Unraveling the Therapeutic Potential of

aimsThis study aims to elucidate the relationship between potential MI targets and SFA's mechanism of action, providing a theoretical basis for clinical development of new drugs.

backgroundMyocardial infarction (MI) has been identified as one of the major cardiovascular diseases with adverse consequences.

objectivesThis study explores the potential mechanisms of SFA in treating MI by integrating bioinformatics, network pharmacology analyses and experimental verification.

methodsNew MI targets were predicted using bioinformatics techniques. Network pharmacology and molecular docking jointly served for predicting the key targets and underlying mechanisms of SFA. A machine learning model was developed to identify the core MI targets. Subsequently, H9c2 cardiomyocytes hypoxia model was established for experimental verification.

results140 active components were ascertained in SFA and 59 differentially expressed genes (DEGs) were screened for MI. Eighty-seven shared genes were obtained by WGCAN. Eighty proteins and 413 interactions were identified by PPI network. After building the machine model, three core targets were identified (STAT1, TNFRSF1A and MCL1). According to in vitro experiments, SFA exerts a protective effect relying on three core targets and biological processes, including cell viability, the inflammatory response, and antiapoptotic effects, etc. Conclusion: This study finds new core targets for MI and the therapeutic activity of SFA against MI, of which the experimental verification provides valuable insights into the molecular mechanisms underlying SFA's efficacy in MI treatment and paves the way for targeted drug development strategies.