Additional file 2 of m6A regulator-mediated methylation modification patterns and tumor microenvironment infiltration characterization in gastric cancer

Additional file 2:Table S1. Basic information of datasets included in this study for identifying distinct m6A methylation modification patterns. Table S2. The gene sets used in this work for marking each TME infiltration cell type. Table S3. Spearman correlation analysis of the 21 m6A modification regulators. Table S4. Estimating relative abundance of tumor microenvironment cells in 1059 gastric cancer patients by the Single-Sample Gene-Set. Table S5. The activation states of biological pathways in distinct m6A modification patterns by GSVA enrichment analysis. Table S6. The changes of m6Aclusters, ACRG molecular subtypes, gene clusters and m6Ascore. Table S7. Prognostic analysis of 718 m6A phenotype-related genes using a univariate Cox regression model. Table S8. Functional annotation for m6A phenotype -related genes (Gene Ontology-Biological process). Table S9. Spearman correlation between m6Ascore and other known signatures within the gastric cancer.

附加文件2：表S1. 本研究用于识别独特m6A甲基化修饰模式的各个数据集的基本信息。表S2. 本工作中用于标记每种肿瘤微环境浸润细胞类型的基因集。表S3. 21个m6A修饰调控因子的Spearman相关性分析。表S4. 通过单样本基因集估计1059例胃癌患者肿瘤微环境细胞类型的相对丰度。表S5. 通过GSVA富集分析，在不同m6A修饰模式中生物通路的激活状态。表S6. m6A簇、ACRG分子亚型、基因簇和m6A评分的变化。表S7. 使用单因素Cox回归模型对718个m6A表型相关基因进行预后分析。表S8. m6A表型相关基因的功能注释（基因本体-生物过程）。表S9. m6A评分与其他已知胃癌相关特征之间的Spearman相关性。