BACKGROUND

Recent research found that N5-methylcytosine (mC) was involved in the development and occurrence of numerous cancers. However, the function and mechanism of mC 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 mC-related genes in ccRCC.

METHODS

Based on The Cancer Genome Atlas (TCGA) database, the expression patterns of twelve mC regulators and matched clinicopathological characteristics were downloaded and analyzed. To reveal the relationships between the expression levels of mC-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 mC-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 mC-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 mC 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 mC RNA methylation-related genes between ccRCC tissues and normal kidney tissues. Consensus cluster analysis was conducted to separate patients into two mC RNA methylation subtypes. Significantly better outcomes were observed in ccRCC patients in cluster 1 than in cluster 2. mC RNA methylation-related risk score was calculated to evaluate the prognosis of ccRCC patients by seven screened mC 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 mC RNA methylation, a risk signature associated with mC methylation that can independently predict prognosis in patients with ccRCC was developed and further verified the predictive efficiency.