Endometrial hyperplasia (EH) is a precursor for endometrial cancer (EC). However, biomarkers for the progression from EH to EC and standard prognostic biomarkers for EC have not been identified. In this study, we aimed to identify key genes with prognostic significance for the progression from EH to EC. Weighted-gene correlation network analysis (WGCNA) was used to identify hub genes utilizing microarray data (GSE106191) downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified from the Uterine Corpus Endometrial Carcinoma (UCEC) dataset of The Cancer Genome Atlas database. The Limma-Voom R package was applied to detect differentially expressed genes (DEGs; mRNAs) between cancer and normal samples. Genes with |log2 (fold change [FC])| > 1.0 and p < 0.05 were considered as DEGs. Univariate and multivariate Cox regression and survival analyses were performed to identify potential prognostic genes using hub genes overlapping in the two datasets. All analyses were conducted using R Bioconductor and related packages. Through WGCNA and overlapping genes in hub modules with DEGs in the UCEC dataset, we identified 42 hub genes. The results of the univariate and multivariate Cox regression analyses revealed that four hub genes, BUB1B, NDC80, TPX2, and TTK, were independently associated with the prognosis of EC (Hazard ratio [95% confidence interval]: 0.591 [0.382–0.912], p = 0.017; 0.605 [0.371–0.986], p = 0.044; 1.678 [1.132–2.488], p = 0.01; 2.428 [1.372–4.29], p = 0.02, respectively). A nomogram was established with a risk score calculated using the four genes’ coefficients in the multivariate analysis, and tumor grade and stage had a favorable predictive value for the prognosis of EC. The survival analysis showed that the high-risk group had an unfavorable prognosis compared with the low-risk group (p < 0.0001). The receiver operating characteristic curves also indicated that the risk model had a potential predictive value of prognosis with area under the curve 0.807 at 2 years, 0.783 at 3 years, and 0.786 at 5 years. We established a four-gene signature with prognostic significance in EC using WGCNA and established a nomogram to predict the prognosis of EC.

子宫内膜增生（EH）是子宫内膜癌（EC）的前兆。然而，尚未确定从 EH 进展到 EC 的生物标志物和 EC 的标准预后生物标志物。在这项研究中，我们旨在确定对从 EH 到 EC 进展具有预后意义的关键基因。加权基因相关网络分析 (WGCNA) 用于利用从基因表达综合数据库下载的微阵列数据 (GSE106191) 来识别中心基因。从癌症基因组图谱数据库的子宫内膜癌 (UCEC) 数据集中鉴定了差异表达基因 (DEG)。Limma-Voom R 包用于检测癌症和正常样本之间的差异表达基因（DEG；mRNA）。具有 |log2 的基因（倍数变化 [FC]）| > 1.0 和磷< 0.05 被认为是 DEG。进行单变量和多变量 Cox 回归和生存分析，以使用在两个数据集中重叠的中枢基因来识别潜在的预后基因。所有分析均使用 R Bioconductor 和相关软件包进行。通过WGCNA和UCEC数据集中具有DEG的枢纽模块中的重叠基因，我们确定了42个枢纽基因。单变量和多变量 Cox 回归分析的结果显示，四个枢纽基因，BUB1B, NDC80、TPX2， 和 TTK，与 EC 的预后独立相关（危险比 [95% 置信区间]：0.591 [0.382–0.912]， 磷= 0.017; 0.605 [0.371–0.986]，磷= 0.044; 1.678 [1.132–2.488]，磷= 0.01; 2.428 [1.372–4.29]，磷= 0.02，分别）。多变量分析中使用四个基因的系数计算风险评分建立列线图，肿瘤分级和分期对EC的预后具有良好的预测价值。生存分析显示，高危组与低危组相比预后较差。磷< 0.0001)。受试者工作特征曲线还表明，风险模型具有潜在的预后预测价值，曲线下面积在 2 年时为 0.807，在 3 年时为 0.783，在 5 年时为 0.786。我们使用 WGCNA 在 EC 中建立了具有预后意义的四基因特征，并建立了列线图来预测 EC 的预后。