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. 2023 Jul 3:10:1068782.
doi: 10.3389/fcvm.2023.1068782. eCollection 2023.

Hypoxia-associated genes predicting future risk of myocardial infarction: a GEO database-based study

Shaohua Li #  1 , Junwen Zhang #  2 , Jingwei Ni  3 , Jiumei Cao  4
Affiliations

Hypoxia-associated genes predicting future risk of myocardial infarction: a GEO database-based study

Shaohua Li et al. Front Cardiovasc Med. .

Abstract

Background: Patients with unstable angina (UA) are prone to myocardial infarction (MI) after an attack, yet the altered molecular expression profile therein remains unclear. The current work aims to identify the characteristic hypoxia-related genes associated with UA/MI and to develop a predictive model of hypoxia-related genes for the progression of UA to MI.

Methods and results: Gene expression profiles were obtained from the GEO database. Then, differential expression analysis and the WGCNA method were performed to select characteristic genes related to hypoxia. Subsequently, all 10 hypoxia-related genes were screened using the Lasso regression model and a classification model was established. The area under the ROC curve of 1 shows its excellent classification performance and is confirmed on the validation set. In parallel, we construct a nomogram based on these genes, showing the risk of MI in patients with UA. Patients with UA and MI had their immunological status determined using CIBERSORT. These 10 genes were primarily linked to B cells and some inflammatory cells, according to correlation analysis.

Conclusion: Overall, GWAS identified that the CSTF2F UA/MI risk gene promotes atherosclerosis, which provides the basis for the design of innovative cardiovascular drugs by targeting CSTF2F.

Keywords: CSTF2F; GEO; GWAS; WGCNA; coronary artery disease; myocardial infarction; unstable angina (UA).

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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
The workflow of the integrative bioinformatics analyses.
Figure 2
Figure 2
Difference analysis showed that hypoxia was associated with the development of coronary heart disease. (A) The volcano map shows the different results between UA and MI. (B) Hypoxia ssGSEA scores were estimated in the UA and MI cohort by performing ssGSEA with a hallmark gene set. (C) The volcano map shows the different results between high-hypoxia scores and low-hypoxia scores. (D) Venn diagram shows that disease differential genes overlap with hypoxia differential genes. (E) Enrichment analysis of disease-differential genes. UA: unstable angina; MI myocardial infarction; BP: Biological Process; CC: Cellular Component; MF: Molecular Function.
Figure 3
Figure 3
The weight gene co-expression network analysis showed that hypoxia was associated with the development of coronary heart disease. (A) Soft threshold selection process. (B) Cluster dendrogram. Each color represents one specific co-expression module. In the colored rows below the dendrogram, the two colored rows represent the original modules and the merged modules. (C-D) The differential expression of eigengenes in UA and MI, high hypoxia and low hypoxia (FDR corrected *P < 0.05, **P < 0.01, ***P < 0.001), respectively. (E) Turquoise gene significance and membership in hypoxia network. (F) Gene ontology and KEGG pathways enrichment in turquoise modules.
Figure 4
Figure 4
Construction of disease classification model. (A-B) The lasso regression top 10 genes in training data. (C) PCA analysis in train data and (F) test data. ROC analysis in train (D) data and (E) test data. (G) Correlation analysis of risk scores and model genes. (H) Model gene expression in high and low-risk groups.
Figure 5
Figure 5
Construction of the nomogram. (A) Comprehensive nomogram of 10 risk genes. (B) Calibration curve.
Figure 6
Figure 6
Consensus clustering of 10 important hypoxia-related genes in UA/MI. (A) Consensus matrices for k = 2. (B) Sankey diagram showing the relationship between UA, MI, and UAp, MIp modes. (C) Expression of 10 hypoxia-related genes in UAp, MIp modes. (D) Expression of 10 hypoxia-related genes among CN, UA, and MI groups.
Figure 7
Figure 7
Immune cell subsets analysis in UA/MI. (A) The proportion of immune cell subsets in UA/MI samples. (B) Differences in analysis of immune cell infiltration between UA and MI patients. (C) Differences in analysis of immune cell infiltration between high- and low-risk groups. (D) 10 hypoxia-related genes associated immune cells. NES: normalized enrichment score.
Figure 8
Figure 8
Correlation between mutations at disease-associated hypoxia gene loci and their expression. (A) Venn diagram among model genes, hypoxia differential genes, and differential genes between UA and MI. (B) Association of seven disease-related hypoxia genes with atherosclerosis. (C) Western blot validation for the relative protein expression levels of seven disease-related hypoxia genes (NEK4, MLYCD, LNPEP, NOM1, COMMD2, CSTF2T, PARG) among healthy volunteers, UA and MI patients. (D) Statistical results of protein bands in C. (E) VENN plot of CAD GWAS and eQTL analysis results of seven disease-related hypoxia genes. (F) Boxplots showing expression quantitative trait locus data from the GTEx dataset for a genome-wide association study of the SNP (rs2879627) association. (G) Normalized effect sizes for single-tissue expression quantitative trait loci normalized effect sizes for rs2879627 are shown for 48 different tissues in GTEx.
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