Prediction of marker genes associated with hypertension by bioinformatics analyses

被引:7
|
作者
Gao, Yuan [1 ]
Qi, Guo-Xian [1 ]
Jia, Zhi-Mei [1 ]
Sun, Ying-Xian [1 ]
机构
[1] China Med Univ, Dept Cardiol, Hosp 1, 155 Nanjing St, Shenyang 110001, Liaoning, Peoples R China
关键词
hypertension; differentially expressed genes; protein- protein interaction network; transcription factor; LYSYL OXIDASE; EXPRESSION; RAT; DYSFUNCTION; CYTOSCAPE; DISEASE; ALPHA; ARRAY;
D O I
10.3892/ijmm.2017.3000
中图分类号
R-3 [医学研究方法]; R3 [基础医学];
学科分类号
1001 ;
摘要
This study aimed to explore the underlying marker genes associated with hypertension by bioinformatics analyses. A gene expression profile (GSE54015) was downloaded. The differentially expressed genes (DEGs) between the normotensive female (NF) and hypertensive female (HF), and between the normotensive male (NM) and hypertensive male (HM) groups were analyzed. Gene Ontology (GO) and pathway enrichment analyses were performed, followed by protein-protein interaction (PPI) network construction. The transcription factors (TFs), and the common DEGs between the HF and HM groups were then analyzed. In total, 411 DEGs were identified between the HF and NF groups, and 418 DEGs were identified between the HM and NM groups. The upregulated DEGs in the HF and HM groups were enriched in 9 GO terms, including oxidation reduction, such as cytochrome P450, family 4, subfamily b, polypeptide 1 (Cyp4b1) and cytochrome P450, family 4, subfamily a, polypeptide 31 Cyp4a31). The downregulated DEGs were mainly enriched in GO terms related to hormone metabolic processes. In the PPI network, cytochrome P450, family 2, subfamily e, polypeptide 1 (Cyp2e1) had the highest degree in all 3 analysis methods in the HF group. Additionally, 4 TFs were indentified from the 2 groups of data, including sterol regulatory element binding transcription factor 1 (Srebf1), estrogen receptor 1 (Esr1), retinoid X receptor gamma (Rxrg) and peroxisome proliferator-activated receptor gamma (Pparg). The intersection genes were mainly enriched in GO terms related to the extracellular region. On the whole, our data indicate that the DEGs, Cyp4b1, Cyp4a31 and Loxl2, and the TFs, Esr1, Pparg and Rxrg, are associated with the progression of hypertension, and may thus serve as potential therapeutic targets in this disease.
引用
收藏
页码:137 / 145
页数:9
相关论文
共 50 条
  • [21] A Novel Bioinformatics Strategy for Function Prediction of Poorly-Characterized Protein Genes Obtained from Metagenome Analyses
    Abe, Takashi
    Kanaya, Shigehiko
    Uehara, Hiroshi
    Ikemura, Toshimichi
    DNA RESEARCH, 2009, 16 (05) : 287 - 297
  • [22] Identification of novel key genes associated with the metastasis of prostate cancer based on bioinformatics prediction and validation
    Feifeng Song
    Yiwen Zhang
    Zongfu Pan
    Xiaoping Hu
    Yaodong Yi
    Xiaochun Zheng
    Haibin Wei
    Ping Huang
    Cancer Cell International, 21
  • [23] Identification of novel key genes associated with the metastasis of prostate cancer based on bioinformatics prediction and validation
    Song, Feifeng
    Zhang, Yiwen
    Pan, Zongfu
    Hu, Xiaoping
    Yi, Yaodong
    Zheng, Xiaochun
    Wei, Haibin
    Huang, Ping
    CANCER CELL INTERNATIONAL, 2021, 21 (01)
  • [24] Prediction of target genes for miR-140-5p in pulmonary arterial hypertension using bioinformatics methods
    Li, Fangwei
    Shi, Wenhua
    Wan, Yixin
    Wang, Qingting
    Feng, Wei
    Yan, Xin
    Wang, Jian
    Chai, Limin
    Zhang, Qianqian
    Li, Manxiang
    FEBS OPEN BIO, 2017, 7 (12): : 1880 - 1890
  • [25] Prediction and Analysis of Key Genes in Glioblastoma Based on Bioinformatics
    Long, Hao
    Liang, Chaofeng
    Zhang, Xi'an
    Fang, Luxiong
    Wang, Gang
    Qi, Songtao
    Huo, Haizhong
    Song, Ye
    BIOMED RESEARCH INTERNATIONAL, 2017, 2017
  • [26] Multiple Bioinformatics Analyses of Integrated Gene Expression Profiling Data and Verification of Hub Genes Associated with Diabetic Retinopathy
    You, Jiaxin
    Qi, Shounan
    Du, Yang
    Wang, Chenguang
    Su, Guanfang
    MEDICAL SCIENCE MONITOR, 2020, 26
  • [27] Integrated Bioinformatics and Machine Learning Algorithms Analyses Highlight Related Pathways and Genes Associated with Alzheimer's Disease
    Zhang, Hui
    Liu, Qidong
    Sun, Xiaoru
    Xu, Yaru
    Fang, Yiling
    Cao, Silu
    Niu, Bing
    Li, Cheng
    CURRENT BIOINFORMATICS, 2022, 17 (03) : 284 - 295
  • [28] Bioinformatics analyses of immune-related genes and immune infiltration associated with lung ischemia-reperfusion injury
    Qian, Jing
    Xu, Zhanyu
    Yin, Mingjing
    Qin, Zhidan
    Pinhu, Liao
    TRANSPLANT IMMUNOLOGY, 2023, 81
  • [29] Identification of potential key genes and pathways in hepatitis B virus-associated hepatocellular carcinoma by bioinformatics analyses
    Zhang, Xiang
    Wang, Lingchen
    Yan, Yehong
    ONCOLOGY LETTERS, 2020, 19 (05) : 3477 - 3486
  • [30] Bioinformatics analyses of differentially expressed genes associated with bisphosphonate-related osteonecrosis of the jaw in patients with multiple myeloma
    Sun, Jingnan
    Wen, Xue
    Jin, Fengyan
    Li, Yuying
    Hu, Jifan
    Sun, Yunpeng
    ONCOTARGETS AND THERAPY, 2015, 8 : 2681 - 2688