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

被引:3
|
作者
Pan, Defeng [1 ]
Zhou, Yufei [2 ,3 ]
Xiao, Shengjue [1 ]
Hu, Yue [4 ]
Huan, Chunyan [1 ]
Wu, Qi [1 ]
Wang, Xiaotong [1 ]
Pan, Qinyuan [1 ]
Liu, Jie [1 ]
Zhu, Hong [1 ]
机构
[1] Xuzhou Med Univ, Dept Cardiol, Affiliated Hosp, 99 Huaihai West Rd, Xuzhou 221004, Jiangsu, Peoples R China
[2] Fudan Univ, Zhongshan Hosp, Shanghai Inst Cardiovasc Dis, Dept Cardiol, Shanghai 200032, Peoples R China
[3] Fudan Univ, Inst Biomed Sci, Shanghai 200032, Peoples R China
[4] Xuzhou Med Univ, Dept Gen Practice, Affiliated Hosp, Xuzhou 221004, Jiangsu, Peoples R China
关键词
atrial fibrillation; bioinformatics analysis; differentially expressed genes; pathways; protein-protein interaction network; HIPPO PATHWAY; ID1; FIBROSIS; BETA;
D O I
10.2147/IJGM.S334122
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
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)-beta 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-beta and Hippo signaling pathways. Our study provided new insights into the mechanisms of action of AF.
引用
收藏
页码:103 / 114
页数:12
相关论文
共 50 条
  • [21] Identification of differentially expressed genes, signaling pathways and immune infiltration in postmenopausal osteoporosis by integrated bioinformatics analysis
    Zhou, Xiaoli
    Chen, Yang
    Zhang, Zepei
    Miao, Jun
    Chen, Guangdong
    Qian, Zhiyong
    HELIYON, 2024, 10 (01)
  • [22] Identification of Differentially Expressed Genes and Signaling Pathways in Acute Myocardial Infarction Based on Integrated Bioinformatics Analysis
    Chen, Da-Qiu
    Kong, Xiang-Sheng
    Shen, Xue-Bin
    Huang, Mao-Zhi
    Zheng, Jian-Ping
    Sun, Jing
    Xu, Shang-Hua
    CARDIOVASCULAR THERAPEUTICS, 2019, 2019
  • [23] Identification of differentially expressed genes, signaling pathways and immune infiltration in rheumatoid arthritis by integrated bioinformatics analysis
    Yanzhi Ge
    Li Zhou
    Zuxiang Chen
    Yingying Mao
    Ting Li
    Peijian Tong
    Letian Shan
    Hereditas, 158
  • [24] Identification of differentially expressed genes and pathways in kidney of ANCA-associated vasculitis by integrated bioinformatics analysis
    Deng, Xu
    Gao, Junying
    Zhao, Fei
    RENAL FAILURE, 2022, 44 (01) : 204 - 216
  • [25] Identification of differentially expressed genes, signaling pathways and immune infiltration in rheumatoid arthritis by integrated bioinformatics analysis
    Ge, Yanzhi
    Zhou, Li
    Chen, Zuxiang
    Mao, Yingying
    Li, Ting
    Tong, Peijian
    Shan, Letian
    HEREDITAS, 2021, 158 (01)
  • [26] Identification of key genes in atrial fibrillation using bioinformatics analysis
    Liu, Yueheng
    Tang, Rui
    Zhao, Ye
    Jiang, Xuan
    Wang, Yuchao
    Gu, Tianxiang
    BMC CARDIOVASCULAR DISORDERS, 2020, 20 (01)
  • [27] Identification of key genes in atrial fibrillation using bioinformatics analysis
    Yueheng Liu
    Rui Tang
    Ye Zhao
    Xuan Jiang
    Yuchao Wang
    Tianxiang Gu
    BMC Cardiovascular Disorders, 20
  • [28] Analysis of Differentially Expressed Genes between Paroxysmal and Persistent Atrial Fibrillation
    W. Wang
    X. Jin
    Y. Li
    Z. Ning
    X. Li
    Russian Journal of Genetics, 2023, 59 : 1222 - 1232
  • [29] Analysis of Differentially Expressed Genes between Paroxysmal and Persistent Atrial Fibrillation
    Wang, W.
    Jin, X.
    Li, Y.
    Ning, Z.
    Li, X.
    RUSSIAN JOURNAL OF GENETICS, 2023, 59 (11) : 1222 - 1232
  • [30] Bioinformatics analysis of differentially expressed genes involved in human developmental chondrogenesis
    Zhou, Jian
    Li, Chenxi
    Yu, Anqi
    Jie, Shuo
    Du, Xiadong
    Liu, Tang
    Wang, Wanchun
    Luo, Yingquan
    MEDICINE, 2019, 98 (27)