Construction of a risk model based on m5C-associated lncRNAs to predict the prognosis in renal cell carcinoma

Renal cell carcinoma (RCC) is one of the most common tumors of the urinary system, and its outcomes vary widely among individuals, primarily due to different molecular characteristics. Both 5-methylcytosine (m5C) methylation and long noncoding RNAs (lncRNAs) play crucial roles in the epigenetics of RCC and may serve as biomarkers for predicting prognosis. Clinical information and transcriptome data of patients with RCC were extracted from the The Cancer Genome Atlas database. Using least absolute shrinkage and selection operator analysis and multivariate Cox regression, m5C-related lncRNAs were filtered. We then built a prognostic prediction model based on m5C-related lncRNAs. The model was analyzed for its predictive role in overall survival (OS) and response to targeted and immunotherapeutic treatments. We selected 3 lncRNAs, HM13-IT1, COLCA1, and AC010285.3, to construct a predictive model that categorizes patients into high-risk and low-risk groups. The results indicated that the high-risk group exhibited a significantly poorer OS than the low-risk group, and upon validation, it was identified as an independent risk factor. Through gene ontology enrichment analysis, this model was found to be closely associated with tumor immune function. The high-risk group showed higher tumor mutation burden and tumor immune dysfunction and exclusion scores, suggesting poorer response to immunotherapy. Additionally, the high-risk group exhibited reduced responsiveness to sorafenib. The predictive model for RCC can accurately forecast the prognosis of RCC, offering new tools for personalized diagnosis and treatment of individual patients.