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Network‐based computational approach to identify genetic links between cardiomyopathy and its risk factors
Cardiomyopathy (CMP) is a group of myocardial diseases that progressively impair cardiac function. The mechanisms underlying CMP development are poorly understood, but lifestyle factors are clearly implicated as risk factors. This study aimed to identify molecular biomarkers involved in inflammatory...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Institution of Engineering and Technology
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8687405/ https://www.ncbi.nlm.nih.gov/pubmed/32196466 http://dx.doi.org/10.1049/iet-syb.2019.0074 |
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author | Haidar, Md. Nasim Islam, M. Babul Chowdhury, Utpala Nanda Rahman, Md. Rezanur Huq, Fazlul Quinn, Julian M.W. Moni, Mohammad Ali |
author_facet | Haidar, Md. Nasim Islam, M. Babul Chowdhury, Utpala Nanda Rahman, Md. Rezanur Huq, Fazlul Quinn, Julian M.W. Moni, Mohammad Ali |
author_sort | Haidar, Md. Nasim |
collection | PubMed |
description | Cardiomyopathy (CMP) is a group of myocardial diseases that progressively impair cardiac function. The mechanisms underlying CMP development are poorly understood, but lifestyle factors are clearly implicated as risk factors. This study aimed to identify molecular biomarkers involved in inflammatory CMP development and progression using a systems biology approach. The authors analysed microarray gene expression datasets from CMP and tissues affected by risk factors including smoking, ageing factors, high body fat, clinical depression status, insulin resistance, high dietary red meat intake, chronic alcohol consumption, obesity, high‐calorie diet and high‐fat diet. The authors identified differentially expressed genes (DEGs) from each dataset and compared those from CMP and risk factor datasets to identify common DEGs. Gene set enrichment analyses identified metabolic and signalling pathways, including MAPK, RAS signalling and cardiomyopathy pathways. Protein–protein interaction (PPI) network analysis identified protein subnetworks and ten hub proteins (CDK2, ATM, CDT1, NCOR2, HIST1H4A, HIST1H4B, HIST1H4C, HIST1H4D, HIST1H4E and HIST1H4L). Five transcription factors (FOXC1, GATA2, FOXL1, YY1, CREB1) and five miRNAs were also identified in CMP. Thus the authors’ approach reveals candidate biomarkers that may enhance understanding of mechanisms underlying CMP and their link to risk factors. Such biomarkers may also be useful to develop new therapeutics for CMP. |
format | Online Article Text |
id | pubmed-8687405 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | The Institution of Engineering and Technology |
record_format | MEDLINE/PubMed |
spelling | pubmed-86874052022-02-16 Network‐based computational approach to identify genetic links between cardiomyopathy and its risk factors Haidar, Md. Nasim Islam, M. Babul Chowdhury, Utpala Nanda Rahman, Md. Rezanur Huq, Fazlul Quinn, Julian M.W. Moni, Mohammad Ali IET Syst Biol Research Article Cardiomyopathy (CMP) is a group of myocardial diseases that progressively impair cardiac function. The mechanisms underlying CMP development are poorly understood, but lifestyle factors are clearly implicated as risk factors. This study aimed to identify molecular biomarkers involved in inflammatory CMP development and progression using a systems biology approach. The authors analysed microarray gene expression datasets from CMP and tissues affected by risk factors including smoking, ageing factors, high body fat, clinical depression status, insulin resistance, high dietary red meat intake, chronic alcohol consumption, obesity, high‐calorie diet and high‐fat diet. The authors identified differentially expressed genes (DEGs) from each dataset and compared those from CMP and risk factor datasets to identify common DEGs. Gene set enrichment analyses identified metabolic and signalling pathways, including MAPK, RAS signalling and cardiomyopathy pathways. Protein–protein interaction (PPI) network analysis identified protein subnetworks and ten hub proteins (CDK2, ATM, CDT1, NCOR2, HIST1H4A, HIST1H4B, HIST1H4C, HIST1H4D, HIST1H4E and HIST1H4L). Five transcription factors (FOXC1, GATA2, FOXL1, YY1, CREB1) and five miRNAs were also identified in CMP. Thus the authors’ approach reveals candidate biomarkers that may enhance understanding of mechanisms underlying CMP and their link to risk factors. Such biomarkers may also be useful to develop new therapeutics for CMP. The Institution of Engineering and Technology 2020-04-01 /pmc/articles/PMC8687405/ /pubmed/32196466 http://dx.doi.org/10.1049/iet-syb.2019.0074 Text en © 2020 The Institution of Engineering and Technology https://creativecommons.org/licenses/by/3.0/This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/ (https://creativecommons.org/licenses/by/3.0/) ) |
spellingShingle | Research Article Haidar, Md. Nasim Islam, M. Babul Chowdhury, Utpala Nanda Rahman, Md. Rezanur Huq, Fazlul Quinn, Julian M.W. Moni, Mohammad Ali Network‐based computational approach to identify genetic links between cardiomyopathy and its risk factors |
title | Network‐based computational approach to identify genetic links between cardiomyopathy and its risk factors |
title_full | Network‐based computational approach to identify genetic links between cardiomyopathy and its risk factors |
title_fullStr | Network‐based computational approach to identify genetic links between cardiomyopathy and its risk factors |
title_full_unstemmed | Network‐based computational approach to identify genetic links between cardiomyopathy and its risk factors |
title_short | Network‐based computational approach to identify genetic links between cardiomyopathy and its risk factors |
title_sort | network‐based computational approach to identify genetic links between cardiomyopathy and its risk factors |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8687405/ https://www.ncbi.nlm.nih.gov/pubmed/32196466 http://dx.doi.org/10.1049/iet-syb.2019.0074 |
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