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Clear cell renal cell carcinoma (ccRCC) is characterized by a high incidence and mortality rate. Despite advancements in therapeutic interventions, the prognosis for renal cancer patients remains suboptimal. Of late, methylation modifications have emerged as promising molecular targets for tumor assessment and treatment, yet their potential has not been fully investigated in the context of ccRCC. Transcriptomic and clinical data were extracted from The Cancer Genome Atlas, Gene Expression Omnibus, and ArrayExpress databases, leading to the identification of 57 methylation-related genes (MRGs). Utilizing DESeq2 analysis, Cox regression analysis, and the LASSO regression algorithm, a Methylation-Related Risk Score (MARS) was constructed. Cluster analysis, Gene Ontology (GO) analysis, clinical feature analysis, immune infiltration analysis, and mutation analysis were further employed to evaluate the model. Our investigation identified six pivotal prognostic MRGs and established a risk score predicated on m6A/m5C/m1A/m7G regulatory factors. This score was validated across two external cohorts and can be utilized to assess individual immune infiltration statuses and predict responses to immunotherapy. Moreover, cluster analysis delineated two distinct m6A/m5C/m1A/m7G gene clusters. We have developed and validated a robust prognostic signature based on genes associated with m6A, m5C, m1A, and m7G modifications. This gene signature demonstrates significant prognostic value in assessing survival outcomes, clinical characteristics, immune infiltration, and responses to immunotherapy in ccRCC patients. This finding provides valuable insights for refining precision treatment strategies.
He et al. (Mon,) studied this question.
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