Molecular Signatures of Human Chronic Atrial Fibrillation in Primary Mitral Regurgitation

Deniz, Gunseli Cubukcuoglu; Durdu, Serkan; Doğan, Yeşim; Erdemlı, Esra; Özdağ, Hilal; Akar, Ahmet

doi:10.1155/2021/5516185

Cited by 21 publications

(11 citation statements)

References 34 publications

(45 reference statements)

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“…Studies have shown that mutations in COLQ can lead to congenital myasthenic syndrome (CMS), which causes cardiac autonomic dysfunction ( 32 , 33 ). Interestingly, Çubukçuoğlu et al ( 34 ) found that the expression of COLQ was higher in degenerative mitral regurgitation patients with AF than in those with SR ( P = 0.003), which is consistent with the results of our study.…”

Section: Discussionsupporting

confidence: 93%

Identification and validation of key genes associated with atrial fibrillation in the elderly

Liu

Zeng

Wu³

et al. 2023

Front. Cardiovasc. Med.

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BackgroundAtrial fibrillation (AF) is the most common cardiac arrhythmia and significantly increases the risk of stroke and heart failure (HF), contributing to a higher mortality rate. Increasing age is a major risk factor for AF; however, the mechanisms of how aging contributes to the occurrence and progression of AF remain unclear. This study conducted weighted gene co-expression network analysis (WGCNA) to identify key modules and hub genes and determine their potential associations with aging-related AF.Materials and methodsWGCNA was performed using the AF dataset GSE2240 obtained from the Gene Expression Omnibus, which contained data from atrial myocardium in cardiac patients with permanent AF or sinus rhythm (SR). Hub genes were identified in clinical samples. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were also performed.ResultsGreen and pink were the most critical modules associated with AF, from which nine hub genes, PTGDS, COLQ, ASTN2, VASH1, RCAN1, AMIGO2, RBP1, MFAP4, and ALDH1A1, were hypothesized to play key roles in the AF pathophysiology in elderly and seven of them have high diagnostic value. Functional enrichment analysis demonstrated that the green module was associated with the calcium, cyclic adenosine monophosphate (cAMP), and peroxisome proliferator-activated receptors (PPAR) signaling pathways, and the pink module may be associated with the transforming growth factor beta (TGF-β) signaling pathway in myocardial fibrosis.ConclusionWe identified nine genes that may play crucial roles in the pathophysiological mechanism of aging-related AF, among which six genes were associated with AF for the first time. This study provided novel insights into the impact of aging on the occurrence and progression of AF, and identified biomarkers and potential therapeutic targets for AF.

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Section: Discussionsupporting

confidence: 93%

Identification and validation of key genes associated with atrial fibrillation in the elderly

Liu

Zeng

Wu³

et al. 2023

Front. Cardiovasc. Med.

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“…The mucin subfamily is also known as transient receptor potential channels, is highly expressed in cardiac fibroblasts, and MCOLN3 , a member of the mucin subfamily, plays an important role in the regulation of calcium ion homeostasis ( 33 ). The downregulation of MCOLN3 has been shown to be closely associated with AF related to chronic primary mitral valve closure insufficiency ( 34 ).…”

Section: Discussionmentioning

confidence: 99%

Screening of atrial fibrillation diagnostic markers based on a GEO database chip and bioinformatics analysis

Wei¹,

Huang²,

Lu³

et al. 2022

J Thorac Dis

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Background: Study have shown that atrial fibrillation (AF) is a disease with genetic risk, and its pathogenesis is still unclear. This study sought to screen the gene microarray data of AF patients and to perform a bioinformatics analysis to identify AF signature diagnostic genes. Methods:The AF gene sets from the Gene Expression Omnibus (GEO) database were screened, and the differentially expressed genes (DEGs) were identified after the normalization of the data set by R software.We conducted a gene set enrichment analysis, a protein-protein interaction (PPI) network analysis, a gene-gene interaction (GGI) network analysis, and an immuno-infiltration analysis. The core genes were identified from the DEGs, and base on receiver operating characteristic, the top 5 core genes in the 2 data sets were selected as diagnostic factors and a nomogram was constructed. The miRNA of the core genes were predicted and an immune cell correlation analysis was performed.Results: A total of 20 DEGs were identified. The functions of these DEGs were mainly related to muscle contraction, autophagosome, and bone morphogenetic protein (BMP) binding, and focused on the calcium signaling pathway, ferroptosis, the extracellular matrix-receptor interaction, and other pathways. A total of 5 core genes [i.e., GPR22 (G protein-coupled receptor 22), COG5 (component of oligomeric golgi complex 5), GALNT16 (polypeptide N-acetylgalactosaminyltransferase 16), OTOGL (otogelin-like), and MCOLN3 (mucolipin 3)] were identified, and a linear model for risk prediction was constructed, which has good prediction ability. Plasma cells and Macrophages M2 were significantly increased in AF, while T cells follicular helper and Dendritic cells activated were significantly decreased.Conclusions: In our study, we identified 5 potential diagnostic key genes (i.e., GPR22, COG5, GALNT16, OTOGL, and MCOLN3). Our findings may provide a theoretical basis for susceptibility analyses and target drug development in AF.

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“…The microarray dataset GSE115574 was retrieved from the GEO database. This dataset contained 14 left atrial tissue samples from persistent AF patients [ 19 ]. The expression of all genes in every sample is shown in Figure 1 , and no significant outlier samples were found.…”

Section: Methodsmentioning

confidence: 99%

Identification of the Potential Molecular Mechanism of TGFBI Gene in Persistent Atrial Fibrillation

Guan

Liu

Huang

et al. 2022

Computational and Mathematical Methods in Medicine

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Background. Transforming growth factor beta-induced protein (TGFBI, encoded by TGFBI gene), is an extracellular matrix protein, widely expressed in variety of tissues. It binds to collagens type I, II, and IV and plays important roles in the interactions of cell with cell, collagen, and matrix. It has been reported to be associated with myocardial fibrosis, and the latter is an important pathophysiologyical basis of atrial fibrillation (AF). However, the mechanism of TGFBI in AF remains unclear. We aimed to detect the potential mechanism of TGFBI in AF via bioinformatics analysis. Methods. The microarray dataset of GSE115574 was examined to detect the genes coexpressed with TGFBI from 14 left atrial tissue samples of AF patients. TGFBI coexpression genes were then screened using the R package. Using online analytical tools, we determined the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, Gene Ontology (GO) annotation, and protein-protein interaction (PPI) network of TGFBI and its coexpression genes. The modules and hub genes of the PPI-network were then identified. Another dataset, GSE79768 was examined to verify the hub genes. DrugBank was used to detect the potential target drugs. Results. In GSE115574 dataset, a total of 1818 coexpression genes (769 positive and 1049 negative) were identified, enriched in 120 biological processes (BP), 38 cellular components (CC), 36 molecular functions (MF), and 39 KEGG pathways. A PPI-network with average 12.2-degree nodes was constructed. The genes clustered in the top module constructed from this network mainly play a role in PI3K-Akt signaling pathway, viral myocarditis, inflammatory bowel disease, and platelet activation. CXCL12, C3, FN1, COL1A2, ACTB, VCAM1, and MMP2 were identified and finally verified as the hub genes, mainly enriched in pathways like leukocyte transendothelial migration, PI3K-Akt signaling pathway, viral myocarditis, rheumatoid arthritis, and platelet activation. Pegcetacoplan, ocriplasmin, and carvedilol were the potential target drugs. Conclusions. We used microdataset to identify the potential functions and mechanisms of the TGFBI and its coexpression genes in AF patients. Our findings suggest that CXCL12, C3, FN1, COL1A2, ACTB, VCAM1, and MMP2 may be the hub genes.

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Molecular Signatures of Human Chronic Atrial Fibrillation in Primary Mitral Regurgitation

Cited by 21 publications

References 34 publications

Identification and validation of key genes associated with atrial fibrillation in the elderly

Identification and validation of key genes associated with atrial fibrillation in the elderly

Screening of atrial fibrillation diagnostic markers based on a GEO database chip and bioinformatics analysis

Identification of the Potential Molecular Mechanism of TGFBI Gene in Persistent Atrial Fibrillation

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