Department of Obstetrics and Gynecology, The Second Hospital of Dalian Medical University, Dalian, Liaoning, China.
State Development of Key Laboratory of Translational Medicine and Innovative Drug, Jiangsu Simcere Diagnostics Co., Ltd., Nanjing, Jiangsu, China.
Medicine (Baltimore). 2022 Oct 14;101(41):e30974. doi: 10.1097/MD.0000000000030974.
To develop an effective uterine corpus endometrial carcinoma (UCEC) risk assessment tool to monitor treatment outcomes. Limma package was used to analyze differentially expressed microRNAs (miRNAs) between UCEC tissues and normal tissues in the TCGA database. According to univariate Cox risk regression, least absolute shrinkage, and selection operator (LASSO) Cox analysis were performed to screen prognostic miRNAs and construct a risk scoring model. The prognostic performance of signature was evaluated by Kaplan-Meier and receiver operating characteristic. Multivariate Cox regression analysis was used to determine the independent prognostic factors of UCEC. Nomogram was constructed according to age, clinical stage, and risk score. A 4-miRNA signature based on miR-31-5p, miR-34a-5p, miR-26a-1-3p and miR-4772-3p was established. Risk scores of each patient were calculated by the 4-miRNA signature. After z-score, the patients were divided into high- and low-risk groups. The overall survival of high-risk patients was significantly shorter than that of low-risk patients, pointing to the high performance and independence of the 4-miRNA signature in predicting UCEC prognosis. The nomogram showed a high accuracy in predicting overall survival of UCEC patients. We developed a 4-miRNA signature that could effectively predict the prognosis of UCEC.
为了开发一种有效的子宫体子宫内膜癌（UCEC）风险评估工具来监测治疗效果。使用 Limma 包分析 TCGA 数据库中 UCEC 组织与正常组织之间差异表达的 microRNAs（miRNAs）。根据单变量 Cox 风险回归、最小绝对收缩和选择算子（LASSO）Cox 分析筛选预后 miRNAs，并构建风险评分模型。通过 Kaplan-Meier 和接收者操作特征评估特征的预后性能。多变量 Cox 回归分析用于确定 UCEC 的独立预后因素。根据年龄、临床分期和风险评分构建列线图。基于 miR-31-5p、miR-34a-5p、miR-26a-1-3p 和 miR-4772-3p 建立了一个 4-miRNA signature。通过 4-miRNA signature 计算每个患者的风险评分。经过 z-score 后，患者被分为高风险组和低风险组。高风险患者的总生存率明显短于低风险患者，表明 4-miRNA signature 在预测 UCEC 预后方面具有较高的性能和独立性。列线图显示了预测 UCEC 患者总生存率的高准确性。我们开发了一种 4-miRNA signature，可以有效地预测 UCEC 的预后。
