Immune Cell Infiltration Analysis Based on Bioinformatics Reveals Novel Biomarkers of Coronary Artery Disease

Tianwen He; Muheremu Muhetaer; Jiahe Wu; Jingjing Wan; Yushuang Hu; Tong Zhang; Yunxiang Wang; Qiongxin Wang; Huanhuan Cai; Zhibing Lu

doi:10.2147/JIR.S416329

Immune Cell Infiltration Analysis Based on Bioinformatics Reveals Novel Biomarkers of Coronary Artery Disease

J Inflamm Res. 2023 Jul 26:16:3169-3184. doi: 10.2147/JIR.S416329. eCollection 2023.

Authors

Tianwen He^#^{1

2}, Muheremu Muhetaer^#^{1

2}, Jiahe Wu^{1

2}, Jingjing Wan^{1

2}, Yushuang Hu^{1

2}, Tong Zhang^{1

2}, Yunxiang Wang^{1

2}, Qiongxin Wang^{1

2}, Huanhuan Cai^{1

2}, Zhibing Lu^{1

2}

Affiliations

¹ Department of Cardiology, Zhongnan Hospital of Wuhan University, Wuhan, People's Republic of China.
² Institute of Myocardial Injury and Repair, Wuhan University, Wuhan, People's Republic of China.

^# Contributed equally.

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.

Keywords: CAD; biomarkers; coronary artery disease; immune infiltration; inflammation; machine learning.

Grants and funding

The study was supported by grants from the National Natural Science Foundation of China (No. 82070425, Zhibing Lu), the Natural Science Foundation of Hubei Province of China (No. 2021CFA011, Zhibing Lu) and the Zhongnan Hospital of Wuhan University Science, Technology and Innovation Seed Fund (No. znpy2019010, Huanhuan Cai).