DNA Methylation Modification Regulator-Mediated Molecular Clusters and Tumor Metabolic Characterization in Prostate Cancer

Abstract

Background. An increasing number of studies have indicated a close link between DNA methylation and tumor metabolism. However, the overall influence of DNA methylation on tumor metabolic characteristics in prostate cancer (PCa) remains unclear. Methods. We first explored the subtypes of DNA methylation modification regulators and tumor metabolic features of 1,205 PCa samples using clustering analysis and gene set variation analysis based on the mRNA levels of DNA methylation modification regulators. A DNA methylation-related score (DMS) was calculated using principal component analysis and the DNA methylation modification-related gene signatures to quantify DNA methylation characteristics. We then performed a meta-analysis to identify the hazard ratio of DMS in the six cohorts. In addition, a nomogram was drawn using univariate and multivariate Cox analyses based on the DMS and clinical variables. Finally, a drug sensitivity analysis of the DMS was performed based on the genomics of drug sensitivity in cancer datasets. Results. Three PCa clusters showing different DNA methylation modification patterns and tumor metabolic features were identified. A DMS system was established to quantify the characteristics of DNA methylation modification. PCa samples showed a differential metabolic landscape between the high and low DMS groups. The prognostic value of the DMS and nomogram was independently validated in multiple cohorts. A high DMS was associated with increases in the tumor mutation burden, copy number variation, and microsatellite instability; high tumor heterogeneity; and poor prognosis. Finally, DMS was closely related to different types of antitumor treatment. Conclusion. Improving the understanding of tumor metabolism by characterizing DNA methylation modification patterns and using the DMS may help clinicians predict prognosis and aid in more personalized antitumor therapy strategies for PCa.

Clinical significance of cyclin-dependent kinase inhibitor 2C expression in cancers: from small cell lung carcinoma to pan-cancers

“…The motif data of predicted TFs were obtained from JASPAR, and the seqlogo was drawn based on the “ggseqlogo” package. By using the “clusterProfiler” package [ 30 – 33 ], enrichment analyses were performed to explore the gene ontology terms and signaling pathways (Kyoto Encyclopedia of Genes and Genomes) of CDKN2C in SCLC. Gene ontology terms and signaling pathways with adjusted p values < 0.05 were selected in the study.…”