Gastric cancer (GC) poses a significant global health challenge, demanding a detailed exploration of its molecular landscape. Studies suggest that exposure to environmental pollutants can lead to changes in microRNA (miRNA) expression patterns, which may contribute to the development and progression of GC. MiRNAs have emerged as crucial regulators implicated in GC pathogenesis. The largest GC serum miRNA dataset to date, comprising 1417 non‐cancer controls and 1417 GC samples was used. We conducted a comprehensive analysis of miRNA expression profiles. Differential expression analysis, co‐expression network construction, and machine learning models were employed to identify key serum miRNAs and their association with clinical parameters. Weighted Gene Co‐expression Network Analysis (WGCNA) and immune infiltration analysis were used to validate the importance of the key miRNA. A total of 1766 differentially expressed miRNAs were identified, with miR‐1290, miR‐1246, and miR‐451a among the top up‐regulated, and miR‐6875‐5p, miR‐6784‐5p, miR‐1228‐5p, and miR‐6765‐5p among the top down‐regulated. WGCNA revealed that modules M1 and M5 were significantly associated with GC subtypes and disease status. MiRNA‐target gene network analysis identified prognostically significant genes TP53, EMCN, CBX8, and ALDH1A3. Machine learning models LASSO, SVM, randomforest, and XGBOOST demonstrated the diagnostic potential of miRNA profiles. Tissue and serum miR‐187 emerged as an independent prognostic factor, influencing patient survival across clinical parameters. Gene expression and immune cell infiltration were different in tissues stratified by miR‐187 expression. In summary, the integration of differential gene expression, co‐expression analysis, and immune cell profiling provided insights into the molecular intricacies of GC progression.

中文翻译：

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血清和组织 microRNA 转录组的综合分析用于胃癌生物标志物的发现


胃癌 （GC） 构成了重大的全球健康挑战，需要对其分子图谱进行详细探索。研究表明，暴露于环境污染物会导致 microRNA （miRNA） 表达模式发生变化，这可能导致 GC 的发生和发展。MiRNA 已成为与 GC 发病机制有关的关键调节因子。使用了迄今为止最大的 GC 血清 miRNA 数据集，包括 1417 个非癌症对照和 1417 个 GC 样品。我们对 miRNA 表达谱进行了全面分析。采用差异表达分析、共表达网络构建和机器学习模型来识别关键血清 miRNAs 及其与临床参数的关联。加权基因共表达网络分析 （WGCNA） 和免疫浸润分析用于验证关键 miRNA 的重要性。共鉴定出 1766 个差异表达的 miRNA，其中 miR-1290 、 miR-1246 和 miR-451a 处于上调状态，miR-6875-5p 、 miR-6784-5p 、 miR-1228-5p 和 miR-6765-5p 处于上调状态。WGCNA 显示模块 M1 和 M5 与 GC 亚型和疾病状态显著相关。MiRNA 靶基因网络分析确定了具有预后意义的基因 TP53 、 EMCN 、 CBX8 和 ALDH1A3。机器学习模型 LASSO 、 SVM 、 randomforest 和 XGBOOST 证明了 miRNA 谱的诊断潜力。组织和血清 miR-187 成为独立的预后因素，影响各种临床参数的患者生存率。基因表达和免疫细胞浸润在按 miR-187 表达分层的组织中是不同的。 总之，差异基因表达、共表达分析和免疫细胞分析的整合提供了对 GC 进展分子复杂性的见解。