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

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...

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Autores principales: Pan, Defeng, Zhou, Yufei, Xiao, Shengjue, Hu, Yue, Huan, Chunyan, Wu, Qi, Wang, Xiaotong, Pan, Qinyuan, Liu, Jie, Zhu, Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8743500/
https://www.ncbi.nlm.nih.gov/pubmed/35023949
http://dx.doi.org/10.2147/IJGM.S334122
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author Pan, Defeng
Zhou, Yufei
Xiao, Shengjue
Hu, Yue
Huan, Chunyan
Wu, Qi
Wang, Xiaotong
Pan, Qinyuan
Liu, Jie
Zhu, Hong
author_facet Pan, Defeng
Zhou, Yufei
Xiao, Shengjue
Hu, Yue
Huan, Chunyan
Wu, Qi
Wang, Xiaotong
Pan, Qinyuan
Liu, Jie
Zhu, Hong
author_sort Pan, Defeng
collection PubMed
description 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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spelling pubmed-87435002022-01-11 Identification of Differentially Expressed Genes and Pathways in Human Atrial Fibrillation by Bioinformatics Analysis Pan, Defeng Zhou, Yufei Xiao, Shengjue Hu, Yue Huan, Chunyan Wu, Qi Wang, Xiaotong Pan, Qinyuan Liu, Jie Zhu, Hong Int J Gen Med Original Research 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. Dove 2022-01-05 /pmc/articles/PMC8743500/ /pubmed/35023949 http://dx.doi.org/10.2147/IJGM.S334122 Text en © 2022 Pan et al. https://creativecommons.org/licenses/by-nc/3.0/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/ (https://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).
spellingShingle Original Research
Pan, Defeng
Zhou, Yufei
Xiao, Shengjue
Hu, Yue
Huan, Chunyan
Wu, Qi
Wang, Xiaotong
Pan, Qinyuan
Liu, Jie
Zhu, Hong
Identification of Differentially Expressed Genes and Pathways in Human Atrial Fibrillation by Bioinformatics Analysis
title Identification of Differentially Expressed Genes and Pathways in Human Atrial Fibrillation by Bioinformatics Analysis
title_full Identification of Differentially Expressed Genes and Pathways in Human Atrial Fibrillation by Bioinformatics Analysis
title_fullStr Identification of Differentially Expressed Genes and Pathways in Human Atrial Fibrillation by Bioinformatics Analysis
title_full_unstemmed Identification of Differentially Expressed Genes and Pathways in Human Atrial Fibrillation by Bioinformatics Analysis
title_short Identification of Differentially Expressed Genes and Pathways in Human Atrial Fibrillation by Bioinformatics Analysis
title_sort identification of differentially expressed genes and pathways in human atrial fibrillation by bioinformatics analysis
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8743500/
https://www.ncbi.nlm.nih.gov/pubmed/35023949
http://dx.doi.org/10.2147/IJGM.S334122
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