Traditional　clinical　features　are　not　sufficient　to　accurately　judge　the　prognosis　of　endometrioid　endometrial　adenocarcinoma　(EEA).　Molecular　biological　characteristics　and　traditional　clinical　features　are　particularly　important　in　the　prognosis　of　EEA.　The　aim　of　the　present　study　was　to　establish　a　predictive　model　that　considers　genes　and　clinical　features　for　the　prognosis　of　EEA.　The　clinical　and　RNA　sequencing　expression　data　of　EEA　were　derived　from　samples　from　The　Cancer　Genome　Atlas　(TCGA)　and　Peking　University　People＇s　Hospital　(PKUPH;　Beijing,　China).　Samples　from　TCGA　were　used　as　the　training　set,　and　samples　from　the　PKUPH　were　used　as　the　testing　set.　Variable　selection　using　Random　Forests　(VSURF)　was　used　to　select　the　genes　and　clinical　features　on　the　basis　of　TCGA　samples.　The　RF　classification　method　was　used　to　establish　the　prediction　model.　Kaplan-Meier　curves　were　tested　with　the　log-rank　test.　The　results　from　this　study　demonstrated　that　on　the　basis　of　TCGA　samples,　11　genes　and　the　grade　were　selected　as　the　input　features.　In　the　training　set,　the　out-of-bag　(OOB)　error　of　RF　model-1,　which　was　established　using　the　11　genes＇,　was　0.15;　the　OOB　error　of　RF　model-2,　which　was　established　using　the　grade＇,　was　0.39;　and　the　OOB　error　of　RF　model-3,　established　using　the　11　genes　and　grade＇,　was　0.15.　In　the　testing　set,　the　classification　accuracy　of　RF　model-1,　model-2　and　model-3　was　71.43,　66.67　and　80.95%,　respectively.　In　conclusion,　to　the　best　of　our　knowledge,　the　VSURF　was　used　to　select　features　relevant　to　EEA　prognosis,　and　an　EEA　predictive　model　combining　genes　and　traditional　features　was　established　for　the　first　time　in　the　present　study.　The　prediction　accuracy　of　the　RF　model　on　the　basis　of　the　11　genes　and　grade　was　markedly　higher　than　that　of　the　RF　models　established　by　either　the　11　genes　or　grade　alone.