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2000
Volume 21, Issue 18
  • ISSN: 1570-1808
  • E-ISSN: 1875-628X

Abstract

Background

Acute myocardial infarction (MI) is a serious emergency disease with high mortality. Hypoxia is associated with unfavorable outcomes in cancer patients. Nevertheless, there remains a shortage of effective hypoxia-related biomarkers to forecast the prognosis of acute MI patients and to identify targeted therapies.

Methods

First, data on acute MI patient samples and hypoxia-related genes were obtained based on public databases. Hypoxia-related gene scores were calculated by single sample Gene Set Enrichment Analysis (ssGSEA). Hypoxia-related hub genes in acute MI were screened weighted correlation network analysis (WGCNA). Acute MI samples were analyzed for differentially expressed genes (DEGs) using the limma package and intersected with hub gene for hypoxia-related DEGs. Then, machine learning methods were used to identify hypoxia-related biomarkers in acute MI. Gene set enrichment analysis (GSEA) and immune infiltration analysis were performed on biomarkers. Targeted drug prediction and molecular docking were conducted based on biomarkers.

Results

The hypoxia-related gene score of the acute MI group was higher than the control group, and 319 hypoxia-related hub genes in acute MI were acquired. A total of 7 hypoxia-related DEGs were obtained by WGCNA and DEGs analysis. Then, 2 hypoxia-related biomarkers in acute MI, and , were identified based on machine learning algorithms. Both and were enriched in the ribosome and spliceosome pathways. The expression levels of and were correlated with immune cell infiltration. Furthermore, 8-hydroxyquinoline, perhexiline, and sotalol were selected as the targeted drugs, which could bind to and .

Conclusion

In short, we screened two important hypoxia-related biomarkers and three potential target drugs based on bioinformatics techniques. This provides new ideas and potential drug targets for early diagnosis and targeted therapy of acute MI.

© 2024 Bentham Science Publishers
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2025-09-02

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Novel Hypoxia-related Biomarkers and Targeted Drugs for Acute Myocardial Infarction Revealed by Bioinformatics
Letters in Drug Design & Discovery 21, 4529 (2024); https://doi.org/10.2174/0115701808357619241218071056
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  • Article Type:     Research Article
Keyword(s): Acute myocardial infarction; biomarker; drug discovery; hypoxia; machine learning; molecular docking

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