Development of Potential Prognostic Biomarkers Based on DNA Methylation-Driven Genes for Patients with Endometrial Cancer

Yiling Lu,1,* Weichun Tang,2,3,* Xiaoyu Wang,1 Xinyi Kang,1 Jun You,1 Liping Chen1 1Department of Obstetrics and Gynecology, Nantong First People’s Hospital, Nantong, Jiangsu, 226001, People’s Republic of China; 2NHC Key Laboratory of Antibody Technique, Nanjing Medical University, Nanjing, Jiangsu, 211166, People’s Republic of China; 3Department of Gynecology and Obstetrics, The Second Affiliated Hospital of Nantong University, Nantong, Jiangsu, 226001, People’s Republic of China*These authors contributed equally to this workCorrespondence: Liping ChenDepartment of Gynecology and Obstetrics, Nantong First People’s Hospital, Nantong, Jiangsu, 226001, People’s Republic of ChinaEmail 42770899@qq.comBackground: Endometrial cancer (EC) is a multifactorial disease, and its progression may be driven by abnormal genetic methylation. To clarify the underlying molecular mechanisms and sensitive biomarkers for EC, this study used an integrated bioinformatic analysis to explore the methylation-driven genes of EC.Methods: The mRNA expression data, methylation data and corresponding clinical information of EC samples were downloaded from The Cancer Genome Atlas (TCGA) database. MethylMix algorithm was used to screen out methylation-driven genes in EC. Functional and pathway enrichment analysis and the protein–protein interaction (PPI) analysis were conducted to demonstrate the functions and interactions between these genes. Then, prognosis-related methylated genes were screened out by using univariate and multivariate Cox analyses, and a prognostic risk assessment model for EC was constructed. The methylation sites and expression profiles of candidate genes were further investigated.Results: A total of 127 methylated genes were identified in EC. Four genes (RP11-968O1.5, DCAF12L1, MSX1 and ALS2CR11) were selected as candidate genes to construct a reliable prognostic risk model. The univariate and multivariate Cox proportional hazards regression analyses showed that the risk score based on four genes was an independent prognostic indicator for OS among EC patients. A nomogram was established and the calibration plot analysis indicated the good performance and clinical utility of the nomogram. In addition, the methylation and expression of MSX1 and DCAF12L1 were significantly associated with EC survival rate. The joint ROC analysis revealed that the AUC of DCAF12L1-MSX1 was 0.867, which suggested both have a good EC-diagnosing efficiency. We then coped DCAF12L1 and MSX1 with GESA analysis, finding both were mainly associated with the KRAS signaling pathway.Conclusion: This bioinformatic study combs the methylated genes involved in EC development for the first time, finding that MSX1 and DCAF12L1 could serve as EC prognostic markers and drug targets.Keywords: endometrial cancer, methylation-driven genes, integrated bioinformatic analysis, prognosis, biomarkers