Integrated Epigenomic and Transcriptomic Analysis Identifies Diagnostic Biomarkers for Endometrial Cancer

Background Endometrial cancer (EC) is one of the most common gynecological malignancies worldwide with significant heterogeneity in prognosis. DNA methylation has emerged as a critical epigenetic mechanism in cancer development and progression. This study aimed to identify methylation‐driven genes with prognostic significance in EC and develop a risk prediction model. Methods We performed integrated analysis of DNA methylation and RNA‐sequencing (RNA‐seq) data from The Cancer Genome Atlas (TCGA) to identify differentially methylated genes in EC. A risk prediction model based on four methylation‐related genes was constructed and validated. Kaplan–Meier survival analysis and time‐dependent ROC curves were used to evaluate the prognostic value of the model. Gene set enrichment analysis (GSEA) and weighted gene co‐expression network analysis (WGCNA) were conducted to explore the biological functions associated with the risk model. Additionally, quantitative real‐time PCR (qRT‐PCR) was performed to validate the expression levels of the identified genes in EC cell lines and normal endometrial cells. Results Four methylation‐related genes (V‐set domain containing T cell activation inhibitor 1 [ VTCN1 ], progesterone receptor [ PGR ], DDR2 , and TSPYL5 ) with significant prognostic value were identified. The risk prediction model potentially stratified EC patients into high‐risk and low‐risk groups with significantly different survival outcomes. The model demonstrated excellent predictive performance for survival, with AUC values of 0.813, 0.920, and 0.845, respectively. The prognostic significance of the model was maintained across various clinicopathological subgroups. GSEA revealed that the high‐risk group was significantly enriched in pathways related to tumor progression, including extracellular matrix interaction, collagen formation, and focal adhesion. WGCNA identified key gene modules significantly correlated with the risk score, with enrichment in biological functions related to angiogenesis and epithelial–mesenchymal transition (EMT). Experimental validation using qRT‐PCR confirmed that VTCN1 was significantly upregulated, whereas PGR , DDR2 , and TSPYL5 were significantly downregulated in EC cells compared to normal cells, consistent with the bioinformatics analysis. Conclusions Our study identified a four‐gene methylation signature with significant prognostic value in EC and developed a risk prediction model that potentially stratifies patients into distinct risk groups with different survival outcomes. These findings contribute to our understanding of the epigenetic landscape of EC and highlight the potential clinical utility of methylation‐based biomarkers for risk stratification and personalized treatment decisions.