Identification and validation of a DNA methylation-driven gene-based prognostic model for clear cell renal cell carcinoma
Background: Clear cell renal cell carcinoma (ccRCC) is a malignant tumor with heterogeneous morphology that affects adults and is associated with generally poor prognosis. This study aimed to establish a DNA methylation-driven gene-based prognostic model for ccRCC.Methods: Reduced representation bisulfite sequencing was performed on the DNA extracts from the ccRCC patients to identify differentially methylated regions (DMRs).Results: We identified 2262 DMRs in the promoter region. After DMR selection, 578 candidates were screened, and there was correspondence with 408 cytosine-phosphate-guanine (CpG) dinucleotides in the 450K array. We collected the DNA methylation profiles of 478 ccRCC samples from TCGA data set. Using the training set with 319 samples, a prognostic panel of 18 CpGs was determined by univariate Cox regression, least absolute shrinkage and selection operator regression, and multivariate Cox proportional hazards regression analyses. Combined with the clinical signatures, we constructed a prognostic model. In the test set (159 samples) and whole set (478 samples), the Kaplan-Meier plot showed significant differences, the receiver operating characteristic curve and survival analyses showed areas under the curve (AUC) all greater than 0.7. The nomogram, including the prognostic model and the clinical factors, showed promising prognostic value.Conclusions: This work provides insight into the role of hypermethylation in ccRCC. These targets might serve as biomarkers for early ccRCC diagnosis and prognosis and as potential targets for ccRCC therapy.