Research on identifying key genes and mechanisms related to lymphangiogenesis in acute myocardial infarction via bioinformatics screening and experimental verification

Background In acute myocardial infarction (AMI), remodeling of the myocardial lymphatic system is crucial for infarct repair and inflammation control. This study used bioinformatics to identify genes related to lymphangiogenesis in AMI, hoping to elucidate the mechanisms of AMI and develop new targeted treatments. Methods GSE66360, GSE48060, and lymphangiogenesis-related genes ( LRGs ) were obtained from databases and the literature. Key genes associated with lymphangiogenesis were identified through machine learning, receiver operating characteristic (ROC) curve analysis, and expression verification. Gene set enrichment analysis (GSEA), immune infiltration analysis, and drug prediction were subsequently carried out. Finally, experimental verification of key gene expression was performed in clinical samples. Results Three PIM3, BMX, and ID1 signature genes were obtained by machine learning, and their regions under the curve showed significant differences in expression between groups, with consistent trends in both GSE66360 and GSE48060 datasets (p < 0.05). In addition, drug predictions showed PIM3 and BMX interacting with SGI-1776, vadimezan, canine, and gefitinib. Finally, genes in clinical samples also show the same expression trend. Conclusion This study identified three key genes ( PIM3, BMX, and ID1 ) as novel key genes in AMI, laying a foundation for clinical diagnosis and drug development.