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      Identification of Differentially Expressed Genes and Pathways in Human Atrial Fibrillation by Bioinformatics Analysis

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          Abstract

          Introduction

          Atrial fibrillation (AF) is the most prevalent sustained cardiac arrhythmia, but the molecular mechanisms underlying AF are not known. We aimed to identify the pivotal genes and pathways involved in AF pathogenesis because they could become potential biomarkers and therapeutic targets of AF.

          Methods

          The microarray datasets of GSE31821 and GSE41177 were downloaded from the Gene Expression Omnibus database. After combining the two datasets, differentially expressed genes (DEGs) were screened by the Limma package. MicroRNAs (miRNAs) confirmed experimentally to have an interaction with AF were screened through the miRTarBase database. Target genes of miRNAs were predicted using the miRNet database, and the intersection between DEGs and target genes of miRNAs, which were defined as common genes (CGs), were analyzed. Functional and pathway-enrichment analyses of DEGs and CGs were performed using the databases DAVID and KOBAS. Protein–protein interaction (PPI) network, miRNA- messenger(m) RNA network, and drug-gene network was visualized. Finally, reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) was used to validate the expression of hub genes in the miRNA–mRNA network.

          Results

          Thirty-three CGs were acquired from the intersection of 65 DEGs from the integrated dataset and 9777 target genes of miRNAs. Fifteen “hub” genes were selected from the PPI network, and the miRNA-mRNA network, including 82 miRNAs and 9 target mRNAs, was constructed. Furthermore, with the validation by RT-qPCR, macrophage migration inhibitory factor ( MIF), MYC proto-oncogene, bHLH transcription factor ( MYC), inhibitor of differentiation 1 ( ID1), and C-X-C Motif Chemokine Receptor 4 ( CXCR4) were upregulated and superoxide Dismutase 2 ( SOD2) was downregulated in patients with AF compared with healthy controls. We also found MIF, MYC, and ID1 were enriched in the transforming growth factor (TGF)-β and Hippo signaling pathway.

          Conclusion

          We identified several pivotal genes and pathways involved in AF pathogenesis. MIF, MYC, and ID1 might participate in AF progression through the TGF-β and Hippo signaling pathways. Our study provided new insights into the mechanisms of action of AF.

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          Most cited references49

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          limma powers differential expression analyses for RNA-sequencing and microarray studies

          limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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            NCBI GEO: archive for functional genomics data sets—update

            The Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) is an international public repository for high-throughput microarray and next-generation sequence functional genomic data sets submitted by the research community. The resource supports archiving of raw data, processed data and metadata which are indexed, cross-linked and searchable. All data are freely available for download in a variety of formats. GEO also provides several web-based tools and strategies to assist users to query, analyse and visualize data. This article reports current status and recent database developments, including the release of GEO2R, an R-based web application that helps users analyse GEO data.
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              STRING v10: protein–protein interaction networks, integrated over the tree of life

              The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database (http://string-db.org) aims to provide a critical assessment and integration of protein–protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein–protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks.
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                Author and article information

                Journal
                Int J Gen Med
                Int J Gen Med
                ijgm
                International Journal of General Medicine
                Dove
                1178-7074
                05 January 2022
                2022
                : 15
                : 103-114
                Affiliations
                [1 ]Department of Cardiology, The Affiliated Hospital of Xuzhou Medical University , Xuzhou, Jiangsu, 221004, People’s Republic of China
                [2 ]Department of Cardiology, Shanghai Institute of Cardiovascular Diseases, Zhongshan Hospital and Institutes of Biomedical Sciences, Fudan University , Shanghai, 200032, People’s Republic of China
                [3 ]Department of General Practice, The Affiliated Hospital of Xuzhou Medical University , Xuzhou, Jiangsu, 221004, People’s Republic of China
                Author notes
                Correspondence: Defeng Pan Department of Cardiology, The Affiliated Hospital of Xuzhou Medical University , 99 Huaihai West Road, Xuzhou, 221004, Jiangsu, People’s Republic of China Tel +86 516-83262017 Email xzdefengpan@xzhmu.edu.cn
                [*]

                These authors contributed equally to this work

                Author information
                http://orcid.org/0000-0002-2974-309X
                http://orcid.org/0000-0003-2561-3260
                http://orcid.org/0000-0003-4807-7309
                http://orcid.org/0000-0003-1398-5411
                Article
                334122
                10.2147/IJGM.S334122
                8743500
                35023949
                e4c22ea0-3fa4-48f6-af69-006ec699157d
                © 2022 Pan et al.

                This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms ( https://www.dovepress.com/terms.php).

                History
                : 19 August 2021
                : 06 December 2021
                Page count
                Figures: 7, Tables: 2, References: 49, Pages: 12
                Funding
                Funded by: the Xuzhou Science and Technology Bureau to Defeng Pan;
                This work was supported by grant from the Xuzhou Science and Technology Bureau to Defeng Pan [grant number: KC20097].
                Categories
                Original Research

                Medicine
                atrial fibrillation,bioinformatics analysis,differentially expressed genes,pathways,protein–protein interaction network

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