Comprehensive analysis of macrophage-associated inflammatory genes in AMI based on bulk combined with single-cell sequencing data.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39308815", "kind": "science-record", "revision": 2, "content_hash": "sha256:5b9701202dec0329b1b9ccb36aa7fdb3ae4e8563b19898ed7795e0a23477cfca", "created_at": "2026-11-01T20:29:29Z", "updated_at": "2026-11-01T21:33:57Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Comprehensive analysis of macrophage-associated inflammatory genes in AMI based on bulk combined with single-cell sequencing data.", "doi": "10.7717/peerj.17981", "pmid": "39308815", "authors": ["Xugang Kong", "Guangjun Jin"], "journal": "PeerJ", "year": 2024, "abstract": "Background: Previous studies have highlighted the crucial role of macrophages in the post-acute myocardial infarction (AMI) inflammatory response. This study specifically focused on investigating macrophage-related targets involved in the inflammatory response after AMI.\nMethods: Bioinformatics methods were applied for identifying differentially expressed genes (DEGs) in datasets GSE163465, GSE236374, and GSE183272 obtained from the Gene Expression Omnibus (GEO) database. Communication analysis was conducted to analyze macrophages in AMI. Subsequent analyses encompassed functional enrichment analysis of Co-DEGs using Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG). Gene set variation analysis (GSVA) and immune infiltration analysis were carried out for screening key genes. Validation of the bioinformatics analysis results involved original and GSE114695 datasets, supported by quantitative real time polymerase chain reaction (qRT-PCR). Animal experiments confirmed the upregulation of Saa3, Acp5, and Fcgr4 genes in AMI mouse myocardial tissues.\nResults: A total of 80 and 1,907 DEGs were respectively identified by analyzing scRNA-seq and bulk RNA-seq data. The overlapping Co-DEGs were found to be closely associated with inflammation-associated pathways, specifically the PI3K-Akt-mTOR pathway. Screening based on GSVA scores and macrophage-associated scores highlighted four key genes (Saa3, Ms4a4c, Acp5, and Fcgr4). Immunoinfiltration analysis revealed their close association with macrophages. Dataset validation corroborated these findings. Experimental validation focused on Saa3, Ms4a4c, Acp5, and Fcgr4, demonstrating the upregulation of their expression in cardiac macrophages in the AMI group, consistent with previous reports.\nConclusion: This study provides new perspectives on AMI treatment. In addition, Saa3, Acp5, and Fcgr4 exhibit potential as biomarkers for improving cardiac repair and slowing down the development of heart failure after AMI.", "claims": [{"id": "claim-method", "statement": "Data for different genes in the two groups were compared using t-test with GraphPad Prism (v 10.1.0) software, with P < 0.05 considered statistically significant.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39308815", "locator": "sections[15] \"Statistical analysis\" paragraph 2", "quote": "Data for different genes in the two groups were compared using t-test with GraphPad Prism (v 10.1.0) software, with P < 0.05 considered statistically significant."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The following information was supplied regarding data availability:The raw data is available in the Supplemental Files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39308815", "locator": "sections[35] \"Data Availability\" paragraph 2", "quote": "The following information was supplied regarding data availability:The raw data is available in the Supplemental Files."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A total of 80 and 1,907 DEGs were respectively identified by analyzing scRNA-seq and bulk RNA-seq data.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39308815", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A total of 80 and 1,907 DEGs were respectively identified by analyzing scRNA-seq and bulk RNA-seq data."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A total of 80 and 1,907 DEGs were respectively identified by analyzing scRNA-seq and bulk RNA-seq data.", "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:39308815"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE114695"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE114695"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39308815", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7717/peerj.17981", "sha256": 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Results: A total of 80 a"], "order_basis": "paper_text_order:1142", "id": "assay:1"}, {"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["Gs were respectively identified by analyzing scRNA-seq and bulk RNA-seq data. The overlapping Co-DE", "dered statistically significant. Analysis of scRNA-seq data Single-cell data from the GSE163465 dat", "AMI The CellChat package was used to analyze scRNA-Seq data for understanding potential intercellul", "ms comparing DEGs from bulk RNA-seq data and scRNA-seq data revealed 33 Co-DEGs (Fig. 3B). Heatmap"], "order_basis": "paper_text_order:1248", "id": "assay:2"}, {"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["y identified by analyzing scRNA-seq and bulk RNA-seq data. The overlapping Co-DEGs were found to", "3A). Venn diagrams comparing DEGs from bulk RNA-seq data and scRNA-seq data revealed 33 Co-DEGs", "between the AMI and the Sham groups in bulk RNA-seq data. Purple dots signify significantly up-r", "DEGs obtained by intersecting DEGs from bulk RNA-seq data and scRNA-seq data; (C) heatmap depicti"], "order_basis": "paper_text_order:1267", "id": "assay:3"}]}