Due to the difficulty in predicting the prognosis of endometrial carcinoma (EC) patients by clinical variables alone, this study aims to build a new EC prognosis model integrating clinical and molecular information, so as to improve the accuracy of predicting the prognosis of EC. The clinical and gene expression data of 496 EC patients in the TCGA database were used to establish and validate this model. General Cox regression was applied to analyze clinical variables and RNAs. Elastic net-penalized Cox proportional hazard regression was employed to select the best EC prognosis-related RNAs, and ridge regression was used to construct the EC prognostic model. The predictive ability of the prognostic model was evaluated by the Kaplan–Meier curve and the area under the receiver operating characteristic curve (AUC-ROC). A clinical-RNA prognostic model integrating two clinical variables and 28 RNAs was established. The 5-year AUC of the clinical-RNA prognostic model was 0.932, which is higher than that of the clinical-alone (0.897) or RNA-alone prognostic model (0.836). This clinical-RNA prognostic model can better classify the prognosis risk of EC patients. In the training group (396 patients), the overall survival of EC patients was lower in the high-risk group than in the low-risk group [HR = 32.263, (95% CI, 7.707–135.058), P = 8e-14]. The same comparison result was also observed for the validation group. A novel EC prognosis model integrating clinical variables and RNAs was established, which can better predict the prognosis and help to improve the clinical management of EC patients.

由于仅凭临床变量难以预测子宫内膜癌（EC）患者的预后，本研究旨在建立一个结合临床和分子信息的新的EC预后模型，以提高预测EC预后的准确性。TCGA数据库中496例EC患者的临床和基因表达数据用于建立和验证该模型。应用一般Cox回归分析临床变量和RNA。运用弹性净罚分的Cox比例风险回归法选择最佳的EC预后相关RNA，并使用ridge回归法构建EC预后模型。通过Kaplan–Meier曲线和接受者工作特征曲线（AUC-ROC）下的面积评估了预后模型的预测能力。建立了整合两个临床变量和28个RNA的临床RNA预后模型。临床RNA预后模型的5年AUC为0.932，高于单独临床预后模型（0.897）或仅单独RNA预后模型（0.836）的5年AUC。这种临床RNA预后模型可以更好地分类EC患者的预后风险。在训练组（396名患者）中，高危组的EC患者总生存率低于低危组[HR = 32.263，（95％CI，7.707–135.058），P= 8e-14]。验证组也观察到相同的比较结果。建立了整合临床变量和RNA的新型EC预后模型，可以更好地预测预后并改善EC患者的临床管理。