Immunogenic cell death-related biomarkers in heart failure probed by transcriptome and single-cell sequencing.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40630963", "kind": "science-record", "revision": 3, "content_hash": "sha256:d6176d706ce3dcbb46981e89f576fc516283364e6eeda71778a3bed042cb8d86", "created_at": "2026-11-01T20:30:43Z", "updated_at": "2026-11-01T22:14:20Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Immunogenic cell death-related biomarkers in heart failure probed by transcriptome and single-cell sequencing.", "doi": "10.3389/fimmu.2025.1560903", "pmid": "40630963", "authors": ["Haoyue Wang", "Dongdong Wu", "Gangfei Han", "Jingjing Yan", "Zehui Wang", "Xing He", "Yuxiang Chen", "Yan Wang", "Qinghua Han"], "journal": "Frontiers in immunology", "year": 2025, "abstract": "Background: Heart failure (HF) represents the terminal stage of various cardiovascular disorders, with immunogenic cell death (ICD) potentially influencing HF progression through modulation of immune cell activity. This study aimed to identify ICD-associated biomarkers in patients with HF and explore their underlying mechanisms.\nMethods: Data from GSE57338, GSE3586 and GSE5406 were retrieved from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to identify candidate genes, followed by enrichment analysis and Protein-Protein Interaction (PPI) network construction. Candidate biomarkers were selected using two machine learning approaches and validated for expression levels, with receiver operating characteristic (ROC) curve analysis determining the final biomarkers. A nomogram model was built based on the biomarkers, followed by molecular regulatory network analysis, gene set enrichment analysis (GSEA), immune infiltration assessment, and drug prediction. Additionally, key cells were selected for pseudo-time and cell communication analysis using the GSE183852 dataset. Next, pseudotemporal analysis was also performed on key cell subpopulations. Real-time quantitative PCR (RT-qPCR) was employed to validate the biomarkers.\nResults: Three biomarkers, CD163, FPR1, and VSIG4, were identified as having significant diagnostic value for HF. GSEA revealed their enrichment in ribosomal and immune cell-related pathways. These biomarkers were notably correlated with CD8 T cells and M2 macrophages. Carbachol and etynodiol were predicted to interact with all three biomarkers. Single-cell RNA sequencing identified nine cell types, with expression of the biomarkers confined to monocytes and macrophages. Strong cell communication was observed between these cell types and fibroblasts. Expression of CD163 and VSIG4 decreased over time in monocytes and macrophages, whereas FPR1 showed an upward trend. In addition, the expression levels of CD163 and VSIG4 increased in subpopulations of monocytes and macrophages, whereas FPR1 showed a decreasing trend. RT-qPCR results confirmed significant down-regulation of CD163, FPR1, and VSIG4 in patients with HF and animal models.\nConclusions: This study identified and validated three ICD-related biomarkers in HF-CD163, FPR1, and VSIG4-offering a novel theoretical foundation for the clinical diagnosis and treatment of HF.", "claims": [{"id": "claim-method", "statement": "Differences between two groups were assessed using the Wilcoxon rank sum test, with statistical significance defined as P < 0.05.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40630963", "locator": "sections[18] \"Statistical analysis\" paragraph 2", "quote": "Differences between two groups were assessed using the Wilcoxon rank sum test, with statistical significance defined as P < 0.05."}], "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 author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40630963", "locator": "sections[33] \"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 author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "This study identified and validated three ICD-related biomarkers in HF—CD163, FPR1, and VSIG4—offering a novel theoretical foundation for the clinical diagnosis and treatment of HF.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40630963", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "This study identified and validated three ICD-related biomarkers in HF—CD163, FPR1, and VSIG4—offering a novel theoretical foundation for the clinical diagnosis and treatment of HF."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "This study identified and validated three ICD-related biomarkers in HF—CD163, FPR1, and VSIG4—offering a novel theoretical foundation for the clinical diagnosis and treatment of HF.", "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:40630963"], "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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