Comprehensive Analysis of m5C Methylation Regulatory Genes and Tumor Microenvironment in Prostate Cancer - Yu, Guopeng; Bao, Jiahao; Zhan, Ming; Wang, Jiangyi; Li, Xinjuan; Gu, Xin; Song, Shangqing; Yang, Qing; Liu, Yushan; Wang, Zhong; Xu, Bin (Frontiers in Immunology, 2022)
Background: 5-Methylcytidine (m5C) methylation is an emerging epigenetic modification in recent years, which is associated with the development and progression of various cancers. However, the prognostic value of m5C regulatory genes and the correlation between m5C methylation and tumor microenvironment (TME) in prostate cancer remains unknown. Methods: In current study, genetic and transcriptional alterations and prognostic value of m5C regulatory genes were investigated in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Then, m5C prognostic model was established by LASSO Cox regression analysis. Gene Set Variation Analysis (GSVA), Gene Set Enrichment Analysis (GSEA), clinical relevance and TME analysis was conducted to explain the biological functions and quantify TME scores between high-risk and low-risk subgroups. m5C regulatory gene clusters and m5C immune subtypes were identified using consensus unsupervised clustering analysis. CIBERSORT algorithm were used to calculate contents of immune cells. Results: TET3 was upregulated at transcriptional levels in PCa compared with normal tissues and high TET3 expression was associated with poor prognosis. m5C prognostic model consisting 3 genes (NSUN2, TET3 and YBX1) was developed and a nomogram was constructed for improving the clinical applicability of the model. Functional analysis revealed the enrichment of pathways and biological process associated with RNA regulation and immune function. Significant differences were also found in m5C regulatory genes’ expression levels, TME scores and immune cell infiltration levels between different risk subgroups. We identified two distinct m5C gene clusters and found its correlation with patient prognosis and immune cell infiltration characteristics. Naive B cells, CD8+ T cells, M1 Macrophages and M2 Macrophages were obtained and 2 m5C immune subtypes were identified. CTLA4, NSUN6, TET1 and TET3 were differentially expressed between immune subtypes. The expression of CTLA4 was found to be correlated with the degree of immune cell infiltration. Conclusions: Our comprehensive analysis of m5C regulatory genes in PCa demonstrated their potential roles in the prognosis, clinical features and TME. These findings may improve our understanding of m5C regulatory genes in tumor biology of PCa.