Molecular Characterization, Clinical Significance, Tumor Microenvironment Association and Drug Susceptibility of DNA Methylation-Driven Genes in Renal Cell Carcinoma

Abstract

Background: Accumulating evidence suggests that DNA methylation has essential roles in the development of renal cell carcinoma (RCC). Aberrant DNA methylation acts as a vital role in RCC progression through regulating gene expression, yet little is known about the role of methylation and its association with prognosis in RCC. The purpose of this study is to explore the DNA methylation-driven genes for establishing prognostic-related molecular clusters and providing a basis for survival prediction.Methods: Differentially expressed genes (DEGs) were calculated by the “limma” R package. DNA methylation-driven genes (MDGs) were identified using the “methylMix” R package. The differentially expressed DNA methylation-driven genes (DEMDGs) were obtained by intersecting MDGs and DEGs. RCC data sets were divided into two groups using “ConsensusClusterPlus” R packet. The prognostic model was constructed based on multivariate Cox regression analysis. Based on the optimal cut-off value of the risk score, the patients were divided into high-risk group and low-risk group. The potential molecular mechanisms of prognostic-related DEMDGs were detected by GO, KEGG and GSEA analysis.Results: In this study, 146 DEMDGs were selected and two clusters were distinguished by consensus clustering. We further evaluated the immune status of two clusters and selected 106 DEGs in cluster 1. Functional enrichment analysis of 106 DEGs and cluster-based immune status analysis provided new insights for the development of RCC. The prognostic model based on 17 DEMDGs was constructed to predict prognosis of RCC patients. The predictive nomogram and the web-based survival rate calculator (http://127.0.0.1:7634/) were built to validate predictive accuracy of prognostic model. Furthermore, the risk scores were strongly associated with clinical features, immune status and drug susceptibility.Conclusion: Novel prognosis-related clusters based on the 146 DEMDGs were constructed and might contribute to precision medicine development. The 17 DEMDGs were utilized to construct a prognostic model for RCC prediction, which was significantly correlated with prognosis, immune infiltration, clinical features and drug sensitivity.