Prognostic evaluation of the novel blueprint of DNA methylation sites by integrating bulk RNA‐sequencing and methylation modification data in endometrial cancer

Abstract Introduction Endometrial cancer (EC) is a prevalent malignancy affecting the female population, with an increasing incidence among younger age groups. DNA methylation, a common epigenetic modification, is well‐established to play a key role in cancer progression. We suspected whether DNA methylation could be used as biomarkers for EC prognosis. Methods In the present study, we analyzed bulk RNA‐sequencing data from 544 EC patients and DNA methylation data from 430 EC patients in the TCGA‐UCEC cohort. We applied weighted correlation network analysis to select a key gene set associated with panoptosis. We conducted correlation analysis between transcriptomic data of the selected key genes and DNA methylation data to identify valuable DNA methylation sites. These sites were further screened by Cox regression and least absolute shrinkage and selection operator analysis. Immune microenvironment differences between high‐risk and low‐risk groups were assessed using single‐sample gene set enrichment analysi, xCell and MCPcounter algorithms. Results Our results identified five DNA methylation sites (cg03906681, cg04549977, cg06029846, cg10043253 and cg15658376) with significant prognostic value in EC. We constructed a prognostic model using these sites, demonstrating satisfactory predictive performance. The low‐risk group showed higher immune cell infiltration. Notably, methylation of site cg03906681 was negatively related to CD8 T cell infiltration, whereas cg04549977 exhibited positive correlations with immune infiltration, particularly in macrophages, activated B cells, dendritic cells and myeloid‐derived suppressor cells. PD0325901_1060 was strongly correlated with risk scores, indicating a potential therapeutic response for high‐risk EC patients. Conclusion We have developed a robust DNA methylation‐based prognostic model for EC, which holds promise for improving prognosis prediction and personalized treatment approaches. These findings may contribute to better management of EC patients, particularly in identifying those at higher risk who may benefit from tailored interventions.