Data_Sheet_4_Integrative Analysis of DNA Methylation Identified 12 Signature Genes Specific to Metastatic ccRCC.PDF

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.

背景：异常的表观遗传学改变可能促进人类恶性肿瘤的发展。对这些改变进行识别，以实现透明细胞肾细胞癌（ccRCC）的早期筛查和预后评估，一直是研究的热点目标。基于生物信息的DNA甲基化数据分析为发现表观遗传学生物标志物提供了广阔的前景。然而，对于ccRCC中甲基化驱动基因的探索尚显不足。方法：从基因表达综合数据库（GEO）中获取了转移性ccRCC的基因表达数据和DNA甲基化数据。筛选了在5′-C-磷酸-G-3′（CpG）位点上的差异甲基化基因（DMGs）和差异表达基因（DEGs），并对DMGs和DEGs中的重叠基因进行了基因集富集分析。随后，利用加权基因共表达网络分析（WGCNA）寻找与ccRCC相关的枢纽DMGs。通过Cox回归分析和ROC分析筛选潜在生物标志物，并基于筛选出的枢纽基因建立预后模型。结果：从两个独立的GEO数据集中获得了314个重叠的DMGs。青绿色模块包含79个枢纽DMGs，这是WGCNA筛选出的最显著模块。此外，通过多变量Cox回归分析，在TCGA数据库中确定了12个枢纽基因（CETN3、DCAF7、GPX4、HNRNPA0、NUP54、SERPINB1、STARD5、TRIM52、C4orf3、C12orf51和C17orf65）。然后，使用所有12个基因构建了ccRCC的诊断和预后模型。ROC分析显示，这些基因对转移性和非转移性ccRCC具有良好的诊断效率。此外，基于12个甲基化驱动基因的预后模型对ccRCC患者的5年生存率具有良好的预测能力。结论：整合DNA甲基化数据分析确定了12个特征基因，这些基因可以作为ccRCC预后的表观遗传学生物标志物。该预后模型对ccRCC患者的5年生存率具有良好的预测效果。