Ma Junyang, Hou Shufu, Gu Xinxin, Guo Peng, Zhu Jiankang
School of Clinical Medicine, Jining Medical University, Jining, China.
Laboratory of Metabolism and Gastrointestinal Tumor, The First Affiliated Hospital of Shandong First Medical University, Jinan, China.
Front Immunol. 2025 Mar 21;16:1533959. doi: 10.3389/fimmu.2025.1533959. eCollection 2025.
Recent studies have suggested a potential association between gastric cancer (GC) and myocardial infarction (MI), with shared pathogenic factors. This study aimed to identify these common factors and potential pharmacologic targets.
Data from the IEU Open GWAS project were used. Two-sample Mendelian randomization (MR) analysis was used to explore the causal link between MI and GC. Transcriptome analysis identified common differentially expressed genes, followed by enrichment analysis. Drug target MR analysis and eQTLs validated these associations with GC, and the Steiger direction test confirmed their direction. The random forest and Lasso algorithms were used to identify genes with diagnostic value, leading to nomogram construction. The performance of the model was evaluated via ROC, calibration, and decision curves. Correlations between diagnostic genes and immune cell infiltration were analyzed.
MI was linked to increased GC risk (=1.112, =0.04). Seventy-four genes, which are related mainly to ubiquitin-dependent proteasome pathways, were commonly differentially expressed between MI and GC. Nine genes were consistently associated with GC, and eight had diagnostic value. The nomogram built on these eight genes had strong predictive performance (=0.950, validation set =0.957). Immune cell infiltration analysis revealed significant correlations between several genes and immune cells, such as T cells, macrophages, neutrophils, B cells, and dendritic cells.
MI is associated with an increased risk of developing GC, and both share common pathogenic factors. The nomogram constructed based on 8 genes with diagnostic value had good predictive performance.
近期研究表明，胃癌（GC）与心肌梗死（MI）之间可能存在关联，且有共同的致病因素。本研究旨在确定这些共同因素及潜在的药物靶点。
使用来自IEU Open GWAS项目的数据。采用两样本孟德尔随机化（MR）分析来探究MI与GC之间的因果关系。转录组分析确定了共同的差异表达基因，随后进行富集分析。药物靶点MR分析和表达数量性状基因座（eQTL）验证了这些与GC的关联，Steiger方向检验证实了它们的方向。使用随机森林和套索算法来识别具有诊断价值的基因，进而构建列线图。通过受试者工作特征曲线（ROC）、校准和决策曲线评估模型的性能。分析诊断基因与免疫细胞浸润之间的相关性。
MI与GC风险增加相关（比值比=1.112，P=0.04）。在MI和GC之间，共有74个主要与泛素依赖性蛋白酶体途径相关的基因存在差异表达。9个基因始终与GC相关，其中8个具有诊断价值。基于这8个基因构建的列线图具有很强的预测性能（训练集AUC=0.950，验证集=0.957）。免疫细胞浸润分析显示，几个基因与免疫细胞（如T细胞、巨噬细胞、中性粒细胞、B细胞和树突状细胞）之间存在显著相关性。
MI与GC发生风险增加相关，且两者具有共同的致病因素。基于8个具有诊断价值的基因构建的列线图具有良好的预测性能。
