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Prediction of marker genes associated with hypertension by bioinformatics analyses

  • Authors:
    • Yuan Gao
    • Guo-Xian Qi
    • Zhi-Mei Jia
    • Ying-Xian Sun
  • View Affiliations / Copyright

    Affiliations: Department of Cardiology, The First Hospital of China Medical University, Shenyang, Liaoning 110001, P.R. China
    Copyright: © Gao et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Pages: 137-145
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    Published online on: May 25, 2017
       https://doi.org/10.3892/ijmm.2017.3000
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Abstract

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.
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Copy and paste a formatted citation
Spandidos Publications style
Gao Y, Qi G, Jia Z and Sun Y: Prediction of marker genes associated with hypertension by bioinformatics analyses. Int J Mol Med 40: 137-145, 2017.
APA
Gao, Y., Qi, G., Jia, Z., & Sun, Y. (2017). Prediction of marker genes associated with hypertension by bioinformatics analyses. International Journal of Molecular Medicine, 40, 137-145. https://doi.org/10.3892/ijmm.2017.3000
MLA
Gao, Y., Qi, G., Jia, Z., Sun, Y."Prediction of marker genes associated with hypertension by bioinformatics analyses". International Journal of Molecular Medicine 40.1 (2017): 137-145.
Chicago
Gao, Y., Qi, G., Jia, Z., Sun, Y."Prediction of marker genes associated with hypertension by bioinformatics analyses". International Journal of Molecular Medicine 40, no. 1 (2017): 137-145. https://doi.org/10.3892/ijmm.2017.3000
Copy and paste a formatted citation
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Spandidos Publications style
Gao Y, Qi G, Jia Z and Sun Y: Prediction of marker genes associated with hypertension by bioinformatics analyses. Int J Mol Med 40: 137-145, 2017.
APA
Gao, Y., Qi, G., Jia, Z., & Sun, Y. (2017). Prediction of marker genes associated with hypertension by bioinformatics analyses. International Journal of Molecular Medicine, 40, 137-145. https://doi.org/10.3892/ijmm.2017.3000
MLA
Gao, Y., Qi, G., Jia, Z., Sun, Y."Prediction of marker genes associated with hypertension by bioinformatics analyses". International Journal of Molecular Medicine 40.1 (2017): 137-145.
Chicago
Gao, Y., Qi, G., Jia, Z., Sun, Y."Prediction of marker genes associated with hypertension by bioinformatics analyses". International Journal of Molecular Medicine 40, no. 1 (2017): 137-145. https://doi.org/10.3892/ijmm.2017.3000
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