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Integrative Identification of Hub Genes Associated With Immune Cells in Atrial Fibrillation Using Weighted Gene Correlation Network Analysis.

ABSTRACT: Background: Atrial fibrillation (AF) is the most common tachyarrhythmia in the clinic, leading to high morbidity and mortality. Although many studies on AF have been conducted, the molecular mechanism of AF has not been fully elucidated. This study was designed to explore the molecular mechanism of AF using integrative bioinformatics analysis and provide new insights into the pathophysiology of AF. Methods: The GSE115574 dataset was downloaded, and Cibersort was applied to estimate the relative expression of 22 kinds of immune cells. Differentially expressed genes (DEGs) were identified through the limma package in R language. Weighted gene correlation network analysis (WGCNA) was performed to cluster DEGs into different modules and explore relationships between modules and immune cell types. Functional enrichment analysis was performed on DEGs in the significant module, and hub genes were identified based on the protein-protein interaction (PPI) network. Hub genes were then verified using quantitative real-time polymerase chain reaction (qRT-PCR). Results: A total of 2,350 DEGs were identified and clustered into eleven modules using WGCNA. The magenta module with 246 genes was identified as the key module associated with M1 macrophages with the highest correlation coefficient. Three hub genes (CTSS, CSF2RB, and NCF2) were identified. The results verified using three other datasets and qRT-PCR demonstrated that the expression levels of these three genes in patients with AF were significantly higher than those in patients with SR, which were consistent with the bioinformatic analysis. Conclusion: Three novel genes identified using comprehensive bioinformatics analysis may play crucial roles in the pathophysiological mechanism in AF, which provide potential therapeutic targets and new insights into the treatment and early detection of AF.

SUBMITTER: Yan T

PROVIDER: S-EPMC7859264 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Integrative Identification of Hub Genes Associated With Immune Cells in Atrial Fibrillation Using Weighted Gene Correlation Network Analysis.

Yan Tao T Zhu Shijie S Zhu Miao M Wang Chunsheng C Guo Changfa C

Frontiers in cardiovascular medicine 20210121

<b>Background:</b> Atrial fibrillation (AF) is the most common tachyarrhythmia in the clinic, leading to high morbidity and mortality. Although many studies on AF have been conducted, the molecular mechanism of AF has not been fully elucidated. This study was designed to explore the molecular mechanism of AF using integrative bioinformatics analysis and provide new insights into the pathophysiology of AF. <b>Methods:</b> The GSE115574 dataset was downloaded, and Cibersort was applied to estimate ...[more]

PMID: 33553270

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Project description:BackgroundThe immune system significantly participates in the pathologic process of atrial fibrillation (AF). However, the molecular mechanisms underlying this participation are not completely explained. The current research aimed to identify critical genes and immune cells that participate in the pathologic process of AF.MethodsCIBERSORT was utilized to reveal the immune cell infiltration pattern in AF patients. Meanwhile, weighted gene coexpression network analysis (WGCNA) was utilized to identify meaningful modules that were significantly correlated with AF. The characteristic genes correlated with AF were identified by the least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine recursive feature elimination (SVM-RFE) algorithm.ResultsIn comparison to sinus rhythm (SR) individuals, we observed that fewer activated mast cells and regulatory T cells (Tregs), as well as more gamma delta T cells, resting mast cells, and M2 macrophages, were infiltrated in AF patients. Three significant modules (pink, red, and magenta) were identified to be significantly associated with AF. Gene enrichment analysis showed that all 717 genes were associated with immunity- or inflammation-related pathways and biological processes. Four hub genes (GALNT16, HTR2B, BEX2, and RAB8A) were revealed to be significantly correlated with AF by the SVM-RFE algorithm and LASSO logistic regression. qRT-PCR results suggested that compared to the SR subjects, AF patients exhibited significantly reduced BEX2 and GALNT16 expression, as well as dramatically elevated HTR2B expression. The AUC measurement showed that the diagnostic efficiency of BEX2, HTR2B, and GALNT16 in the training set was 0.836, 0.883, and 0.893, respectively, and 0.858, 0.861, and 0.915, respectively, in the validation set.ConclusionsThree novel genes, BEX2, HTR2B, and GALNT16, were identified by WGCNA combined with machine learning, which provides potential new therapeutic targets for the early diagnosis and prevention of AF.

Project description:Atrial fibrillation (AF) is the most common form of cardiac arrhythmia and significantly increases the risks of morbidity, mortality and health care expenditure; however, treatment for AF remains unsatisfactory due to the complicated and incompletely understood underlying mechanisms. In the present study, weighted gene co‑expression network analysis (WGCNA) was conducted to identify key modules and hub genes to determine their potential associations with AF. WGCNA was performed in an AF dataset GSE79768 obtained from the Gene Expression Omnibus, which contained data from paired left and right atria in cardiac patients with persistent AF or sinus rhythm. Differentially expressed gene (DEG) analysis was used to supplement and validate the results of WGCNA. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were also performed. Green and magenta modules were identified as the most critical modules associated with AF, from which 6 hub genes, acetyl‑CoA Acetyltransferase 1, death domain‑containing protein CRADD, gypsy retrotransposon integrase 1, FTX transcript, XIST regulator, transcription elongation factor A like 2 and minichromosome maintenance complex component 3 associated protein, were hypothesized to serve key roles in the pathophysiology of AF due to their increased intramodular connectivity. Functional enrichment analysis results demonstrated that the green module was associated with energy metabolism, and the magenta module may be associated with the Hippo pathway and contain multiple interactive pathways associated with apoptosis and inflammation. In addition, the blue module was identified to be an important regulatory module in AF with a higher specificity for the left atria, the genes of which were primarily correlated with complement, coagulation and extracellular matrix formation. These results suggest that may improve understanding of the underlying mechanisms of AF, and assist in identifying biomarkers and potential therapeutic targets for treating patients with AF.

Project description:BackgroundThe goal of this study is to identify the hub genes for Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD) via weighted correlation network analysis (WGCNA).MethodsThe gene expression profile of vastus lateralis biopsy samples obtained in 17 patients with DMD, 11 patients with BMD and 6 healthy individuals was downloaded from the Gene Expression Omnibus (GEO) database (GSE109178). After obtaining different expressed genes (DEGs) via GEO2R, WGCNA was conducted using R package, modules and genes that highly associated with DMD, BMD, and their age or pathology were screened. Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analysis and protein-protein interaction (PPI) network analysis were also conducted. Hub genes and highly correlated clustered genes were identified using Search Tool for the Retrieval of Interacting Genes (STRING) and Cystoscape software.ResultsOne thousand four hundred seventy DEGs were identified between DMD and control, with 1281 upregulated and 189 downregulated DEGs. Four hundred and twenty DEGs were found between BMD and control, with 157 upregulated and 263 upregulated DEGs. Fourteen modules with different colors were identified for DMD vs control, and 7 modules with different colors were identified for BMD vs control. Ten hub genes were summarized for DMD and BMD respectively, 5 hub genes were summarized for BMD age, 5 and 3 highly correlated clustered genes were summarized for DMD age and BMD pathology, respectively. In addition, 20 GO enrichments were found to be involved in DMD, 3 GO enrichments were found to be involved in BMD, 3 GO enrichments were found to be involved in BMD age.ConclusionIn DMD, several hub genes were identified: C3AR1, TLR7, IRF8, FYB and CD33(immune and inflammation associated genes), TYROBP, PLEK, AIF1(actin reorganization associated genes), LAPTM5 and NT5E(cell death and arterial calcification associated genes, respectively). In BMD, a number of hub genes were identified: LOX, ELN, PLEK, IKZF1, CTSK, THBS2, ADAMTS2, COL5A1(extracellular matrix associated genes), BCL2L1 and CDK2(cell cycle associated genes).

Dataset Information

Integrative Identification of Hub Genes Associated With Immune Cells in Atrial Fibrillation Using Weighted Gene Correlation Network Analysis.

Publications

Integrative Identification of Hub Genes Associated With Immune Cells in Atrial Fibrillation Using Weighted Gene Correlation Network Analysis.

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