ArticleJournal of inflammation research2024
Use of Machine Learning for the Identification and Validation of Immunogenic Cell Death Biomarkers and Immunophenotypes in Coronary Artery Disease.
Article in Journal of inflammation research, 2024. 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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Who cites it
4 citing papers in PubMed, 4 citations in OpenAlex.
- Unveiling the comorbidity hub: WT1 drives renal cancer progression in chronic kidney disease and confers sirolimus vulnerability.Experimental and therapeutic medicine · 2026Article
- Targeting the Golgi apparatus enhances PD-L1 blockade and synergizes with oxaliplatin to improve immunotherapy efficacy.The Journal of biological chemistry · 2026Article
- Identification of SUMOylation modifiers involved in lung adenocarcinoma progression and Osimertinib resistance by integrated bioinformatics analysis.Scientific reports · 2025Article
- Immunogenic cell death-related biomarkers in heart failure probed by transcriptome and single-cell sequencing.Frontiers in immunology · 2025Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Immunogenic cell death (ICD) is part of the immune system's response to coronary artery disease (CAD). In this study, we bioinformatically evaluated the diagnostic and therapeutic utility of immunogenic cell death-related genes (IRGs) and their relationship with immune infiltration features in CAD. Methods: We acquired the CAD-related datasets GSE12288, GSE71226, and GSE120521 from the Gene Expression Omnibus (GEO) database and the IRGs from the GeneCards database. After identifying the immune cell death-related differentially expressed genes (IRDEGs), we developed a risk model and detected immune subtypes in CAD. IRDEGs were identified using least absolute shrinkage and selection operator (LASSO) analysis. Using a nomogram, we confirmed that both the LASSO model and ICD signature genes had good diagnostic performance. Results: There was a high degree of coincidence and immune representativeness between two CAD groups based on characteristic genes and hub genes. Hub genes were associated with the interaction of neuroactive ligands with receptors and cell adhesion receptors. The two groups differed in terms of adipogenesis, allograft rejection, and apoptosis, as well as the ICD signature and hub gene expression levels. The two CAD-ICD subtypes differed in terms of immune infiltration. Conclusion: Quantitative real-time PCR (qRT-PCR) correlated CAD with the expression of
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