Li Jing, Zuo Zhifan, Lai Shusheng, Zheng Zhendong, Liu Bo, Wei Yuan, Han Tao
Key Laboratory of Visceral Theory and Application in Traditional Chinese Medicine of Ministry of Education, Liaoning University of Traditional Chinese Medicine, Shenyang, China.
China Medical University, The General Hospital of Northern Theater Command Training Base for Graduate, Shenyang, China.
J Gastrointest Oncol. 2021 Aug;12(4):1384-1397. doi: 10.21037/jgo-21-325.
Methylation is one of the common forms of RNA modification, which mainly include N6-methyladenosine (m6A), C5-methylcytidine (m5C), and N1-methyladenosine (m1A). Numerous studies have shown that RNA methylation is associated with tumor development. We aim to construct prognostic models of gastric cancer based on RNA methylation regulators.
The transcriptome and clinical data of gastric cancer and normal samples were obtained from the National Cancer Institute Genome Data Commons (NCI-GDC). Use Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis to construct risk models for different types of RNA methylation. Receiver operating characteristic (ROC) curves were generated to evaluate the predictive efficiency of risk characteristics. Cluster heat maps are used to assess the correlation with clinical information. Univariate and multivariate Cox analyses were used to analyze prognostic effects of risk scores. Gene Set Enrichment Analysis (GSEA) analyzes the functional enrichment of RNA methylation genes. And make a separate analysis of the data of Asians.
The expression of most of the 30 RNA methylation regulators were significantly different in cancer and paracancerous tissues (P<0.05). Three methylated genes (, , and ) were screened from m6A by LASSO Cox regression analysis. Five methylated genes (, , , , and ) were selected from the population, and were used to construct two risk ratio models. Survival analysis showed that the survival rate of patients in the low-risk group was significantly higher than that in the high-risk group (P<0.05). All ROC curves indicated that the predictive efficiency of risk characteristics was good [area under the ROC curve (AUC): 0.6-1].Cluster analysis reveals differences in clinical data between the two groups. Univariate and multivariate Cox regression results show that the risk score has independent prognostic value. GSEA showed that pathways such as cell cycle were significantly enriched in the low-risk group, while pathways such as calcium signaling pathway were significantly enriched in the high-risk group. In addition, three methylation models that can predict the prognosis of Asian gastric cancer patients were obtained.
The methylation prognosis model constructed in this study can effectively predict the prognosis of gastric cancer patients.
甲基化是RNA修饰的常见形式之一，主要包括N6-甲基腺苷（m6A）、C5-甲基胞嘧啶（m5C）和N1-甲基腺苷（m1A）。大量研究表明，RNA甲基化与肿瘤发展相关。我们旨在构建基于RNA甲基化调节因子的胃癌预后模型。
从美国国立癌症研究所基因组数据共享库（NCI-GDC）获取胃癌及正常样本的转录组和临床数据。使用最小绝对收缩和选择算子（LASSO）Cox回归分析构建不同类型RNA甲基化的风险模型。生成受试者工作特征（ROC）曲线以评估风险特征的预测效率。聚类热图用于评估与临床信息的相关性。单因素和多因素Cox分析用于分析风险评分的预后效应。基因集富集分析（GSEA）分析RNA甲基化基因的功能富集情况。并对亚洲人的数据进行单独分析。
30个RNA甲基化调节因子中的大多数在癌组织和癌旁组织中的表达存在显著差异（P<0.05）。通过LASSO Cox回归分析从m6A中筛选出3个甲基化基因（ 、 和 ）。从总体中选出5个甲基化基因（ 、 、 、 和 ），并用于构建两个风险比模型。生存分析表明，低风险组患者的生存率显著高于高风险组（P<0.05）。所有ROC曲线均表明风险特征的预测效率良好[ROC曲线下面积（AUC）：0.6 - 1]。聚类分析揭示了两组临床数据的差异。单因素和多因素Cox回归结果表明，风险评分具有独立的预后价值。GSEA显示，细胞周期等通路在低风险组中显著富集，而钙信号通路等通路在高风险组中显著富集。此外，获得了3个可预测亚洲胃癌患者预后的甲基化模型。
本研究构建的甲基化预后模型可有效预测胃癌患者的预后。
