.4350</td>\n",
+       "      <td>10.117500</td>\n",
+       "      <td>40.4300</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "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": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "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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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "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": [
+       "<Figure size 1200x800 with 2 Axes>"
+      ]
+     },
+     "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": [
+       "<Figure size 1500x1000 with 6 Axes>"
+      ]
+     },
+     "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": [
+       "<Figure size 1500x1000 with 6 Axes>"
+      ]
+     },
+     "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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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "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
+}
diff --git a/Endometrial Cancer Prediction/README.md b/Endometrial Cancer Prediction/README.md
new file mode 100644
index 00000000..348c9467
--- /dev/null
+++ b/Endometrial Cancer Prediction/README.md	
@@ -0,0 +1,77 @@
+# 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.
+
+
+- [Endometrial cancer Prediction Dataset](https://www.kaggle.com/datasets/yeganehbavafa/uterine-corpus-endometrial-carcinoma)
+
+## Cause of Uterine Cancer
+
+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.
+
+## 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 compared to "0:LIVING" (indicating living)).
+
+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").
+
+## 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.
diff --git a/Endometrial Cancer Prediction/Uterine Corpus Endometrial Carcinoma dataset.csv b/Endometrial Cancer Prediction/Uterine Corpus Endometrial Carcinoma dataset.csv
new file mode 100644
index 00000000..3a181ad3
--- /dev/null
+++ b/Endometrial 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 DEAD TUMOR FREE,431,0.0038,57,0.9762,30.24,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma
+TCGA-A5-A0GI,TCGA-A5-A0GI-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,340,0.0439,63,0.5646,14.54,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma
+TCGA-A5-A0GJ,TCGA-A5-A0GJ-01,Uterine Endometrioid Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,61,0.4758,44,0.2959,0.31,White,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma
+TCGA-A5-A0GM,TCGA-A5-A0GM-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,42,0.0002,53,0.2497,0.05,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma
+TCGA-A5-A0GN,TCGA-A5-A0GN-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,56,0.557,65,0.2642,0.65,White,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma
+TCGA-A5-A0GP,TCGA-A5-A0GP-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR 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 FREE,43,0.0465,51,0.2815,0.33,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma
+TCGA-A5-A0RA,TCGA-A5-A0RA-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,76,0.0018,68,0.2688,0.03,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma
+TCGA-A5-A0VO,TCGA-A5-A0VO-01,Uterine Endometrioid Carcinoma,1:DECEASED,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,63,,64,0.2792,0,White,,Endometrioid Endometrial Adenocarcinoma
+TCGA-A5-A0VP,TCGA-A5-A0VP-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,1472,0.0813,74,0.5739,13.09,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma
+TCGA-A5-A0VQ,TCGA-A5-A0VQ-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,366,0.0001,62,0.6689,15.57,,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma
+TCGA-A5-A1OF,TCGA-A5-A1OF-01,Uterine Mixed Endometrial Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,10487,0.0009,47,0.4126,1.9,,UCEC_POLE,Mixed Serous and Endometrioid Carcinoma
+TCGA-A5-A1OG,TCGA-A5-A1OG-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,1:Recurred/Progressed,1:DEAD WITH TUMOR,37,0.3103,65,0.329,0.24,Asian,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma
+TCGA-A5-A1OH,TCGA-A5-A1OH-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,112,0.5541,86,0.3161,0.4,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma
+TCGA-A5-A1OJ,TCGA-A5-A1OJ-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,63,0.081,31,0.2921,0,Asian,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma
+TCGA-A5-A1OK,TCGA-A5-A1OK-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,65,0.0342,63,0.2684,0.01,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma
+TCGA-A5-A2K2,TCGA-A5-A2K2-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,124,0.2488,77,0.2927,0.29,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma
+TCGA-A5-A2K3,TCGA-A5-A2K3-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,1232,0.0559,68,0.4541,1.65,White,UCEC_MSI,Serous Endometrial Adenocarcinoma
+TCGA-A5-A2K4,TCGA-A5-A2K4-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,1:Recurred/Progressed,1:DEAD WITH TUMOR,30,0.2796,69,0.2858,0.04,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma
+TCGA-A5-A2K5,TCGA-A5-A2K5-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,8510,0.0001,76,0.2961,0.27,White,UCEC_POLE,Endometrioid Endometrial Adenocarcinoma
+TCGA-A5-A2K7,TCGA-A5-A2K7-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,609,0.0004,41,0.9748,20.29,,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma
+TCGA-A5-A3LO,TCGA-A5-A3LO-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD 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 Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,105,0.5519,61,0.3253,0.51,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma
+TCGA-AJ-A23N,TCGA-AJ-A23N-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,46,0.4072,70,0.328,0.11,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma
+TCGA-AJ-A23O,TCGA-AJ-A23O-01,Uterine Endometrioid Carcinoma,1:DECEASED,,0:ALIVE OR DEAD TUMOR FREE,860,0.1019,69,0.8244,15.75,White,UCEC_MSI,Endometrioid Endometrial Adenocarcinoma
+TCGA-AJ-A2QK,TCGA-AJ-A2QK-01,Uterine Endometrioid Carcinoma,0:LIVING,1:Recurred/Progressed,0:ALIVE OR DEAD TUMOR FREE,70,0.2657,65,0.2833,0.03,White,UCEC_CN_HIGH,Endometrioid Endometrial Adenocarcinoma
+TCGA-AJ-A2QL,TCGA-AJ-A2QL-01,Uterine Endometrioid Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,44,0.011,60,0.3116,0.01,White,UCEC_CN_LOW,Endometrioid Endometrial Adenocarcinoma
+TCGA-AJ-A2QM,TCGA-AJ-A2QM-01,Uterine Serous Carcinoma/Uterine 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 FREE,32,0.6446,72,0.4075,0.55,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma
+TCGA-KP-A3W4,TCGA-KP-A3W4-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,1:DECEASED,,1:DEAD WITH TUMOR,47,0.1501,63,0.3573,0.06,White,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma
+TCGA-PG-A5BC,TCGA-PG-A5BC-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,42,0.42,72,0.3104,0.35,Black or African American,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma
+TCGA-PG-A6IB,TCGA-PG-A6IB-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,0:DiseaseFree,0:ALIVE OR DEAD TUMOR FREE,433,0.0006,68,0.6358,9.83,Black or African American,UCEC_MSI,Serous Endometrial Adenocarcinoma
+TCGA-PG-A7D5,TCGA-PG-A7D5-01,Uterine Serous Carcinoma/Uterine Papillary Serous Carcinoma,0:LIVING,,0:ALIVE OR DEAD TUMOR FREE,30,0.4382,62,0.3506,0.1,Black or African American,UCEC_CN_HIGH,Serous Endometrial Adenocarcinoma
+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