Construction and validation of an m6A RNA methylation regulator prognostic model for early-stage clear cell renal cell carcinoma

N6-methyladenosine (m6A) is the most common type of RNA methylation and is considered to participate in various biological and pathological processes, specifically in the regulation of tumorigenesis and metastasis. However, the exact prognostic role of m6A methylation regulators in early-stage clear cell renal cell carcinoma (ccRCC) is currently unknown. In the present study, a prognostic model consisting of m6A RNA methylation regulators in early stage ccRCC was constructed and the reliability of the signature was assessed by proteomics and immunohistochemistry. Additionally, the relationship between the prognostic model and tumor infiltrating immune cells within the tumor microenvironment was investigated. Gene mutation and RNA sequencing data of 19 m6A methylation regulators for early-stage ccRCC patients were extracted from The Cancer Genome Atlas (TCGA) database with the corresponding clinical information. Univariate and multivariate Cox regression analysis were applied to construct a prognostic model and the proteomic data as well as immunohistochemistry were used to validate the result. The correlations between the prognostic model and tumor infiltrating immune cells were assessed using Spearman's rank correlation analysis. A total of 192 early stage ccRCC gene mutation data as well as 261 RNA sequencing data with relative clinical data were extracted from the TCGA. The overall mutation frequency of the 19 m6A RNA methylation regulators was relatively low with 4.69%. The transcriptome data revealed that 11 genes were differentially expressed between cancer tissues and relatively normal tissues. Survival analysis highlighted four specific genes as having a significant influence on overall survival. An established model with four genes demonstrated the best predictability for early-stage ccRCC. After integrating clinical characteristics into the multivariate analysis, the model remained effective at predicting ccRCC prognosis. Spearman's rank analysis suggested several tumor infiltrating immune cells such as dendric cells, CD4(+) cells, CD8(+) T cells and macrophages were significantly correlated with the model. Proteomic data analysis as well as immunohistochemistry from the Human Protein Atlas showed that all the genes used to construct the model were differentially expressed between ccRCC and normal tissues. In conclusion, a novel m6A methylation regulators-based prognostic signature was established and validated with proteomics and immunohistochemistry. In addition, the model was significantly correlated with multiple infiltrating immune cells in tumor microenvironment.