Department of Urology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
Department of Urology, Shaoxing People's Hospital, Shaoxing, China.
J Clin Lab Anal. 2021 Dec;35(12):e24031. doi: 10.1002/jcla.24031. Epub 2021 Oct 30.
In this study, we aimed to screen methylation signatures associated with the prognosis of patients with clear cell renal cell carcinoma (ccRCC).
Gene expression and methylation profiles of ccRCC patients were downloaded from publicly available databases, and differentially expressed genes (DEGs)-differentially methylated genes (DMGs) were obtained. Subsequently, gene set enrichment and transcription factor (TF) regulatory network analyses were performed. In addition, a prognostic model was constructed and the relationship between disease progression and immunity was analyzed.
A total of 23 common DEGs-DMGs were analyzed, among which 14 DEGs-DMGs were obtained with a cutoff value of PCC < 0 and p < 0.05. The enrichment analysis showed that the 14 DEGs-DMGs were enriched in three GO terms and three KEGG pathways. In addition, a total of six TFs were shown to be associated with the 14 DEGs-DMGs, including RP58, SOX9, NF-κB65, ATF6, OCT, and IK2. A prognostic model using five optimized DEGs-DMGs which efficiently predicted survival was constructed and validated using the GSE105288 dataset. Additionally, four types of immune cells (NK cells, macrophages, neutrophils, and cancer-associated fibroblasts), as well as ESTIMATE, immune, and stromal scores were found to be significantly correlated with ccRCC progression (normal, primary, and metastasis) in addition to the five optimized DEGs-DMGs.
A five-gene methylation signature with the predictive ability for ccRCC prognosis was investigated in this study, consisting of CCNB2, CDKN1C, CTSH, E2F2, and ERMP1. In addition, potential targets for methylation-mediated immunotherapy were highlighted.
本研究旨在筛选与透明细胞肾细胞癌（ccRCC）患者预后相关的甲基化特征。
从公开数据库中下载 ccRCC 患者的基因表达和甲基化谱，获得差异表达基因（DEGs）-差异甲基化基因（DMGs）。随后进行基因集富集和转录因子（TF）调控网络分析。此外，构建了一个预后模型，并分析了疾病进展与免疫的关系。
共分析了 23 个共同的 DEGs-DMGs，其中有 14 个 DEGs-DMGs 的 PCC<0 和 p<0.05。富集分析显示，这 14 个 DEGs-DMGs 富集在三个 GO 术语和三个 KEGG 通路中。此外，共有 6 个 TF 与这 14 个 DEGs-DMGs 相关，包括 RP58、SOX9、NF-κB65、ATF6、OCT 和 IK2。使用五个优化的 DEGs-DMGs 构建了一个能够有效预测生存的预后模型，并使用 GSE105288 数据集进行验证。此外，除了五个优化的 DEGs-DMGs 之外，四种免疫细胞（NK 细胞、巨噬细胞、中性粒细胞和癌症相关成纤维细胞）、ESTIMATE、免疫和基质评分与 ccRCC 进展（正常、原发性和转移性）显著相关。
本研究探讨了一种具有预测 ccRCC 预后能力的五基因甲基化特征，由 CCNB2、CDKN1C、CTSH、E2F2 和 ERMP1 组成。此外，还强调了潜在的用于甲基化介导免疫治疗的靶标。
