Intra-tumor heterogeneity (ITH) plays an important role in the therapeutic resistance and prognosis of gastric cancer (GC), but there are no effective methods to detect it. The purpose of this study was to apply the mutant-allele tumor heterogeneity (MATH) algorithm to reveal the relationship between ITH and clinical features, and to use weighted gene co-expression network analysis (WGCNA) to search hub genes. The whole exome sequencing data with tumor mutations, RNA-seq, and clinical data were obtained from The Cancer Genome Atlas database. We calculated the MATH values and further investigated their relationships with clinical features and key genes screened out from molecular classification published by Nature. The WGCNA method was applied to discover hub genes within the high ITH cases. We found that MATH values were related to grade classification (P < 0.05). Our study also showed a "high MATH" group with a higher TP53 percentage (P < 0.001), whereas PIK3CA and RHOA had the opposite results (P = 0.004; P = 0.031). Using WGCNA, we found that red module, black module, and brown module were enriched in spliceosome, ribosome, and butanoate metabolism, and their hub genes were PSMD1, RPS23, and FAM84B, respectively. Together, these results demonstrate that in the high MATH group, represented as high heterogeneity, there was a higher frequency of TP53 mutation, and RHOA and PIK3CA tended to appear in the low heterogeneity group. PSMD1, RPS23, and FAM84B were the hub genes in high heterogeneity GC. They were related to the pathology and prognosis of patients.

中文翻译：

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基于加权基因共表达网络的高异质性胃癌Hub基因鉴定。

肿瘤内异质性（ITH）在胃癌（GC）的治疗抵抗和预后中起着重要作用，但目前尚无有效的检测方法。本研究的目的是应用突变等位基因肿瘤异质性（MATH）算法揭示ITH与临床特征之间的关系，并使用加权基因共表达网络分析（WGCNA）搜索枢纽基因。包含肿瘤突变、RNA-seq 和临床数据的全外显子组测序数据来自癌症基因组图谱数据库。我们计算了 MATH 值，并进一步研究了它们与临床特征和从 Nature 发表的分子分类中筛选出的关键基因的关系。应用 WGCNA 方法发现高 ITH 病例中的枢纽基因。我们发现 MATH 值与等级分类相关（P < 0.05）。我们的研究还显示“高 MATH”组具有更高的 TP53 百分比（P < 0.001），而 PIK3CA 和 RHOA 的结果相反（P = 0.004；P = 0.031）。使用WGCNA，我们发现红色模块、黑色模块和棕色模块富含剪接体、核糖体和丁酸代谢，它们的枢纽基因分别为PSMD1、RPS23和FAM84B。综上所述，这些结果表明，在以高异质性为代表的高MATH组中，TP53突变频率较高，而RHOA和PIK3CA则倾向于出现在低异质性组中。PSMD1、RPS23 和 FAM84B 是高异质性 GC 中的枢纽基因。它们与患者的病理和预后有关。黑色模块和棕色模块富含剪接体、核糖体和丁酸代谢，其枢纽基因分别为 PSMD1、RPS23 和 FAM84B。综上所述，这些结果表明，在以高异质性为代表的高MATH组中，TP53突变频率较高，而RHOA和PIK3CA则倾向于出现在低异质性组中。PSMD1、RPS23 和 FAM84B 是高异质性 GC 中的枢纽基因。它们与患者的病理和预后有关。黑色模块和棕色模块富含剪接体、核糖体和丁酸代谢，其枢纽基因分别为 PSMD1、RPS23 和 FAM84B。综上所述，这些结果表明，在以高异质性为代表的高MATH组中，TP53突变频率较高，而RHOA和PIK3CA则倾向于出现在低异质性组中。PSMD1、RPS23 和 FAM84B 是高异质性 GC 中的枢纽基因。它们与患者的病理和预后有关。RHOA 和 PIK3CA 倾向于出现在低异质性组中。PSMD1、RPS23 和 FAM84B 是高异质性 GC 中的枢纽基因。它们与患者的病理和预后有关。RHOA 和 PIK3CA 倾向于出现在低异质性组中。PSMD1、RPS23 和 FAM84B 是高异质性 GC 中的枢纽基因。它们与患者的病理和预后有关。