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This study aimed to identify diagnostic biomarkers and therapeutic candidates for MetS-associated MIRI.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Three MIRI and two MetS datasets from GEO were analyzed using differential expression analysis, WGCNA, and machine learning (LASSO\/SVM-RFE). Hub genes were validated via qRT-PCR in hypoxia-induced H9C2 cells. Drug candidates were predicted via PPI networks, CTD, and molecular docking, followed by experimental evaluation of dexamethasone.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Five hub genes\u2014DAK, GTF3C5, KCNMB1, TRAF1, and ZNF692\u2014were identified, with distinct expression patterns (DAK\/GTF3C5 downregulated; KCNMB1\/TRAF1\/ZNF692 upregulated). These genes were enriched in immune-related pathways, and their diagnostic performance was robust (AUCs: 0.875\u20130.969). Dexamethasone downregulated KCNMB1\/TRAF1\/ZNF692 and reduced apoptosis in H9C2 cells.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This study reveals immune-metabolic dysregulation as a key driver of MetS-MIRI, proposes five biomarkers for diagnosis, and highlights dexamethasone as a promising therapeutic candidate.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fimmu.2025.1567572","type":"journal-article","created":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T04:12:45Z","timestamp":1748491965000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Identification of diagnostic genes for myocardial ischemia reperfusion injury associated with metabolic syndrome through the integration of bioinformatics analysis, molecular docking and experimental 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