Investigation of Molecular Mechanisms and Biomarker Identification for Dysregulated Acid Metabolism in Gastric Cancer Based on Omics Data Analysis

胃癌作为全球高发恶性肿瘤，其代谢重编程引起的酸代谢异常在肿瘤进展中具有重要作用，然而其具体分子机制及靶向干预策略仍有待深入探索。本研究结合TCGA和GTEx数据库的转录组数据，系统探究了胃癌细胞酸性代谢的分子调控机制及预后相关标志物。通过差异表达分析、WGCNA共表达网络构建、随机森林模型和生存分析，筛选出与酸性代谢特征及肿瘤微环境酸碱平衡的调控相关的103个差异表达基因(75个上调，28个下调)，其中16个基因与胃癌细胞排酸功能显著相关。WGCNA分析揭示了green模块(模块核心基因包括LRRC8C)与胃癌TNM分期正相关。随机森林模型在胃癌诊断中表现出高灵敏度和特异性，其中LRRC8C基因的特征重要性位于其他基因前列。生存分析进一步鉴定了LRRC8C核心基因能够作为独立预后标志物，且该基因高表达与患者不良预后显著相关。本研究为胃癌的分子分型、预后评估及靶向治疗提供了新视角。Gastric cancer, a prevalent malignancy worldwide, is critically influenced by metabolic reprogramming-driven acid metabolism dysregulation during tumor progression. However, its specific molecular mechanisms and targeted intervention strategies remain underexplored. This study systematically investigated the molecular regulatory mechanisms of acidic metabolism and prognosis-related biomarkers in gastric cancer using transcriptomic data from the TCGA and GTEx databases. Through differential expression analysis, WGCNA co-expression network construction, random forest modeling, and survival analysis, 103 differentially expressed genes (75 upregulated and 28 downregulated) associated with acidic metabolic features and acid-base balance regulation in the tumor microenvironment were identified, including 16 genes significantly linked to acid extrusion in gastric cancer cells. WGCNA revealed the green module (core gene: LRRC8C) to be positively correlated with TNM staging. The random forest model demonstrated high sensitivity and specificity in gastric cancer diagnosis, with LRRC8C ranking high in feature importance. Survival analysis further identified LRRC8C as an independent prognostic biomarker, where its high expression was significantly associated with poor patient outcomes. This study provides novel insights into molecular subtyping, prognostic evaluation, and targeted therapy for gastric cancer.