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2000
Volume 31, Issue 18
  • ISSN: 1381-6128
  • E-ISSN: 1873-4286

Abstract

Aims

This 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.

Background

Myocardial infarction (MI) has been identified as one of the major cardiovascular diseases with adverse consequences. Aiton (SFA) is indicated for the therapeutic treatment of MI. However, there is no systematic research on the new therapeutic targets for MI and the exact action mechanism of SFA.

Objectives

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

Methods

New 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.

Results

140 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 experiments, SFA exerts a protective effect relying on three core targets and biological processes, including cell viability, the inflammatory response, and antiapoptotic effects, .

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.

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Unraveling the Therapeutic Potential of Sophora flavescens Aiton in Myocardial Infarction: An Integrative Approach Combining Bioinformatics, Network Pharmacology, and Experimental Validation
Curr. Pharm. Des. 31, 1474 (2025); https://doi.org/10.2174/0113816128342405241204055321
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  • Article Type:     Research Article
Keyword(s): acute coronary syndrome (ACS); Bioinformatics; core targets; machine learning model; myocardial infarction; network pharmacology

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