Deng Fang, Mu Jing, Qu Chiwen, Yang Fang, Liu Xing, Zeng Xiaomin, Peng Xiaoning
Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China.
School of Mathematics and Statistics, Hunan Normal University, Changsha, China.
Front Mol Biosci. 2021 Jan 7;7:587822. doi: 10.3389/fmolb.2020.587822. eCollection 2020.
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), = 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.
由于仅通过临床变量难以预测子宫内膜癌（EC）患者的预后，本研究旨在构建一个整合临床和分子信息的新型EC预后模型，以提高EC预后预测的准确性。利用TCGA数据库中496例EC患者的临床和基因表达数据来建立并验证该模型。应用广义Cox回归分析临床变量和RNA。采用弹性网络惩罚的Cox比例风险回归来选择与EC预后最佳相关的RNA，并使用岭回归构建EC预后模型。通过Kaplan-Meier曲线和受试者工作特征曲线下面积（AUC-ROC）评估预后模型的预测能力。建立了一个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，高于单纯临床预后模型（0.897）或单纯RNA预后模型（0.836）。该临床-RNA预后模型能够更好地对EC患者的预后风险进行分类。在训练组（396例患者）中，高危组EC患者的总生存率低于低危组[HR = 32.263，（95%CI，7.707 - 135.058），P = 8e-14]。在验证组中也观察到了相同的比较结果。建立了一个整合临床变量和RNA的新型EC预后模型，其能够更好地预测预后并有助于改善EC患者的临床管理。
