Identification of potential crucial genes in atrial fibrillation: a bioinformatic analysis.

BMC Medical Genomics
Junguo ZhangGuowei Li

Abstract

Atrial fibrillation (AF) is at least partially heritable, affecting 2-3% of the population in Europe and the USA. However, a substantial proportion of heritability is still lacking. In the present study, we aim to identify potential crucial genes associated with AF through bioinformatic analyses of public datasets. Microarray data sets of GSE115574, GSE31821, GSE79768, GSE41177 and GSE14975 from the Gene Expression Omnibus (GEO) database were retrieved. After merging all microarray data and adjusting batch effect, differentially expressed genes (DEGs) were identified. Functional enrichment analyses based on Gene Ontology (GO) resource, Kyoto Encyclopedia of Genes and Genomes (KEGG) resource, Gene Set Enrichment Analysis (GSEA), Reactome Pathway Database and Disease Ontology (DO) were carried out. Protein-protein interaction (PPI) network was constructed using the STRING database. Combined with aforementioned significant bioinformatics information, potential crucial genes were subsequently selected. The comparative toxicogenomics database (CTD) was used to explore the interaction between potential crucial genes and AF. We identified 27 of DEGs with gene expression fold change (FC) ≥ 1.5 or ≤ 2/3 (|log2 FC| ≥ 0.58) and 5 with FC ...Continue Reading

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Citations

Oct 24, 2020·Molecular Diagnosis & Therapy·Rodrigo Haas Bueno, Mariana Recamonde-Mendoza
Mar 23, 2021·Computational and Mathematical Methods in Medicine·Shengjue XiaoDefeng Pan

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Datasets Mentioned

BETA
GSE41177
GSE14975

Software Mentioned

ReactomePA
MCODE
GSEA
Affymetrix Microarray Suite
limma
ggplot2
Cytoscape
DOSE
Molecular Complex Detection ( MCODE
ActivePerl

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