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目的 旨在鉴定胃癌因果关联蛋白，构建预后预测模型，并解析风险分层的生物学机制及药物响应特征。方法 通过多组学整合策略，系统整合跨队列胃癌全基因组关联-蛋白质数量性状位点（pQTL）数据进行孟德尔随机化分析，结合京都基因与基因组百科全书及基因本体功能注释筛选关键通路。通过小绝对收缩和选择运算符（LASSO）-Cox和多因素Cox回归分析构建预后模型并计算风险评分，根据风险评分中位数，将所有患者分为高风险和低风险队列，基于GSE62254、GSE15459和癌症基因组图谱胃腺癌（TCGA-STAD）数据集训练及验证模型效能。采用基因集富集分析、免疫微环境分析和药物敏感性预测揭示高低风险组的分子特征。结果 胃癌风险相关pQTL显著富集于免疫应答（细胞因子受体互作、补体级联）和代谢通路（磷脂酰肌醇3激酶-蛋白激酶B、丝裂原活化蛋白激酶）。基于与胃癌有因果关系的pQTL及胃癌预后相关基因，通过LASSO-Cox和多变量Cox回归分析结果显示，将抑制素βB亚基、基质蛋白3、TATA盒结合蛋白样蛋白1、钙结合蛋白样蛋白、硫酸软骨素蛋白聚糖4、脂多糖结合蛋白和ST3 β-半乳糖苷α-2,3-唾液酸转移酶6共7个基因纳入预后模型，Kaplan-Meier分析和受试者工作特征曲线下面积分析验证了模型在训练集和验证集中均具有良好预测效能。高风险组呈现促炎微环境特征。高风险组对5-氟尿嘧啶等10种药物的敏感性显著高于低风险组。结论 本研究建立的7个基因预后模型实现了胃癌生存风险分层，揭示免疫-代谢失衡是风险分层的核心机制，并为高风险胃癌患者的个体化药物治疗方案选择提供了潜在的生物标志物依据。
Abstract：Objective To identify causal proteins and develop a prognostic model for gastric cancer,with the ultimate goal of unraveling the biological basis of risk stratification and its implications for drug sensitivity.Methods Utilizing an integrative multi-omics strategy,this study systematically combined cross-cohort gastric cancer genome-wide association study and protein quantitative trait loci(pQTL) data for Mendelian randomization analysis.This approach was integrated with Kyoto Encyclopedia of Genes and Genomes and Gene Ontology functional annotations to identify key biological pathways.A prognostic model was constructed using Least Absolute Shrinkage and Selection Operator(LASSO)-Cox and multivariate Cox regression analyses,and a risk score was calculated.All patients were divided into high-risk and low-risk cohorts based on the median risk score.The model was trained and validated using the GSE62254,GSE15459,and TCGA-STAD datasets.Gene Set Enrichment Analysis,immune microenvironment analysis,and drug sensitivity prediction were used to reveal the molecular characteristics of the high-and low-risk groups.Results Gastric cancer risk-associated pQTLs were significantly enriched in immune response(cytokine receptor interactions,complement cascade) and metabolic pathways(phosphatidy linositol 3-kinase-protein kinase B,mitogen-activated protein kinase).LASSO-Cox and multivariate Cox regression analyses,based on causally associated pQTLs and prognostic genes,revealed that seven genes including inhibin subunit beta B,matrilin 3,TATA-box binding protein like 1,calmegin,chondroitin sulfate proteoglycan 4,lipopolysaccharide binding protein and ST3 beta-galactoside alpha-2,3-sialyltransferase 6 were included in the prognostic model.Kaplan-Meier analysis and area under the receiver operating characteristic curve analysis validated the model's robust predictive performance in both the training and validation sets.The high-risk group exhibited a proinflammatory microenvironment.Furthermore,this group exhibited significantly higher sensitivity to 10 drugs,including 5-fluorouracil,compared with the low-risk group.Conclusion The 7-gene prognostic model established in this study achieves survival risk stratification for gastric cancer,reveals that immune-metabolic imbalance is the core mechanism of risk stratification,and provides a potential biomarker basis for the selection of personalized drug treatment options for high-risk gastric cancer patients.
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基本信息:
中图分类号:R735.2
引用信息:
[1]王毅,金海洁,王文玲,等.基于多组学分析的胃癌预后模型构建及药物敏感性预测[J].临床肿瘤学杂志,2025,30(10):956-963.
基金信息:
贵州省基础研究计划（自然科学）项目（[2022]340、zk[2025]一般项目475）; 贵州医科大学培育项目（20NSP040）