Liu Donghui, Li Long, Wang Liru, Wang Chao, Hu Xiaowei, Jiang Qingxin, Wang Xuyao, Xue Guiqin, Liu Yu, Xue Dongbo
Department of Oncology, Heilongjiang Provincial Hospital, Harbin, China.
Harbin Institute of Technology, School of Life Science and Technology, Harbin, China.
Front Genet. 2021 Oct 21;12:758926. doi: 10.3389/fgene.2021.758926. eCollection 2021.
The management of gastric cancer (GC) still lacks tumor markers with high specificity and sensitivity. The goal of current research is to find effective diagnostic and prognostic markers and to clarify their related mechanisms. In this study, we integrated GC DNA methylation data from publicly available datasets obtained from TCGA and GEO databases, and applied random forest and LASSO analysis methods to screen reliable differential methylation sites (DMSs) for GC diagnosis. We constructed a diagnostic model of GC by logistic analysis and conducted verification and clinical correlation analysis. We screened credible prognostic DMSs through univariate Cox and LASSO analyses and verified a prognostic model of GC by multivariate Cox analysis. Independent prognostic and biological function analyses were performed for the prognostic risk score. We performed TP53 correlation analysis, mutation and prognosis analysis on eleven-DNA methylation driver gene (DMG), and constructed a multifactor regulatory network of key genes. The five-DMS diagnostic model distinguished GC from normal samples, and diagnostic risk value was significantly correlated with grade and tumor location. The prediction accuracy of the eleven-DMS prognostic model was verified in both the training and validation datasets, indicating its certain potential for GC survival prediction. The survival rate of the high-risk group was significantly lower than that of the low-risk group. The prognostic risk score was an independent risk factor for the prognosis of GC, which was significantly correlated with N stage and tumor location, positively correlated with the VIM gene, and negatively correlated with the CDH1 gene. The expression of CHRNB2 decreased significantly in the TP53 mutation group of gastric cancer patients, and there were significant differences in CCDC69, RASSF2, CHRNB2, ARMC9, and RPN1 between the TP53 mutation group and the TP53 non-mutation group of gastric cancer patients. In addition, CEP290, UBXN8, KDM4A, RPN1 had high frequency mutations and the function of eleven-DMG mutation related genes in GC patients is widely enriched in multiple pathways. Combined, the five-DMS diagnostic and eleven-DMS prognostic GC models are important tools for accurate and individualized treatment. The study provides direction for exploring potential markers of GC.
胃癌（GC）的管理仍然缺乏具有高特异性和敏感性的肿瘤标志物。当前研究的目标是找到有效的诊断和预后标志物，并阐明其相关机制。在本研究中，我们整合了从TCGA和GEO数据库获得的公开可用数据集中的GC DNA甲基化数据，并应用随机森林和LASSO分析方法筛选用于GC诊断的可靠差异甲基化位点（DMS）。我们通过逻辑分析构建了GC诊断模型，并进行了验证和临床相关性分析。我们通过单变量Cox和LASSO分析筛选出可信的预后DMS，并通过多变量Cox分析验证了GC的预后模型。对预后风险评分进行了独立的预后和生物学功能分析。我们对11个DNA甲基化驱动基因（DMG）进行了TP53相关性分析、突变和预后分析，并构建了关键基因的多因素调控网络。五DMS诊断模型可将GC与正常样本区分开来，诊断风险值与分级和肿瘤位置显著相关。在训练和验证数据集中均验证了11-DMS预后模型的预测准确性，表明其在GC生存预测方面具有一定潜力。高风险组的生存率显著低于低风险组。预后风险评分是GC预后的独立危险因素，与N分期和肿瘤位置显著相关，与VIM基因呈正相关，与CDH1基因呈负相关。在胃癌患者的TP53突变组中，CHRNB2的表达显著降低，胃癌患者的TP53突变组和TP53非突变组之间，CCDC69、RASSF2、CHRNB2、ARMC9和RPN1存在显著差异。此外，CEP290、UBXN8、KDM4A、RPN1具有高频突变，GC患者中11个DMG突变相关基因的功能在多个途径中广泛富集。综合来看，五DMS诊断和11-DMS预后GC模型是准确和个体化治疗的重要工具。该研究为探索GC的潜在标志物提供了方向。
