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Updated: Dec 13, 2025

Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients
Published on: June 16, 2017
Identification and Comprehensive Validation of a DNA Methylation-Driven Gene-Based Prognostic Model for Clear Cell
Di Zhang1,2, Yicun Wang1,2, Xiaopeng Hu1,2
1Department of Urology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
Abstract:
Clear cell renal cell carcinoma (ccRCC) is the most prevalent renal malignancy in adults with generally poor prognosis. This study aimed to establish a DNA methylation-driven gene-based prognostic model for ccRCC. We collected DNA methylation and gene expression profiles of over 1500 ccRCC samples from The Cancer Genome Atlas (TCGA) dataset, four Gene Expression Omnibus (GEO) datasets, the Genotype-Tissue Expression (GTEx) dataset, and cancer cell lines from Cancer Cell Line Encyclopedia database and performed comprehensive bioinformatics analysis. As a result, a total of 31 differentially expressed methylation-driven genes (DEMDGs) were identified. After univariate Cox regression, least absolute shrinkage and selection operator, and multivariate Cox regression analyses, four (NFE2L3, HHLA2, IFI16, and ZNF582) were finally selected to construct a risk score prognostic model. The high-risk group demonstrated significantly poor prognosis than the low-risk group did in TCGA training (hazard ratio [HR] = 3.533, p < 0.001), TCGA internal, and GEO external validation datasets. Furthermore, the nomogram, including the prognostic model and clinical factors, showed promising prognostic value (HR = 5.756, p < 0.001, and area under the curve at 1 year = 0.856). In addition, the model was found to be significantly associated with drug sensitivity of eight targeted agents. These findings provided a novel and reliable four DEMDG-based prognostic model for ccRCC.

