ArticlePeerJ2020
Identification of key genes and pathways affected in epicardial adipose tissue from patients with coronary artery disease by integrated bioinformatics analysis.
Article in PeerJ, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 25 citations in OpenAlex.
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- TRIM28 Fosters Microglia Ferroptosis via Autophagy Modulation to Enhance Neuropathic Pain and Neuroinflammation.Molecular neurobiology · 2024Article
- MiR-196a-5p hinders vascular smooth muscle cell proliferation and vascular remodeling via repressing BACH1 expression.Scientific reports · 2024Article
- Deciphering the emerging landscape of HOX genes in cardiovascular biology, atherosclerosis and beyond (Review).International journal of molecular medicine · 2024Article
- Advanced Platelet-Rich Fibrin Extract Treatment Promotes the Proliferation and Differentiation of Human Adipose-Derived Mesenchymal Stem Cells through Activation of Tryptophan Metabolism.Current stem cell research & therapy · 2023Article
- Potential effective diagnostic biomarker in patients with primary and metastatic small intestinal neuroendocrine tumors.Frontiers in genetics · 2023Article
- Profiling of Non-Coding Regulators and Their Targets in Epicardial Fat from Patients with Coronary Artery Disease.International journal of molecular sciences · 2022Article
- Ageing- and AAA-associated differentially expressed proteins identified by proteomic analysis in mice.PeerJ · 2022Article
- Epicardial Adipose Tissue: The Genetics Behind an Emerging Cardiovascular Risk Marker.Clinical Medicine Insights. Cardiology · 2021Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCoronary artery disease (CAD) is a common disease with high cost and mortality. Here, we studied the differentially expressed genes (DEGs) between epicardial adipose tissue (EAT) and subcutaneous adipose tissue (SAT) from patients with CAD to explore the possible pathways and mechanisms through which EAT participates in the CAD pathological process.
methodsMicroarray data for EAT and SAT were obtained from the Gene Expression Omnibus database, including three separate expression datasets: GSE24425, GSE64554 and GSE120774. The DEGs between EAT samples and SAT control samples were screened out using the limma package in the R language. Next, we conducted bioinformatic analysis of gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways to discover the enriched gene sets and pathways associated with DEGs. Simultaneously, gene set enrichment analysis was carried out to discover enriched gene functions and pathways from all expression data rather than DEGs. The PPI network was constructed to reveal the possible protein interactions consistent with CAD. Mcode and Cytohubba in Cytoscape revealed the possible key CAD genes. In the next step, the corresponding predicted microRNAs (miRNAs) were analysed using miRNA Data Integration Portal. RT-PCR was used to validate the bioinformatic results.
resultsThe three datasets had a total of 89 DEGs (FC log2 > 1 and
conclusionsBetween EAT and SAT in CAD patients, a total of 89 DEGs, and 10 key genes, including HOXA5, HOXB5, HOXC6, HOXC8, HOXB7, COL1A1, CCND1, CCL2, HP and TWIST1, and miRNAs hsa-miR-196a-5p and hsa-miR-196b-5p were predicted to play essential roles in CAD pathogenesis. Pro-inflammatory and immunological pathways could act as key EAT regulators by participating in the CAD pathological process.
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