Characterization of lactylation-related subtypes and diagnostic markers in myocardial ischemic reperfusion injury using weighted gene coexpression network analysis and machine learning

Introduction Myocardial ischemia-reperfusion injury (MIRI) is a secondary injury that occurs after treatment for ischemic heart disease. This study aimed to identify key lactylation-related genes (LRGs) in MIRI to enable early diagnosis and reveal potential therapeutic targets for improved patient outcomes. Methods We analyzed MIRI gene expression datasets from the Gene Expression Omnibus database using differential gene expression and weighted gene coexpression network analyses to determine key genes and coexpression modules. LRGs from the GeneCards database were examined to reveal associations with MIRI. Consensus clustering was used to classify MIRI into distinct subtypes, and machine learning models were developed for diagnostic purposes. Immune cell infiltration was evaluated using CIBERSORT. Key findings were validated via western blot, and an in vitro hypoxia -reoxygenation model of HL-1 cardiomyocytes was employed to verify gene expression patterns. Results We identified seven significantly expressed LRGs in MIRI: G6pd , Tkt , Eif2s2 , Pabpc1 , Itgb2 , Cenpf , and Runx2 . Four of these genes ( G6pd , Itgb2 , Pabpc1 , and Runx2 ) showed consistent expression in cell-based assays, supporting their biomarker potential. Enrichment analysis revealed that these genes primarily clustered in cellular division, cytoskeletal reorganization, and multiple signaling pathways, highlighting their critical roles in myocardial injury. Notably, lactylation modifications may modulate immune responses and cellular signaling in MIRI, suggesting therapeutic relevance. Discussion In summary, this study identified seven robust diagnostic biomarkers for MIRI and demonstrated distinct molecular subtypes, offering key insights into its pathogenesis and providing a foundation for developing early diagnosis and personalized therapeutic strategies.