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Identification of Genetic Biomarkers for Diagnosis of Myocardial Infarction Compared with Angina Patients
BACKGROUND: Myocardial infarction (MI) is the most terrible appearance of cardiovascular disease. The incidence of heart failure, one of the complications of MI, has increased in the past few decades. Therefore, the identification of MI from angina patients and the determination of new diagnoses and...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7671815/ https://www.ncbi.nlm.nih.gov/pubmed/33224271 http://dx.doi.org/10.1155/2020/8535314 |
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author | Zhang, Yuanyuan Tian, Chunyang Liu, Xuejian Zhang, He |
author_facet | Zhang, Yuanyuan Tian, Chunyang Liu, Xuejian Zhang, He |
author_sort | Zhang, Yuanyuan |
collection | PubMed |
description | BACKGROUND: Myocardial infarction (MI) is the most terrible appearance of cardiovascular disease. The incidence of heart failure, one of the complications of MI, has increased in the past few decades. Therefore, the identification of MI from angina patients and the determination of new diagnoses and therapies of MI are increasingly important. The present study was aimed at identifying differentially expressed genes and miRNAs as biomarkers for the clinical and prognosis factors of MI compared with angina using microarray data analysis. METHODS: Differentially expressed miRNAs and genes were manifested by GEO2R. The biological function of differentially expressed genes (DEGs) was examined by GO and KEGG. The construction of a protein-protein network was explored by STRING. cytoHubba was utilized to screen hub genes. Analysis of miRNA-gene pairs was executed by the miRWalk 3.0 database. The miRNA-target pairs overlapped with hub genes were seen as key genes. Logistic regressive analysis was performed by SPSS. RESULTS: A number of 779 DEGs were recorded. The biological function containing extracellular components, signaling pathways, and cell adhesion was enriched. Twenty-four hub genes and three differentially expressed miRNAs were noted. Eight key genes were demonstrated, and 6 out of these 8 key genes were significantly related to clinical and prognosis factors following MI. CONCLUSIONS: CALCA, CDK6, MDM2, NRXN1, SOCS3, VEGFA, SMAD4, NCAM1, and hsa-miR-127-5p were thought to be potential diagnosis biomarkers for MI. Meanwhile, CALCA, CDK6, NRXN1, SMAD4, SOCS3, and NCAM1 were further identified to be potential diagnosis and therapy targets for MI. |
format | Online Article Text |
id | pubmed-7671815 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-76718152020-11-19 Identification of Genetic Biomarkers for Diagnosis of Myocardial Infarction Compared with Angina Patients Zhang, Yuanyuan Tian, Chunyang Liu, Xuejian Zhang, He Cardiovasc Ther Research Article BACKGROUND: Myocardial infarction (MI) is the most terrible appearance of cardiovascular disease. The incidence of heart failure, one of the complications of MI, has increased in the past few decades. Therefore, the identification of MI from angina patients and the determination of new diagnoses and therapies of MI are increasingly important. The present study was aimed at identifying differentially expressed genes and miRNAs as biomarkers for the clinical and prognosis factors of MI compared with angina using microarray data analysis. METHODS: Differentially expressed miRNAs and genes were manifested by GEO2R. The biological function of differentially expressed genes (DEGs) was examined by GO and KEGG. The construction of a protein-protein network was explored by STRING. cytoHubba was utilized to screen hub genes. Analysis of miRNA-gene pairs was executed by the miRWalk 3.0 database. The miRNA-target pairs overlapped with hub genes were seen as key genes. Logistic regressive analysis was performed by SPSS. RESULTS: A number of 779 DEGs were recorded. The biological function containing extracellular components, signaling pathways, and cell adhesion was enriched. Twenty-four hub genes and three differentially expressed miRNAs were noted. Eight key genes were demonstrated, and 6 out of these 8 key genes were significantly related to clinical and prognosis factors following MI. CONCLUSIONS: CALCA, CDK6, MDM2, NRXN1, SOCS3, VEGFA, SMAD4, NCAM1, and hsa-miR-127-5p were thought to be potential diagnosis biomarkers for MI. Meanwhile, CALCA, CDK6, NRXN1, SMAD4, SOCS3, and NCAM1 were further identified to be potential diagnosis and therapy targets for MI. Hindawi 2020-11-10 /pmc/articles/PMC7671815/ /pubmed/33224271 http://dx.doi.org/10.1155/2020/8535314 Text en Copyright © 2020 Yuanyuan Zhang et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Zhang, Yuanyuan Tian, Chunyang Liu, Xuejian Zhang, He Identification of Genetic Biomarkers for Diagnosis of Myocardial Infarction Compared with Angina Patients |
title | Identification of Genetic Biomarkers for Diagnosis of Myocardial Infarction Compared with Angina Patients |
title_full | Identification of Genetic Biomarkers for Diagnosis of Myocardial Infarction Compared with Angina Patients |
title_fullStr | Identification of Genetic Biomarkers for Diagnosis of Myocardial Infarction Compared with Angina Patients |
title_full_unstemmed | Identification of Genetic Biomarkers for Diagnosis of Myocardial Infarction Compared with Angina Patients |
title_short | Identification of Genetic Biomarkers for Diagnosis of Myocardial Infarction Compared with Angina Patients |
title_sort | identification of genetic biomarkers for diagnosis of myocardial infarction compared with angina patients |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7671815/ https://www.ncbi.nlm.nih.gov/pubmed/33224271 http://dx.doi.org/10.1155/2020/8535314 |
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