Integrative Analysis of DNA Methylation Identified 12 Signature Genes Specific to Metastatic ccRCC
Siwei Qian, Si Sun, Lei Zhang, Shengwei Tian, Kai Xu, Guangyuan Zhang, Ming Chen
Abstract
Background: Abnormal epigenetic alterations can contribute to the development of human malignancies. Identification of these alterations for early screening and prognosis of clear cell renal cell carcinoma (ccRCC) has been a highly sought-after goal. Bioinformatic analysis of DNA methylation data provides broad prospects for discovery of epigenetic biomarkers. However, there is short of exploration of methylation-driven genes of ccRCC. Methods: Gene expression data and DNA methylation data in metastatic ccRCC were sourced from the Gene Expression Omnibus (GEO) database. Differentially methylated genes (DMGs) at 5′-C-phosphate-G- 3′ (CpG) sites and differentially expressed genes (DEGs) were screened and the overlapping genes in DMGs and DEGs were further assessed with gene set enrichment analysis. Next, the weighted gene co-expression network analysis (WGCNA) was used to search hub DMGs associated with ccRCC. Cox regression and ROC analyses were subsequently performed to screen for potential biomarkers to develop a prognostic model. Results: 314 overlapping DMGs were obtained from two independent GEO datasets. 79 hub DMGs were enriched in the turquoise module, which represent the most significant module screened by WGCNA. Furthermore, a total of 12 hub genes (CETN3, DCAF7, GPX4, HNRNPA0, NUP54, SERPINB1, STARD5, TRIM52, C4orf3, C12orf51, and C17orf65) were identified in the TCGA database by multivariate Cox regression analysis. All the 12 genes were then used to generate a model for diagnosis and prognosis of ccRCC. ROC analysis showed that these genes exhibited good diagnostic efficiency for metastatic and non-metastatic ccRCC. Moreover, the prognostic model demonstrated a good prediction for 5-year survival rates of ccRCC patients. Conclusion: Integrative analysis of DNA methylation data identified 12 methylation-driven signature genes, which could be used as epigenetic biomarkers for prognosis of ccRCC. This prognostic model has a good prediction for 5-year survival of ccRCC patients.