ArticleFrontiers in immunology2026
Characterization of lactylation-related subtypes and diagnostic markers in myocardial ischemic reperfusion injury using weighted gene coexpression network analysis and machine learning.
Article in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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 Results: We identified seven significantly expressed LRGs in MIRI: 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.
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