Tumor heterogeneity makes the diagnosis and treatment of endometrial cancer difficult. As an important modulator of gene expression, DNA methylation can affect tumor heterogeneity and, therefore, provide effective information for clinical treatment. In this study, we explored specific prognostic clusters based on 482 examples of endometrial cancer methylation data in the TCGA database. By analyzing 4870 CpG clusters, we distinguished three clusters with different prognostics. Differences in DNA methylation levels are associated with differences in age, grade, clinical pathological staging, and prognosis. Subsequently, we screened out 264 specific hypermethylation and hypomethylation sites and constructed a prognostic model for Bayesian network classification, which corresponded to the classification of the test set to the classification results of the train set. Since the tumor microenvironment plays a key role in determining immunotherapy responses, we conducted relevant analyses based on clusters separated from DNA methylation data to determine the immune function of each cluster. We also predicted their sensitivity to chemotherapy drugs. Specific classifications of DNA methylation may help to address the heterogeneity of previously existing molecular clusters of endometrial cancer, as well as to develop more effective, individualized treatments.
Keywords: DNA methylation; TCGA; endometrial cancer; molecular subtypes.