Comprehensive Analysis of m5C RNA Methylation Regulator Genes in Clear Cell Renal Cell Carcinoma

• Main Authors: Jiajin Wu, Chao Hou, Yuhao Wang, Zhongyuan Wang, Pu Li, Zengjun Wang
• Format: Article
• Language: English
• Published: Hindawi Limited 2021-01-01
• Series: International Journal of Genomics
• Online Access: http://dx.doi.org/10.1155/2021/3803724

Background. Recent research found that N5-methylcytosine (m5C) was involved in the development and occurrence of numerous cancers. However, the function and mechanism of m5C RNA methylation regulators in clear cell renal cell carcinoma (ccRCC) remains undiscovered. This study is aimed at investigating the predictive and clinical value of these m5C-related genes in ccRCC. Methods. Based on The Cancer Genome Atlas (TCGA) database, the expression patterns of twelve m5C regulators and matched clinicopathological characteristics were downloaded and analyzed. To reveal the relationships between the expression levels of m5C-related genes and the prognosis value in ccRCC, consensus clustering analysis was carried out. By univariate Cox analysis and last absolute shrinkage and selection operator (LASSO) Cox regression algorithm, a m5C-related risk signature was constructed in the training group and further validated in the testing group and the entire cohort. Then, the predictive ability of survival of this m5C-related risk signature was analyzed by Cox regression analysis and nomogram. Functional annotation and single-sample Gene Set Enrichment Analysis (ssGSEA) were applied to further explore the biological function and potential signaling pathways. Furthermore, we performed qRT-PCR experiments and measured global m5C RNA methylation level to validate this signature in vitro and tissue samples. Results. In the TCGA-KIRC cohort, we found significant differences in the expression of m5C RNA methylation-related genes between ccRCC tissues and normal kidney tissues. Consensus cluster analysis was conducted to separate patients into two m5C RNA methylation subtypes. Significantly better outcomes were observed in ccRCC patients in cluster 1 than in cluster 2. m5C RNA methylation-related risk score was calculated to evaluate the prognosis of ccRCC patients by seven screened m5C RNA methylation regulators (NOP2, NSUN2, NSUN3, NSUN4, NSUN5, TET2, and DNMT3B) in the training cohort. The AUC for the 1-, 2-, and 3-year survival in the training cohort were 0.792, 0.675, and 0.709, respectively, indicating that the risk signature had an excellent prognosis prediction in ccRCC. Additionally, univariate and multivariate Cox regression analyses revealed that the risk signature could be an independent prognostic factor in ccRCC. The results of ssGSEA suggested that the immune cells with different infiltration degrees between the high-risk and low-risk groups were T cells including follicular helper T cells, Th1_cells, Th2_cells, and CD8+_T_cells, and the main differences in immune-related functions between the two groups were the interferon response and T cell costimulation. In addition, qRT-PCR experiments confirmed our results in renal cell lines and tissue samples. Conclusions. According to the seven selected regulatory factors of m5C RNA methylation, a risk signature associated with m5C methylation that can independently predict prognosis in patients with ccRCC was developed and further verified the predictive efficiency.