Identification of hub genes in myocardial infarction by bioinformatics and machine learning: insights into inflammation and immune regulation.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40630623", "kind": "science-record", "revision": 4, "content_hash": "sha256:7f03b908376917a15710f767ad74201e50c3e570237a21a33dfde235c407b84f", "created_at": "2026-11-01T20:30:43Z", "updated_at": "2026-11-01T22:56:47Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of hub genes in myocardial infarction by bioinformatics and machine learning: insights into inflammation and immune regulation.", "doi": "10.3389/fmolb.2025.1607096", "pmid": "40630623", "authors": ["Juan Yang", "Xiang Li", "Li Ma", "Jun Zhang"], "journal": "Frontiers in molecular biosciences", "year": 2025, "abstract": "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.\nMethods: 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.\nResults: After correcting for batch effects across datasets, we identified 687 differentially expressed genes (DEGs), including 405 upregulated 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.\nConclusion: 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.", "claims": [{"id": "claim-method", "statement": "Using the limma package, we identified 687 differentially expressed genes (DEGs) with thresholds of |log2FC| > 0.585 and P < 0.05, including 405 upregulated and 282 downregulated genes (Figures 1A,B).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40630623", "locator": "sections[14] \"Identification of DEGs in MI\" paragraph 2", "quote": "Using the limma package, we identified 687 differentially expressed genes (DEGs) with thresholds of |log2FC| > 0.585 and P < 0.05, including 405 upregulated and 282 downregulated genes (Figures 1A,B)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40630623", "locator": "sections[21] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Five key MI-associated genes were identified and validated: ANPEP, S100A9, MMP9, DAPK2, and FCAR.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40630623", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Integrative machine learning (LASSO regression and SVM-RFE) and validation identified five key MI-associated genes: ANPEP, S100A9, MMP9, DAPK2, and FCAR."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Five key MI-associated genes were identified and validated: ANPEP, S100A9, MMP9, DAPK2, and FCAR.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:40630623"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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