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

doi:10.3389/fonc.2020.556018

Integrative Analysis of DNA Methylation Identified 12 Signature Genes Specific to Metastatic ccRCC

Front Oncol. 2020 Oct 8:10:556018. doi: 10.3389/fonc.2020.556018. eCollection 2020.

Authors

Siwei Qian¹, Si Sun¹, Lei Zhang^{1

2}, Shengwei Tian¹, Kai Xu³, Guangyuan Zhang^{1

2}, Ming Chen^{1

2}

Affiliations

¹ Department of Urology, Zhongda Hospital, Southeast University, Nanjing, China.
² Institute of Urology, School of Medicine, Southeast University, Nanjing, China.
³ Department of Urology, Changzhou No. 2 People's Hospital, Changzhou, China.

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 then subject to 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 performed to screen potential biomarkers and develop a prognostic model based on the screened hub genes. Results: Three hundred and fourteen overlapping DMGs were obtained from two independent GEO datasets. The turquoise module contained 79 hub DMGs, 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 analyses. All the 12 genes were then used to generate the model for diagnosis and prognosis of ccRCC. ROC analysis showed that these genes exhibited good diagnostic efficiency for metastatic and non-metastatic ccRCC. Furthermore, the prognostic model with the 12 methylation-driven genes demonstrated a good prediction of 5-year survival rates for ccRCC patients. Conclusion: Integrative analysis of DNA methylation data identified 12 signature genes, which could be used as epigenetic biomarkers for prognosis of metastatic ccRCC. This prognostic model has a good prediction of 5-year survival for ccRCC patients.

Keywords: DNA methylation; ROC (receiver operating characteristic curve); clear cell renal cell carcinoma; data integration; gene expresssion.