Shen Bingbing, Wen Zhen, Lv Gang, Wang Jianguo, Han Ruijie, Jiang Jianxin
Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, China.
Department of Anaesthesiology, Renmin Hospital of Wuhan University, Wuhan, China.
Front Genet. 2022 Oct 10;13:1022078. doi: 10.3389/fgene.2022.1022078. eCollection 2022.
Liver cancer is the main reason of cancer deaths globally, with an unfavorable prognosis. DNA methylation is one of the epigenetic modifications and maintains the right adjustment of gene expression and steady gene silencing. We aim to explore the novel signatures for prognosis by using DNA methylation-driven genes. To acquire the DNA methylation-driven genes, we perform the difference analysis from the gene expression data and DNA methylation data in TCGA or GEO databases. And we obtain the 31 DNA methylation-driven genes. Subsequently, consensus clustering analysis was utilized to identify the molecular subtypes based on the 31 DNA methylation-driven genes. So, two molecular subtypes were identified to perform those analyses: Survival, immune cell infiltration, and tumor mutation. Results showed that two subtypes were clustered with distinct prognoses, tumor-infiltrating immune cell and tumor mutation burden. Furthermore, the 31 DNA methylation-driven genes were applied to perform the survival analysis to select the 14 survival-related genes. Immediately, a five methylation-driven genes risk model was built, and the patients were divided into high and low-risk groups. The model was established with TCGA as the training cohort and GSE14520 as the validation cohort. According to the risk model, we perform the systematical analysis, including survival, clinical feature, immune cell infiltration, somatic mutation status, underlying mechanisms, and drug sensitivity. Results showed that the high and low groups possessed statistical significance. In addition, the ROC curve was utilized to measure the accuracy of the risk model. AUCs at 1-year, 3-years, and 5-years were respectively 0.770, 0.698, 0.676 in training cohort and 0.717, 0.649, 0.621 in validation cohort. Nomogram was used to provide a better prediction for patients' survival. Risk score increase the accuracy of survival prediction in HCC patients. In conclusion, this study developed a novel risk model of five methylation-driven genes based on the comprehensive bioinformatics analysis, which accurately predicts the survival of HCC patients and reflects the immune and mutation features of HCC. This study provides novel insights for immunotherapy of HCC patients and promotes medical progress.
肝癌是全球癌症死亡的主要原因，预后不佳。DNA甲基化是表观遗传修饰之一，可维持基因表达的正确调节和基因稳定沉默。我们旨在通过使用DNA甲基化驱动基因探索新的预后特征。为了获得DNA甲基化驱动基因，我们对TCGA或GEO数据库中的基因表达数据和DNA甲基化数据进行差异分析。我们获得了31个DNA甲基化驱动基因。随后，利用共识聚类分析基于这31个DNA甲基化驱动基因鉴定分子亚型。因此，鉴定出两种分子亚型来进行这些分析：生存、免疫细胞浸润和肿瘤突变。结果显示，两种亚型在预后、肿瘤浸润免疫细胞和肿瘤突变负担方面聚类不同。此外，应用这31个DNA甲基化驱动基因进行生存分析以选择14个生存相关基因。随即，构建了一个由五个甲基化驱动基因组成的风险模型，并将患者分为高风险组和低风险组。该模型以TCGA作为训练队列，GSE14520作为验证队列建立。根据风险模型，我们进行了系统分析，包括生存、临床特征、免疫细胞浸润、体细胞突变状态、潜在机制和药物敏感性。结果显示，高风险组和低风险组具有统计学意义。此外，利用ROC曲线测量风险模型的准确性。训练队列中1年、3年和5年的AUC分别为0.770、0.698、0.676，验证队列中分别为0.717、0.649、0.621。使用列线图为患者生存提供更好的预测。风险评分提高了肝癌患者生存预测的准确性。总之，本研究基于综合生物信息学分析开发了一种新的由五个甲基化驱动基因组成的风险模型，该模型准确预测了肝癌患者的生存情况，并反映了肝癌的免疫和突变特征。本研究为肝癌患者的免疫治疗提供了新的见解，推动了医学进步。
