Zhou Hongmin, Xie Tiancheng, Gao Yuchen, Zhan Xiangcheng, Dong Yunze, Liu Ding, Xu Yunfei
Department of Urology, Shanghai 10th People's Hospital, Tongji University School of Medicine, Shanghai, China.
Department of Urology, Shanghai 10th People's Hospital, Nanjing Medical University, Shanghai, China.
Front Genet. 2022 Oct 18;13:996291. doi: 10.3389/fgene.2022.996291. eCollection 2022.
Clear cell renal cell carcinoma (ccRCC) is a lethal urological malignancy. DNA methylation is involved in the regulation of ccRCC occurrence and progression. This study aimed to establish a prognostic model based on DNA methylation to predict the overall survival (OS) of patients with ccRCC. To create this model, we used the transcriptome and DNA methylation data of patients with ccRCC from The Cancer Genome Atlas (TCGA) database. We then used the MethylMix R package to identify methylation-driven genes, and LASSO regression and multivariate Cox regression analyses established the prognostic risk model, from which we derived risk scores. We incorporated these risk scores and clinical parameters to develop a prognostic nomogram to predict 3-, 5-, and 7-year overall survival, and its predictive power was validated using the ArrayExpress cohort. These analyses identified six methylation-driven genes (, , , , , and ) that produced risk scores, which were sorted into high- and low-risk patient groups. These two groups differed in nomogram-predicted prognosis, the extent of immune cell infiltration, tumor mutational burden, and expected response to additional therapies. In conclusion, we established a nomogram based on six DNA methylation-driven genes with excellent accuracy for prognostic prediction in ccRCC patients. This nomogram model might provide novel insights into the epigenetic mechanism and individualized treatment of ccRCC.
透明细胞肾细胞癌(ccRCC)是一种致命的泌尿系统恶性肿瘤。DNA甲基化参与ccRCC的发生和进展调控。本研究旨在建立基于DNA甲基化的预后模型,以预测ccRCC患者的总生存期(OS)。为创建该模型,我们使用了来自癌症基因组图谱(TCGA)数据库的ccRCC患者的转录组和DNA甲基化数据。然后我们使用MethylMix R软件包来识别甲基化驱动基因,通过LASSO回归和多变量Cox回归分析建立预后风险模型,并从中得出风险评分。我们将这些风险评分与临床参数相结合,开发了一种预后列线图,以预测3年、5年和7年总生存期,并使用ArrayExpress队列验证了其预测能力。这些分析确定了六个产生风险评分的甲基化驱动基因(、、、、和),并将患者分为高风险和低风险组。这两组在列线图预测的预后、免疫细胞浸润程度、肿瘤突变负担以及对其他治疗的预期反应方面存在差异。总之,我们基于六个DNA甲基化驱动基因建立了列线图,对ccRCC患者的预后预测具有出色的准确性。该列线图模型可能为ccRCC的表观遗传机制和个体化治疗提供新的见解。