Identification and analysis of diagnostic markers related to lactate metabolism in myocardial infarction.

Lactate metabolism is implicated in myocardial infarction (MI), yet the underlying mechanisms are not fully understood. Identifying lactate metabolism-related genes (LMRGs) could uncover new diagnostic and therapeutic targets for MI. We conducted a bioinformatics analysis on GeneCards database to identify 498 LMRGs and intersected them with differentially expressed genes (DEGs) from MI samples, yielding 17 key genes. We utilized consensus clustering and weighted gene co-expression network analysis (WGCNA) to refine our gene list to 981 candidate genes. Machine learning algorithms identified three biomarkers: OLIG1, LIN52, and RLBP1, associated with 'ribosome' and 'carbon metabolism' pathways. Enrichment analyses and immune microenvironment assessments were performed, and networks including drug-gene interactions and kinase-transcription factor (TF)-mRNA-miRNA were constructed to explore the functions and potential therapeutic implications of these genes. The three biomarkers showed significant correlations with immune cell types, with OLIG1 having the highest positive correlation with monocytes and the highest negative correlation with neutrophils. The drug-gene network revealed potential interactions such as methapyrilene with LIN52 and 'bisphenol A' with RLBP1. The kinase-TF-mRNA-miRNA network comprised 209 nodes and 470 edges, indicating complex regulatory mechanisms. Our study identified three key biomarkers, OLIG1, LIN52, and RLBP1, in lactate metabolism associated with MI, providing insights into potential diagnostic markers and therapeutic targets. These findings warrant further investigation into the molecular mechanisms of these biomarkers in MI.