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. 2023 Aug 10:14:1181510.
doi: 10.3389/fphys.2023.1181510. eCollection 2023.

Identification and validation of potential hypoxia-related genes associated with coronary artery disease

Yuqing Jin  1 , Weiyan Ren  1 , Jiayi Liu  1 , Xuejiao Tang  1 , Xinrui Shi  1 , Dongchen Pan  1 , Lianguo Hou  2 , Lei Yang  1
Affiliations

Identification and validation of potential hypoxia-related genes associated with coronary artery disease

Yuqing Jin et al. Front Physiol. .

Abstract

Introduction: Coronary artery disease (CAD) is one of the most life-threatening cardiovascular emergencies with high mortality and morbidity. Increasing evidence has demonstrated that the degree of hypoxia is closely associated with the development and survival outcomes of CAD patients. However, the role of hypoxia in CAD has not been elucidated. Methods: Based on the GSE113079 microarray dataset and the hypoxia-associated gene collection, differential analysis, machine learning, and validation of the screened hub genes were carried out. Results: In this study, 54 differentially expressed hypoxia-related genes (DE-HRGs), and then 4 hub signature genes (ADM, PPFIA4, FAM162A, and TPBG) were identified based on microarray datasets GSE113079 which including of 93 CAD patients and 48 healthy controls and hypoxia-related gene set. Then, 4 hub genes were also validated in other three CAD related microarray datasets. Through GO and KEGG pathway enrichment analyses, we found three upregulated hub genes (ADM, PPFIA4, TPBG) were strongly correlated with differentially expressed metabolic genes and all the 4 hub genes were mainly enriched in many immune-related biological processes and pathways in CAD. Additionally, 10 immune cell types were found significantly different between the CAD and control groups, especially CD8 T cells, which were apparently essential in cardiovascular disease by immune cell infiltration analysis. Furthermore, we compared the expression of 4 hub genes in 15 cell subtypes in CAD coronary lesions and found that ADM, FAM162A and TPBG were all expressed at higher levels in endothelial cells by single-cell sequencing analysis. Discussion: The study identified four hypoxia genes associated with coronary heart disease. The findings provide more insights into the hypoxia landscape and, potentially, the therapeutic targets of CAD.

Keywords: coronary artery disease; hypoxia; immune cell infiltration; metabolism; single-cell sequencing.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Identification of differentially expressed genes (DE-mRNAs) and differentially expressed long non-coding RNAs (DE-lncRNAs). (A, B) Volcano plot showing differentially expressed mRNAs (A) and lncRNAs (B) in patients with coronary artery disease versus the normal control population. Red dots represent upregulated genes and blue dots represent downregulated genes with a threshold of |log2FC| >0 and adjusted to p < 0.05. (C) Shows DE-mRNAs and hypoxia-related genes taken to intersect to obtain differential hypoxia-related genes (DE-HRGs).
FIGURE 2
FIGURE 2
Functional annotation of DE-HRGs. (A) GO enrichment analysis of the DE-HRGs. (B) KEGG enrichment analysis of the DE-HRGs. (C) Protein–protein interaction (PPI) network of the DE-HRGs.
FIGURE 3
FIGURE 3
Identification of diagnostic genes using three machine learning algorithms. (A, B) Based on support vector machine‐recursive feature elimination (SVM-RFE) to screen hub genes. (C) Least absolute shrinkage and selection operator (LASSO) regression algorithm to screen hub genes. (D) Random forest (RF) algorithm to screen hub genes. (E) The rank of genes in accordance with their relative importance. (F) Venn diagram of hub genes in three machine learning algorithms (SVM, LASSSO, RF).
FIGURE 4
FIGURE 4
Expression analysis and diagnostic efficacy of hub genes in the prediction of CAD. (A) Box plots showing the mRNA expression of hub genes in CAD patients and Normal control in the GSE113079 dataset. (B) ROC curves estimating the diagnostic performance of hub genes.
FIGURE 5
FIGURE 5
Heatmap indicating the correlation between the hub genes and differentially expressed metabolism-related genes. The color represents the p-value of the correlation, with positive correlations in red and negative correlations in blue.
FIGURE 6
FIGURE 6
Validation of mRNA expression of hypoxia-related genes (A) Differential expression of hub genes in hypoxic control (hypoxia) and hypoxic coronary artery disease groups (Hypoxia-TNFα). (B) Differential expression of hub genes in 10 pairs of CAD patients and healthy physical examination population (N). *p < 0.05 and **p < 0.01.
FIGURE 7
FIGURE 7
GSEA identifies signaling pathways involved in the hub genes. (A–D) The main signaling pathways that are significantly enriched in high or low expressions of characteristic genes. (A) ADM, (B) PPFIA4, (C) TPBG, (D) FAM162A.
FIGURE 8
FIGURE 8
Analyzing and showing immune cell infiltration. (A) Immune cell kinds and ratios in patients with CAD. (B) A box plot showing the expression of 22 different immune cell types between CAD and controls. (C) A heat map demonstrating correlation for 21 different immune cell types. The degree of the correlation is shown by the size of the colored squares; red indicates a positive correlation, and blue indicates a negative correlation. The association is greater the darker the hue. (D) Correlation between immune cells and important genes.
FIGURE 9
FIGURE 9
The cell distribution of 4 hub HRGs in CAD. (A) t-distribution random neighbor embedding (t-SNE) plot showing annotation and color coding of CAD cell types. The single-cell data were downscaled by the TSNE algorithm to obtain 2 dimensions, tSNE_1 and tSNE_2. (B) Scatter plot and (C) violin plot show the distribution of 4 hub HRGs in CAD.
FIGURE 10
FIGURE 10
Developing the ceRNA Network. The expected lncRNAs were shown by red nodes. The expected miRNAs were shown by green nodes. Hub genes were represented by blue nodes.
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References

    1. AbdelMassih A., Yacoub E., Husseiny R. J., Kamel A., Hozaien R., El Shershaby M., et al. (2021). Hypoxia-inducible factor (HIF): the link between obesity and COVID-19. Obes. Med. 22, 100317. 10.1016/j.obmed.2020.100317 - DOI - PMC - PubMed
    1. Abe H., Semba H., Takeda N. (2017). The roles of hypoxia signaling in the pathogenesis of cardiovascular diseases. J. Atheroscler. Thromb. 24 (9), 884–894. 10.5551/jat.RV17009 - DOI - PMC - PubMed
    1. Chen P., Zhou C., Li B., Yang C. (2020). Circular RNA MGAT1 regulates cell proliferation and apoptosis in hypoxia-induced cardiomyocytes through miR-34a/YAP1 axis. Int. J. Clin. Exp. Pathol. 13 (10), 2474–2486. - PMC - PubMed
    1. Fan Y., Li Y., Chen Y., Zhao Y. J., Liu L. W., Li J., et al. (2016). Comprehensive metabolomic characterization of coronary artery diseases. J. Am. Coll. Cardiol. 68 (12), 1281–1293. 10.1016/j.jacc.2016.06.044 - DOI - PubMed
    1. Haberka M., Machnik G., Kowalowka A., Biedron M., Skudrzyk E., Regulska-Ilow B., et al. (2019). Epicardial, paracardial, and perivascular fat quantity, gene expressions, and serum cytokines in patients with coronary artery disease and diabetes. Pol. Arch. Intern Med. 129 (11), 738–746. 10.20452/pamw.14961 - DOI - PubMed