Abstract
BACKGROUND: This study aimed to identify novel methyla-tion-based prognostic biomarkers for endometrial cancer (EC) to facilitate early diagnosis and treatment. To explore methylation-related prognostic markers in endometrial tis-sue by analyzing TCGA data and to establish a methylation-based risk model for EC patients. METHODS: We systematically analyzed methylation-related gene expression and prognostic significance in 409 EC patients using TCGA DNA methylation data. DNA methy-lation biomarkers were identified through consensus clus-tering and weighted gene co-expression network analysis (WGCNA). The clusterProfiler algorithm was employed to determine key signaling pathways across different sub-groups. A gene panel targeting critical DNA methylation sites was subsequently constructed. RESULTS: A methylation-related prognostic risk model was developed, incorporating five CpG sites: cg01416891, cg00082235, cg01493517,cg03811891,and cg05317207. The model demonstrated strong predictive performance, with high-risk patients exhibiting significantly poorer prog-noses compared to low-risk patients. A gene panel was also established to predict prognosis across different EC risk groups. CONCLUSIONS: The methylation-related gene panel model serves as a reliable prognostic biomarker for EC, offering potential for enhanced early diagnosis and personalized treatment strategies.