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Updated: Sep 2, 2025
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
Published on: September 20, 2024
Identification of hub genes and their correlation with immune infiltration in coronary artery disease through
Ke-Ke Huang1, Hui-Lei Zheng2, Shuo Li1
1Department of Cardiology, Institute of Cardiovascular Diseases, the First Affiliated Hospital, Guangxi Medical University, Nanning, China.
Insights
This study identified four key genes (MMP9, PELI1, THBD, ZFP36) as potential biomarkers for coronary artery disease (CAD). These optimal feature genes (OFGs) and their association with immune cell infiltration offer new avenues for CAD genetic prediction.
Area of Science:
- Cardiovascular Research
- Immunology
- Genetics
Background:
- Coronary artery disease (CAD) is a complex, multifactorial condition with an incompletely understood pathogenesis.
- Investigating optimal feature genes (OFGs) and immune cell infiltration is crucial for advancing CAD understanding and prediction.
Purpose of the Study:
- To identify optimal feature genes (OFGs) for coronary artery disease (CAD).
- To explore the role of immune cell infiltration in CAD pathogenesis.
- To enhance the understanding and genetic prediction of CAD.
Main Methods:
- Utilized Gene Expression Omnibus (GEO) datasets for CAD cases meeting specific diagnostic criteria.
- Applied machine learning algorithms (LASSO, SVM-RFE, RF) to identify OFGs.
- Analyzed differentially expressed genes (DEGs) and immune cell infiltration using CIBERSORT.
Main Results:
- DEGs were linked to IL-17, NF-kappa B, and TNF signaling pathways, and enriched in LPS, tertiary granule, and pattern recognition receptor activity.
- Identified Matrix metalloproteinase 9 (MMP9), Pellino E3 ubiquitin protein ligase 1 (PELI1), thrombomodulin (THBD), and zinc finger protein 36 (ZFP36) as OFGs.
- Observed significant differences in immune cell proportions between CAD patients and controls, with correlations between OFGs and immune cells.
Conclusions:
- MMP9, PELI1, THBD, and ZFP36 show promise as predictive biomarkers for CAD.
- The identified OFGs and their immune infiltration associations offer potential for improved CAD genetic prediction and assessment.
Background:
Coronary artery disease (CAD) is a multifactorial disease and its pathogenesis remains unclear. We aimed to explore the optimal feature genes (OFGs) for CAD and to investigate the function of immune cell infiltration of CAD. It will be helpful for better understanding of the pathogenesis and the development of genetic prediction of CAD.
Methods:
Datasets related to CAD were obtained from the Gene Expression Omnibus (GEO) database. Cases from the datasets met diagnostic criteria including clinical symptoms, electrocardiographic (ECG) and angiographic evidence. We identified differentially expressed genes (DEGs) and conducted functional enrichment analysis. OFGs were obtained from the least absolute shrinkage and selection operator (LASSO) algorithm, support vector machine recursive feature elimination (SVM-RFE) algorithm, and random forest (RF) algorithm. CIBERSORT was used to compare immune infiltration between CAD patients and normal controls, and the correlation between OFGs and immune cells was analyzed.
Results:
DEGs were involved in the interleukin (IL)-17 signaling pathway, nuclear factor (NF)-kappa B signaling pathway, and tumor necrosis factor (TNF) signaling pathway. Gene Ontology (GO) analysis revealed DEGs were enriched in lipopolysaccharide (LPS), tertiary granule, and pattern recognition receptor activity. Disease Ontology (DO) analysis suggested DEGs were enriched in lung disease, arteriosclerotic cardiovascular disease (CVD). Matrix metalloproteinase 9 (MMP9), Pellino E3 ubiquitin protein ligase 1 (PELI1), thrombomodulin (THBD), and zinc finger protein 36 (ZFP36) were screened by the intersection of OFGs obtained from LASSO, SVM-REF, and RF algorithms. CAD patients had a lower proportion of memory B cells (P=0.019), CD8 T cells (P<0.001), resting memory CD4 T cells (P<0.001), regulatory T cells (P=0.028), and gamma delta T cells (P<0.001) than normal controls, while the proportion of activated memory CD4 T cells (P=0.014), resting natural killer (NK) cells (P<0.001), monocytes (P<0.001), M0 macrophages (P=0.023), activated mast cells (P<0.001), and neutrophils (P<0.001) in CAD patients were higher than normal controls. MMP9, PELI1, THBD, and ZFP36 were correlated with immune cells.
Conclusions:
MMP9, PELI1, THBD, and ZFP36 may be predicted biomarkers for CAD. The OFGs and association between OFGs and immune infiltration may provide potential biomarkers for CAD prediction along with the better assessment of the disease.
