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

IntroductionMyocardial 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.MethodsWe 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.ResultsWe 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.