Table_14_Integrative Analysis of DNA Methylation Identified 12 Signature Genes Specific to Metastatic ccRCC.XLSX

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：异常表观遗传改变可促进人类恶性肿瘤的发生与发展。鉴定此类改变以用于透明细胞肾细胞癌（clear cell renal cell carcinoma, ccRCC）的早期筛查与预后评估，一直是学界备受关注的研究目标。DNA甲基化数据的生物信息学分析为表观遗传生物标志物的发现提供了广阔前景。然而，目前针对ccRCC的甲基化驱动基因的相关探索仍较为匮乏。
方法：本研究从基因表达综合数据库（Gene Expression Omnibus, GEO）获取转移性ccRCC的基因表达数据与DNA甲基化数据。筛选5′-C-磷酸-G-3′（CpG）位点的差异甲基化基因（differentially methylated genes, DMGs）与差异表达基因（differentially expressed genes, DEGs），并将DMGs与DEGs的交集基因进行基因集富集分析。随后，采用加权基因共表达网络分析（Weighted Gene Co-expression Network Analysis, WGCNA）筛选与ccRCC相关的核心差异甲基化基因。通过Cox回归与受试者工作特征（Receiver Operating Characteristic, ROC）分析筛选潜在生物标志物，并基于筛选得到的核心基因构建预后模型。
结果：从两个独立的GEO数据集中共获得314个交集差异甲基化基因。通过WGCNA筛选得到的最显著模块为绿松石模块，共包含79个核心差异甲基化基因。进一步通过多变量Cox回归分析，在癌症基因组图谱（The Cancer Genome Atlas, TCGA）数据库中鉴定出12个核心基因：CETN3、DCAF7、GPX4、HNRNPA0、NUP54、SERPINB1、STARD5、TRIM52、C4orf3、C12orf51及C17orf65。利用上述12个基因构建用于ccRCC诊断与预后评估的模型。ROC分析显示，该基因集对转移性与非转移性ccRCC均表现出良好的诊断效能。此外，基于这12个甲基化驱动基因构建的预后模型可较好地预测ccRCC患者的5年生存率。
结论：本研究通过DNA甲基化数据的整合分析鉴定出12个特征基因，可作为转移性ccRCC预后评估的表观遗传生物标志物。该预后模型可较好地预测ccRCC患者的5年生存率。