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Potential biomarkers of acute myocardial infarction based on co-expression network analysis

Acute myocardial infarction (AMI) is a common cause of death in numerous countries. Understanding the molecular mechanisms of the disease and analyzing potential biomarkers of AMI is crucial. However, specific diagnostic biomarkers have thus far not been fully established and candidate regulatory ta...

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Autores principales: Hu, Zhaohui, Liu, Ruhui, Hu, Hairong, Ding, Xiangjun, Ji, Yuyao, Li, Guiyuan, Wang, Yiping, Xie, Shengquan, Liu, Xiaohong, Ding, Zhiwen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: D.A. Spandidos 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8753964/
https://www.ncbi.nlm.nih.gov/pubmed/35069843
http://dx.doi.org/10.3892/etm.2021.11085
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author Hu, Zhaohui
Liu, Ruhui
Hu, Hairong
Ding, Xiangjun
Ji, Yuyao
Li, Guiyuan
Wang, Yiping
Xie, Shengquan
Liu, Xiaohong
Ding, Zhiwen
author_facet Hu, Zhaohui
Liu, Ruhui
Hu, Hairong
Ding, Xiangjun
Ji, Yuyao
Li, Guiyuan
Wang, Yiping
Xie, Shengquan
Liu, Xiaohong
Ding, Zhiwen
author_sort Hu, Zhaohui
collection PubMed
description Acute myocardial infarction (AMI) is a common cause of death in numerous countries. Understanding the molecular mechanisms of the disease and analyzing potential biomarkers of AMI is crucial. However, specific diagnostic biomarkers have thus far not been fully established and candidate regulatory targets for AMI remain to be determined. In the present study, the AMI gene chip dataset GSE48060 comprising blood samples from control subjects with normal cardiac function (n=21) and patients with AMI (n=26) was downloaded from Gene Expression Omnibus. The differentially expressed genes (DEGs) between the AMI and control groups were identified with the online tool GEO2R. The co-expression network of DEGs was analyzed by calculating the Pearson correlation coefficient of all gene pairs, mutual rank screening and cutoff threshold screening. Subsequently, the Gene Ontology (GO) database was used to analyze the genes' functions and pathway enrichment of genes in the most important modules was performed. Kyoto Encyclopedia of Genes and Genomes (KEGG) Disease and BioCyc were used to analyze the hub genes in the module to determine important sub-pathways. In addition, the expression of hub genes was confirmed by reverse transcription-quantitative PCR in AMI and control specimens. In the present study, 52 DEGs, including 26 upregulated and 26 downregulated genes, were identified. As key hub genes, three upregulated genes (AKR1C3, RPS24 and P2RY12) and three downregulated genes (ACSL1, B3GNT5 and MGAM) were identified from the co-expression network. Furthermore, GO enrichment analysis of all AMI co-expression network genes revealed functional enrichment mainly in ‘RAGE receptor binding’ and ‘negative regulation of T cell cytokine production’. In addition, KEGG Disease and BioCyc analysis indicated functional enrichment of the genes RPS24 and P2RY12 in ‘cardiovascular diseases’, of AKR1C3 in ‘cardenolide biosynthesis’, of MGAM in ‘glycogenolysis’, of B3GNT5 in ‘glycosphingolipid biosynthesis’ and of ACSL1 in ‘icosapentaenoate biosynthesis II’. In conclusion, the hub genes AKR1C3, RPS24, P2RY12, ACSL1, B3GNT5 and MGAM are potential markers of AMI, and have potential application value in the diagnosis of AMI.
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spelling pubmed-87539642022-01-21 Potential biomarkers of acute myocardial infarction based on co-expression network analysis Hu, Zhaohui Liu, Ruhui Hu, Hairong Ding, Xiangjun Ji, Yuyao Li, Guiyuan Wang, Yiping Xie, Shengquan Liu, Xiaohong Ding, Zhiwen Exp Ther Med Articles Acute myocardial infarction (AMI) is a common cause of death in numerous countries. Understanding the molecular mechanisms of the disease and analyzing potential biomarkers of AMI is crucial. However, specific diagnostic biomarkers have thus far not been fully established and candidate regulatory targets for AMI remain to be determined. In the present study, the AMI gene chip dataset GSE48060 comprising blood samples from control subjects with normal cardiac function (n=21) and patients with AMI (n=26) was downloaded from Gene Expression Omnibus. The differentially expressed genes (DEGs) between the AMI and control groups were identified with the online tool GEO2R. The co-expression network of DEGs was analyzed by calculating the Pearson correlation coefficient of all gene pairs, mutual rank screening and cutoff threshold screening. Subsequently, the Gene Ontology (GO) database was used to analyze the genes' functions and pathway enrichment of genes in the most important modules was performed. Kyoto Encyclopedia of Genes and Genomes (KEGG) Disease and BioCyc were used to analyze the hub genes in the module to determine important sub-pathways. In addition, the expression of hub genes was confirmed by reverse transcription-quantitative PCR in AMI and control specimens. In the present study, 52 DEGs, including 26 upregulated and 26 downregulated genes, were identified. As key hub genes, three upregulated genes (AKR1C3, RPS24 and P2RY12) and three downregulated genes (ACSL1, B3GNT5 and MGAM) were identified from the co-expression network. Furthermore, GO enrichment analysis of all AMI co-expression network genes revealed functional enrichment mainly in ‘RAGE receptor binding’ and ‘negative regulation of T cell cytokine production’. In addition, KEGG Disease and BioCyc analysis indicated functional enrichment of the genes RPS24 and P2RY12 in ‘cardiovascular diseases’, of AKR1C3 in ‘cardenolide biosynthesis’, of MGAM in ‘glycogenolysis’, of B3GNT5 in ‘glycosphingolipid biosynthesis’ and of ACSL1 in ‘icosapentaenoate biosynthesis II’. In conclusion, the hub genes AKR1C3, RPS24, P2RY12, ACSL1, B3GNT5 and MGAM are potential markers of AMI, and have potential application value in the diagnosis of AMI. D.A. Spandidos 2022-02 2021-12-21 /pmc/articles/PMC8753964/ /pubmed/35069843 http://dx.doi.org/10.3892/etm.2021.11085 Text en Copyright: © Hu et al. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
spellingShingle Articles
Hu, Zhaohui
Liu, Ruhui
Hu, Hairong
Ding, Xiangjun
Ji, Yuyao
Li, Guiyuan
Wang, Yiping
Xie, Shengquan
Liu, Xiaohong
Ding, Zhiwen
Potential biomarkers of acute myocardial infarction based on co-expression network analysis
title Potential biomarkers of acute myocardial infarction based on co-expression network analysis
title_full Potential biomarkers of acute myocardial infarction based on co-expression network analysis
title_fullStr Potential biomarkers of acute myocardial infarction based on co-expression network analysis
title_full_unstemmed Potential biomarkers of acute myocardial infarction based on co-expression network analysis
title_short Potential biomarkers of acute myocardial infarction based on co-expression network analysis
title_sort potential biomarkers of acute myocardial infarction based on co-expression network analysis
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8753964/
https://www.ncbi.nlm.nih.gov/pubmed/35069843
http://dx.doi.org/10.3892/etm.2021.11085
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