Comprehensive analysis of diagnostic biomarkers related to histone acetylation in acute myocardial infarction.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40251588", "kind": "science-record", "revision": 3, "content_hash": "sha256:769dc5884f1deb8660720a42ece599bb6afca4c9480b8b9443b1873f1c149a34", "created_at": "2026-11-01T20:30:24Z", "updated_at": "2026-11-01T22:13:55Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Comprehensive analysis of diagnostic biomarkers related to histone acetylation in acute myocardial infarction.", "doi": "10.1186/s12920-025-02135-2", "pmid": "40251588", "authors": ["Man Li", "Lifeng Yang", "Yan Wang", "Lei Zhang"], "journal": "BMC medical genomics", "year": 2025, "abstract": "BACKGROUND: Acute myocardial infarction (AMI) has become a serious disease that endangers human health, with high morbidity and mortality. Numerous studies have reported histone acetylation can result in the occurrence of cardiovascular diseases. This article aims to explore the potential biomarkers of histone acetylation regulatory genes (ARGs) in AMI patients.\nMETHODS: Five AMI datasets were downloaded from the Gene Expression Omnibus (GEO) database. Next, ARG-related genes were gathered by gene set variation analysis (GSVA) and Spearman's correlation analysis. Subsequently, weighted gene co-expression network analysis (WGCNA) was performed to identify the module genes related to histone acetylation regulation. In the GSE60993 and GSE48060 datasets, the common differentially expressed genes (DEGs) between AMI and control samples were screened. Importantly, the intersecting genes were obtained by overlapping ARGs-related genes, common DEGs, and module genes. Then, the biomarkers in AMI were determined by machine learning, receiver operating characteristic (ROC) curves, and quantitative PCR (qPCR). In addition, immune analysis, drug prediction, molecular docking, and the lncRNA-miRNA-mRNA regulatory network targeting the biomarkers were analyzed, respectively.\nRESULTS: Here, a total of 18 intersecting genes were identified by overlapping 7,349 ARGs-related genes, 5,565 module genes, and 25 common DEGs. Further, five biomarkers (AQP9, HLA-DQA1, MCEMP1, NKG7, and S100A12) were obtained, and a nomogram was constructed and verified based on these biomarkers. Notably, the biomarkers were significantly associated with CD8 T cells and neutrophils. In addition, the drugs related to biomarkers were predicted, and ATOGEPANT with the molecular target (S100A12) had a high binding affinity (docking score = -10 kcal/mol).\nCONCLUSION: AQP9, HLA-DQA1, MCEMP1, NKG7, and S100A12 were identified as biomarkers related to ARGs in AMI, which provides a new perspective to study the relationship between ARGs and AMI.", "claims": [{"id": "claim-method", "statement": "ARG-related genes were gathered by gene set variation analysis (GSVA) and Spearman’s correlation analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40251588", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "ARG-related genes were gathered by gene set variation analysis (GSVA) and Spearman’s correlation analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data availability statement: Five datasets of AMI including GSE60993, GSE48060, GSE61144, GSE24548, and GSE97320 were sourced from the GEO database (https://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40251588", "locator": "sections[25] \"notes\" paragraph 1", "quote": "Data availability statement: Five datasets of AMI including GSE60993, GSE48060, GSE61144, GSE24548, and GSE97320 were sourced from the GEO database (https://www.ncbi.nlm.nih.gov/geo/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Five biomarkers (AQP9, HLA-DQA1, MCEMP1, NKG7, and S100A12) were identified as biomarkers related to ARGs in AMI.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40251588", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "AQP9, HLA-DQA1, MCEMP1, NKG7, and S100A12 were identified as biomarkers related to ARGs in AMI, which provides a new perspective to study the relationship between ARGs and AMI."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Five biomarkers (AQP9, HLA-DQA1, MCEMP1, NKG7, and S100A12) were identified as biomarkers related to ARGs in AMI.", "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:40251588"], "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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