ArticleJournal of inflammation research2023
Immune Cell Infiltration Analysis Based on Bioinformatics Reveals Novel Biomarkers of Coronary Artery Disease.
Article in Journal of inflammation research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Shared diagnostic biomarkers in metabolic syndrome and coronary artery disease identified by integrated bioinformatics and machine learning.Endocrine connections · 2026Article
- Identification of hub biomarkers in coronary artery disease patients using machine learning and bioinformatic analyses.Scientific reports · 2025Article
- Comprehensive Analysis Based on Genes Associated With Cuproptosis, Ferroptosis, and Pyroptosis for the Prediction of Diagnosis and Therapies in Coronary Artery Disease.Cardiovascular therapeutics · 2025Article
- Uncovering the Interplay of Ferroptosis and Immune System in Coronary Artery Disease: Construction and Validation of a Diagnostic Model.International journal of general medicine · 2025Article
- Unveiling shared therapeutic targets and pathological pathways between coronary artery disease and major depressive disorder through bioinformatics analysis.Scientific reports · 2024Article
- Genetically predicted HLA-DR+ natural killer cells as potential mediators in the lipid-coronary artery disease/ calcification (CAD/CAC) causal pathway.Frontiers in immunology · 2024Article
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Authors and funding
10 authors.
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Abstract
Background: Coronary artery disease (CAD) is a multifactorial immune disease, but research into the specific immune mechanism is still needed. The present study aimed to identify novel immune-related markers of CAD. Methods: Three CAD-related datasets (GSE12288, GSE98583, GSE113079) were downloaded from the Gene Expression Integrated Database. Gene ontology annotation, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis and weighted gene co-expression network analysis were performed on the common significantly differentially expressed genes (DEGs) of these three data sets, and the most relevant module genes for CAD obtained. The immune cell infiltration of module genes was evaluated with the CIBERSORT algorithm, and characteristic genes accompanied by their diagnostic effectiveness were screened by the machine-learning algorithm least absolute shrinkage and selection operator (LASSO) regression analysis. The expression levels of characteristic genes were evaluated in the peripheral blood mononuclear cells of CAD patients and healthy controls for verification. Results: A total of 204 upregulated and 339 downregulated DEGs were identified, which were mainly enriched in the following pathways: "Apoptosis", "Th17 cell differentiation", "Th1 and Th2 cell differentiation", "Glycerolipid metabolism", and "Fat digestion and absorption". Five characteristic genes, LMAN1L, DOK4, CHFR, CEL and CCDC28A, were identified by LASSO analysis, and the results of the immune cell infiltration analysis indicated that the proportion of immune infiltrating cells (activated CD8 T cells and CD56 DIM natural killer cells) in the CAD group was lower than that in the control group. The expressions of CHFR, CEL and CCDC28A in the peripheral blood of the healthy controls and CAD patients were significantly different. Conclusion: We identified CHFR, CEL and CCDC28A as potential biomarkers related to immune infiltration in CAD based on public data sets and clinical samples. This finding will contribute to providing a potential target for early noninvasive diagnosis and immunotherapy of CAD.
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