ArticleFrontiers in immunology2025
Immunogenic cell death-related biomarkers in heart failure probed by transcriptome and single-cell sequencing.
Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Multiple triggering mechanisms of myocardial fibrosis: Comparison and integration in different disease contexts.iScience · 2026Review
- Association of Immunoglobulin E With Right Ventricular Dysfunction in Pulmonary Arterial Hypertension.Pulmonary circulation · 2026Article
- Identification of novel lipid metabolism-related biomarkers of aortic dissection by integrating single-cell RNA sequencing analysis and machine learning algorithms.Frontiers in immunology · 2025Article
- Construction of a competitive endogenous RNA network and identification of potential regulatory axes in hypertrophic cardiomyopathy.Frontiers in cardiovascular medicine · 2025Article
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9 authors.
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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. Methods: 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. Results: 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. Conclusions: 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.
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