Table2_Unsupervised Hierarchical Clustering Identifies Immune Gene Subtypes in Gastric Cancer.XLS

Objectives: The pathogenesis of heterogeneity in gastric cancer (GC) is not clear and presents as a significant obstacle in providing effective drug treatment. We aimed to identify subtypes of GC and explore the underlying pathogenesis. Methods: We collected two microarray datasets from GEO (GSE84433 and GSE84426), performed an unsupervised cluster analysis based on gene expression patterns, and identified related immune and stromal cells. Then, we explored the possible molecular mechanisms of each subtype by functional enrichment analysis and identified related hub genes. Results: First, we identified three clusters of GC by unsupervised hierarchical clustering, with average silhouette width of 0.96, and also identified their related representative genes and immune cells. We validated our findings using dataset GSE84426. Subtypes associated with the highest mortality (subtype 2 in the training group and subtype C in the validation group) showed high expression of SPARC, COL3A1, and CCN. Both subtypes also showed high infiltration of fibroblasts, endothelial cells, hematopoietic stem cells, and a high stromal score. Furthermore, subtypes with the best prognosis (subtype 3 in the training group and subtype A in the validation group) showed high expression of FGL2, DLGAP1-AS5, and so on. Both subtypes also showed high infiltration of CD4+ T cells, CD8+ T cells, NK cells, pDC, macrophages, and CD4+ T effector memory cells. Conclusion: We found that GC can be classified into three subtypes based on gene expression patterns and cell composition. Findings of this study help us better understand the tumor microenvironment and immune milieu associated with heterogeneity in GC and provide practical information to guide personalized treatment.研究目的：胃癌（gastric cancer, GC）异质性的发病机制尚未明确，这已成为开展有效药物治疗的重大阻碍。本研究旨在鉴定胃癌亚型，并探究其潜在的发病机制。 研究方法：我们从基因表达综合数据库（Gene Expression Omnibus, GEO）中获取了两组微阵列数据集（GSE84433与GSE84426），基于基因表达模式开展无监督聚类分析，鉴定出相关免疫细胞与基质细胞。随后，通过功能富集分析探究各亚型的潜在分子机制，并筛选出相关枢纽基因（hub genes）。 研究结果：首先，我们通过无监督层次聚类鉴定出三类胃癌亚型，平均轮廓宽度为0.96，并确定了各亚型对应的标志性基因与免疫细胞。我们使用数据集GSE84426对研究结果进行了验证。死亡率最高的亚型（训练集亚型2与验证集亚型C）呈现SPARC、COL3A1及CCN基因的高表达，且这两类亚型均表现出成纤维细胞、内皮细胞、造血干细胞的高浸润性，同时基质评分较高。此外，预后最佳的亚型（训练集亚型3与验证集亚型A）高表达FGL2、DLGAP1-AS5等基因，且这两类亚型均存在CD4+ T细胞、CD8+ T细胞、自然杀伤细胞（natural killer cells, NK）、浆细胞样树突状细胞（plasmacytoid dendritic cells, pDC）、巨噬细胞及CD4+效应记忆T细胞的高浸润。 研究结论：本研究发现，可基于基因表达模式与细胞组成将胃癌分为三类亚型。本研究结果有助于我们更好地理解胃癌异质性相关的肿瘤微环境与免疫微环境，同时可为指导个性化治疗提供实用依据。