Level D· Scientific groundwork from lab and animal studiesLaboratory StudyEurope PMCOpen access

Network-based computational approach to identify genetic links between cardiomyopathy and its risk factors

Haidar MN., Islam MB., Chowdhury UN., Rahman MR., Huq F., Quinn JMW.

Laboratory Study on Type 2 Diabetes, Cardiovascular Disease, published in IET Syst Biol (2020) — summary generated from the PubMed abstract.

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Level D· Scientific groundwork from lab and animal studiesEvidence level of this study

Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.

  • Level A · Stronger Clinical Evidence
  • Level B · Emerging clinical evidence with positive signals
  • Level C · Early human research exploring benefits
  • Level D · Scientific groundwork from lab and animal studies
  • Emerging · Emerging topic under active research
Read the A–D evidence level guide

This page is generated from the PubMed record. The Thai description is an automated summary of bibliographic fields and the abstract, not a full translation, and is not medical advice.

Study type
Laboratory Study
Journal
IET Syst Biol (2020)
Reported sample size
—
Source database
Europe PMC
PMID
32196466
PMCID
PMC8687405
DOI
10.1049/iet-syb.2019.0074
Citations
7

Abstract (original English)

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 ris

What this study does not prove

  • • This study does not prove SVF is an approved treatment or a replacement for standard care.
  • • This is preclinical work; animal or laboratory results cannot be applied to humans.

Evidence level

Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.

How we grade evidence
HumansCardiomyopathiesOligonucleotide Array Sequence AnalysisRisk FactorsGene Expression ProfilingComputational BiologyProtein Interaction Maps

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