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. 2023 May 29:16:2111-2123.
doi: 10.2147/IJGM.S407759. eCollection 2023.

Development and Validation of a Diagnostic Model Based on Hypoxia-Related Genes in Myocardial Infarction

Ke Jiang #  1 , Ling Kang #  1 , Andong Jiang  2 , Qiang Zhao  1
Affiliations

Development and Validation of a Diagnostic Model Based on Hypoxia-Related Genes in Myocardial Infarction

Ke Jiang et al. Int J Gen Med. .

Abstract

Purpose: Myocardial infarction (MI) is a common cardiovascular disease, and its underlying pathological mechanism remains unclear. We aimed to develop a diagnostic model to distinguish different subtypes of MI.

Patients and methods: The gene expression profiles of MI from the GEO database and hypoxia-related genes (HRGs) from MSigDB were downloaded. Then, the different MI subtypes based on HRGs were identified with unsupervised clustering. The difference of expression patterns and hypoxic-immune status among different subtypes of MI were investigated. The diagnostic model to distinguish the different subtypes of MI was developed and validated.

Results: Based on HRGs, MI samples were divided into two subtypes, cluster A and cluster B. A total of 211 genes showed significant changes in expression between the two subtypes. Cluster A was characterized by high hypoxia status and low immunity status. Based on weighted gene co-expression network analysis, ROC analysis and LASSO regression algorithm, 5 genes were identified as potential diagnostic markers. Finally, a diagnostic model based on these 5 genes was established, which can distinguish the two subtypes well.

Conclusion: The five hub genes, including ANKRD36, HLTF, KIF3A, OXCT1 and VPS13A, may be associated with the different subtypes of MI.

Keywords: diagnostic model; hypoxia; immune; myocardial infarction.

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Conflict of interest statement

All authors declare that they have no conflicts of interest in this work.

Figures

Figure 1
Figure 1
Differentially expressed gene (DEG) analysis. (A) The volcano plot of DEGs between MI and control group. (B) The heatmap of top 25 up- and down-regulated DEGs between MI and control group. (C) GO enrichment analysis of DEGs between MI and control group. (D) The KEGG analysis of DEGs between MI and control group.
Figure 2
Figure 2
Consensus clusters by HRGs in training sets (A–C) and validation sets (D–F). (A and D) Consensus clustering cumulative distribution function (CDF) for k = 2 to 5. (B and E) Relative change in area under the CDF curve for k = 2 to 5. (C and F) Consensus clustering matrix for k = 2.
Figure 3
Figure 3
Differentially expression analysis between two different subtypes in training sets. (A) The volcano plot of DEGs between cluster A and cluster B. (B) The heatmap of top 25 up- and down-regulated DEGs between cluster A and cluster B. (C) GSVA enrichment analysis showing the activation states of biological pathways between cluster A and cluster B.
Figure 4
Figure 4
Characteristics of IME cell infiltration between cluster A and cluster B group in training sets. (A) The abundance of each IME infiltrating cell in cluster A and cluster B group. (B) Difference activity of specific immune responses between cluster A and cluster B group. (C) Difference in immune score between cluster A and cluster B group. (D) Difference in the HLA-related gene expression between cluster A and cluster B group. (E) Differences in EMT pathways and hypoxia condition between cluster A and cluster B group. *Indicates p < 0.05; **Indicates p < 0.01; ***Indicates p < 0.001; ****Indicates p < 0.0001; ns indicates p > 0.05.
Figure 5
Figure 5
Hub module selection. (A) Determination of soft thresholding power in the WGCNA. (B) The cluster dendrogram of module eigengenes. (C) The module trait relationships. (D) A scatter plot of gene significance for MI versus the module membership in the turquoise module. (E) A scatter plot of gene significance for MI versus the module membership in the brown module.
Figure 6
Figure 6
Construction of diagnostic model. (A) LASSO coefficient profiles of 9 hub genes. (B) Selection of the optimal parameter (lambda) in the LASSO model. (C) ROC curves showing the predictive efficiency of diagnostic model in training sets. (D) ROC curves showing the predictive efficiency of diagnostic model in validation sets.
Figure 7
Figure 7
Correlation of candidate genes and immune cells and immune score. *Indicates p < 0.05; **Indicates p < 0.01; ***Indicates p < 0.001.
Figure 8
Figure 8
Expression validations of ANKRD36, HLTF, KIF3A, OXCT1 and VPS13A by RT-qPCR. (A) ANKRD36, (B) HLTF, (C) KIF3A, (D) OXCT1, (E) VPS13A. *p < 0.05.
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