ORIGINAL RESEARCH article

Front. Mol. Biosci.

Sec. Molecular Diagnostics and Therapeutics

Volume 12 - 2025 | doi: 10.3389/fmolb.2025.1607096

This article is part of the Research TopicChallenges and Opportunities in Tumor MetabolomicsView all 6 articles

Identification of Hub Genes in Myocardial Infarction by Bioinformatics and Machine Learning: Insights into Inflammation and Immune Regulation

Provisionally accepted
Juan  YangJuan Yang1Xiang  LiXiang Li2Li  MaLi Ma2*Jun  ZhangJun Zhang2*
  • 1The Second People's Hospital of Dazu District, Chongqing, China
  • 2Shanghai Tenth People's Hospital, Tongji University, Shanghai, China

The final, formatted version of the article will be published soon.

Objective: This study aims to identify and validate key genes involved in the progression of myocardial infarction (MI) and to investigate their roles in inflammatory response, immune regulation, and myocardial remodeling. A systematic analysis will be conducted using bioinformatics and machine learning methods.Methods: Gene expression data of GSE60993, GSE61144, GSE66360 and GSE48060 from four datasets were collected from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between MI samples and normal samples were screened by the limma package. Weighted gene co-expression network analysis (WGCNA) was employed to identify genetic modules associated with MI.Core genes in key modules were screened using LASSO regression and support vector machine recursive feature elimination (SVM-RFE). These genes were then subjected to functional enrichment analysis, including Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and Gene Set Enrichment Analysis (GSEA).The CIBERSORT algorithm was utilized to evaluate immune cell infiltration patterns.Finally, potential therapeutic targets were explored through drug-gene interaction analysis using the DGIdb database.Results: After correcting for batch effects across datasets, we identified 687 differentially expressed genes (DEGs), including 405 up-regulated and 282 downregulated genes. WGCNA analysis identified a highly correlated module with MI (turquoise module) containing 324 genes. Integrative machine learning (LASSO regression and SVM-RFE) and validation identified five key MI-associated genes: ANPEP, S100A9, MMP9, DAPK2, and FCAR. These genes were functionally enriched in inflammatory and immune-related pathways and correlated with immune cell infiltration, particularly neutrophils and macrophages. Notably, S100A9, FCAR, and MMP9 emerged as druggable targets.The five hub genes identified in this study (ANPEP, S100A9, MMP9, DAPK2, and FCAR) significantly contribute to MI development by modulating inflammatory responses and immune regulation. Their strong association with MI pathogenesis highlights their potential as diagnostic markers and therapeutic targets, which may lead to new clinical applications for MI management.

Keywords: Myocardial Infarction, Hub genes, Inflammation, Immune Regulation, weighted gene co-expression network analysis (WGCNA), cardiac remodeling, LASSO, Drug-gene interaction

Received: 07 Apr 2025; Accepted: 22 May 2025.

Copyright: © 2025 Yang, Li, Ma and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Li Ma, Shanghai Tenth People's Hospital, Tongji University, Shanghai, China
Jun Zhang, Shanghai Tenth People's Hospital, Tongji University, Shanghai, China

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