Literature DB >> 26725916

Bioinformatics Analyses of Differentially Expressed Genes Associated with Acute Myocardial Infarction.

Yuan Gao1, Guo-xian Qi1, Liang Guo1, Ying-xian Sun1.   

Abstract

BACKGROUND: We aimed to predict key genes associated with acute myocardial infarction (AMI) by bioinformatics analysis.
METHODS: The microarray data of GSE48060, including peripheral blood samples from 31 first-time AMI patients within 48-h post-MI and 21 normal controls, were obtained from Gene Expression Omnibus database. The differentially expressed genes (DEGs) in AMI samples compared with normal controls were identified. Functional enrichment analysis was then performed, followed by analysis of protein-protein interaction (PPI) network and transcription regulatory network (TRN).
RESULTS: A total of 385 up- and 504 down-regulated DEGs were identified. They were mainly enriched in five pathways, such as natural killer (NK) cell-mediated cytotoxicity and chemokine signaling pathway. Chemokine (C-C motif) ligand 5 (CCL5) was hub protein in PPI network. Besides, four transcription factors (TFs), including nuclear receptor subfamily 2, group C, member 2 (NR2C2), MYC-associated factor X (MAX), general transcription factor IIIC, polypeptide 2, beta 110 kDa (GTF3C2), and B-cell CLL/lymphoma 3 (BCL3), were identified. Notably, nuclear receptor coactivator 7 (NCOA7) interacted with GTF3C2 and MAX directly.
CONCLUSIONS: CCL5, BCL3, NR2C2, MAX, GTF3C2, and NCOA7 might play important roles in AMI development.
© 2016 John Wiley & Sons Ltd.

Entities:  

Keywords:  Acute myocardial infarction; Differentially expressed genes; Protein-protein interaction network; Transcription factors; Transcription regulatory network

Mesh:

Year:  2016        PMID: 26725916     DOI: 10.1111/1755-5922.12171

Source DB:  PubMed          Journal:  Cardiovasc Ther        ISSN: 1755-5914            Impact factor:   3.023


  6 in total

1.  Identification of Differentially Expressed Genes and Signaling Pathways in Acute Myocardial Infarction Based on Integrated Bioinformatics Analysis.

Authors:  Da-Qiu Chen; Xiang-Sheng Kong; Xue-Bin Shen; Mao-Zhi Huang; Jian-Ping Zheng; Jing Sun; Shang-Hua Xu
Journal:  Cardiovasc Ther       Date:  2019-08-01       Impact factor: 3.023

2.  PIK3R1, SPNB2, and CRYAB as Potential Biomarkers for Patients with Diabetes and Developing Acute Myocardial Infarction.

Authors:  Yue Zheng; Yuheng Lang; Zhenchang Qi; Wenqing Gao; Xiaomin Hu; Tong Li
Journal:  Int J Endocrinol       Date:  2021-11-30       Impact factor: 3.257

3.  Low ZCCHC9 Gene Expression in Peripheral Blood May Be an Acute Myocardial Infarction Genetic Molecular Marker in Patients with Stable Coronary Atherosclerotic Disease.

Authors:  Lihong Li; Heyu Meng; Xue Wang; Jianjun Ruan; Xiaomin Tian; Fanbo Meng
Journal:  Int J Gen Med       Date:  2022-02-18

4.  Identification of potentially relevant genes for myocardial infarction using RNA sequencing data analysis.

Authors:  Qiang Zhao; Ke Wu; Nannan Li; Zhengmei Li; Fenglin Jin
Journal:  Exp Ther Med       Date:  2017-11-28       Impact factor: 2.447

5.  Biomarkers identification for acute myocardial infarction detection via weighted gene co-expression network analysis.

Authors:  Shu Zhang; Weixia Liu; Xiaoyan Liu; Jiaxin Qi; Chunmei Deng
Journal:  Medicine (Baltimore)       Date:  2017-11       Impact factor: 1.817

6.  Potential biomarkers of acute myocardial infarction based on co-expression network analysis.

Authors:  Zhaohui Hu; Ruhui Liu; Hairong Hu; Xiangjun Ding; Yuyao Ji; Guiyuan Li; Yiping Wang; Shengquan Xie; Xiaohong Liu; Zhiwen Ding
Journal:  Exp Ther Med       Date:  2021-12-21       Impact factor: 2.447

  6 in total

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