Abstract

Objective Dyslipidemia and glutamate metabolism (DG) plays a crucial role in the pathogenesis of Acute myocardial infarction (AMI). Hence, deeper understanding of DG in AMI paves the way for the clinical applications of AMI. Methods In combination of PPI network and Limma framework, we first identified DG-associated shared differentially expressed genes (DEGs) from AMI patient bulk profile (GSE61144) and DG-associated gene list acquired from GEO and Genecard databases respectively. In another AMI patient bulk profile (GSE28454), we performed consensus clustering for classifying patient into 2 subgroups based on shared DG-related DEGs. By employing 2 machine learning algorithms (RF and SVM) in AMI patient training set (GSE109048), we identified DG-related hub gene, and then evaluated its diagnostic performance across GSE61144, GSE109048 and GSE66360(independent AMI patient bulk profile). Indeed, the molecular feature of DG-related hub gene was deciphered at GSE109048 and AMI single-cell transcriptomic profile (GSE269269). Finally, in vitro study illustrated the expression pattern of DG-related hub gene and DGIDB database with molecular docking proved the potential therapeutic framework for combating AMI. Results We discovered that DG-associated molecular subgroups and down-regulated expression gene signature CALR was diagnostic biomarkers for AMI, and Trentinoin should be considered as novel drug repurposing framework for the treatment of AMI. Conclusion We first pointed out that DG should be considered as diagnostic and therapeutic repurposing targets for the clinical translation of AMI, which can guide AMI patient personalized and precision medicine.