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Prediction of marker genes associated with hypertension by bioinformatics analyses
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 normotens...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
D.A. Spandidos
2017
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5466388/ https://www.ncbi.nlm.nih.gov/pubmed/28560446 http://dx.doi.org/10.3892/ijmm.2017.3000 |
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author | Gao, Yuan Qi, Guo-Xian Jia, Zhi-Mei Sun, Ying-Xian |
author_facet | Gao, Yuan Qi, Guo-Xian Jia, Zhi-Mei Sun, Ying-Xian |
author_sort | Gao, Yuan |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5466388 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | D.A. Spandidos |
record_format | MEDLINE/PubMed |
spelling | pubmed-54663882017-06-15 Prediction of marker genes associated with hypertension by bioinformatics analyses Gao, Yuan Qi, Guo-Xian Jia, Zhi-Mei Sun, Ying-Xian Int J Mol Med Articles 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. D.A. Spandidos 2017-07 2017-05-25 /pmc/articles/PMC5466388/ /pubmed/28560446 http://dx.doi.org/10.3892/ijmm.2017.3000 Text en Copyright: © Gao et al. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. |
spellingShingle | Articles Gao, Yuan Qi, Guo-Xian Jia, Zhi-Mei Sun, Ying-Xian Prediction of marker genes associated with hypertension by bioinformatics analyses |
title | Prediction of marker genes associated with hypertension by bioinformatics analyses |
title_full | Prediction of marker genes associated with hypertension by bioinformatics analyses |
title_fullStr | Prediction of marker genes associated with hypertension by bioinformatics analyses |
title_full_unstemmed | Prediction of marker genes associated with hypertension by bioinformatics analyses |
title_short | Prediction of marker genes associated with hypertension by bioinformatics analyses |
title_sort | prediction of marker genes associated with hypertension by bioinformatics analyses |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5466388/ https://www.ncbi.nlm.nih.gov/pubmed/28560446 http://dx.doi.org/10.3892/ijmm.2017.3000 |
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