DNA methylation expression patterns predict outcome of clear cell renal cell carcinoma

Objective To identify DNA methylation markers related to clear cell renal cell carcinoma (ccRCC) prognosis and construct a prognostic model. Methods Methylation data from TCGA and GSE113501 dataset were analyzed. Differential analysis, univariate Cox regression, and LASSO regression were used to find survival - related CpG sites and build a risk score model. The model was evaluated by the area under the curve, and multivariate analysis determined risk factors. Results We determined 13 CpGs that are significantly associated with prognosis through a series of regression analyses and established a risk model based on them. Patients were divided into a high-risk group and a low-risk group according to the median risk score. The results showed that there was a significant difference in the overall survival rate between the two groups (p < 0.001), and the area under the curve (AUC) of the model was greater than 0.8. Verified by the GSE113501 dataset, the model performed well in distinguishing ccRCC with different progression states. In addition, by combining methylation data with gene expression analysis, five methylation-related differentially expressed genes (LINC02541, SLAMF8, LPXN, LGALS12, EGFR) were identified, and their expression levels were significantly upregulated in tumor tissues. Multivariate analysis indicated that age, clinical stage, and methylation risk score were independent prognostic factors. Conclusion This study confirmed that DNA methylation markers can effectively predict the progression and prognosis of clear cell renal cell carcinoma (ccRCC), providing a highly efficient and minimally invasive assessment tool for clinical practice.