Cargando…
Bioinformatics analysis of gene expression profiling for identification of potential key genes among ischemic stroke
This study aimed to identify the key differentially expressed genes (DEGs) following ischemic stroke (IS). The GSE22255 microarray dataset, which contains samples from peripheral blood mononuclear cells of 20 IS patients and 20 sex- and age-matched controls, was downloaded from the Gene Expression O...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Wolters Kluwer Health
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5571993/ https://www.ncbi.nlm.nih.gov/pubmed/28834871 http://dx.doi.org/10.1097/MD.0000000000007564 |
_version_ | 1783259445561655296 |
---|---|
author | Zhai, Kaihua Kong, Xiangli Liu, Boyu Lou, Jiyu |
author_facet | Zhai, Kaihua Kong, Xiangli Liu, Boyu Lou, Jiyu |
author_sort | Zhai, Kaihua |
collection | PubMed |
description | This study aimed to identify the key differentially expressed genes (DEGs) following ischemic stroke (IS). The GSE22255 microarray dataset, which contains samples from peripheral blood mononuclear cells of 20 IS patients and 20 sex- and age-matched controls, was downloaded from the Gene Expression Omnibus. After data pre-processing, DEGs were identified using the Linear Models for Microarray Data package in R. The Search Tool for the Retrieval of Interacting Genes database was used to predict the interactions among the products of DEGs, and then Cytoscape software was used to visualize the protein–protein interaction (PPI) network. DEGs in the PPI network were then analyzed using the Database for Annotation, Visualization, and Integrated Discovery online software to predict their underlying functions through functional and pathway enrichment analyses. A total of 144 DEGs were identified in IS samples compared with control samples, including 75 upregulated and 69 downregulated genes. Genes with higher degrees in the PPI network included FOS (degree = 26), TP53 (degree = 22), JUN (degree = 20), EGR1 (degree = 18), JUNB (degree = 16), and ATF3 (degree = 15), and these genes may function in IS by interacting with each other (e.g., EGR1-JUN, EGR1-TP53, ATF3-FOS, and JUNB-FOS). Functional enrichment analysis indicated that the downregulated TP53 gene was enriched in immune response and protein targeting categories. ATF3 and EGR1 may have an important protective effect on IS, whereas FOS, JUN, and JUNB may be associated with the development of IS. In addition, TP53 may function as an indicator of poor prognosis for IS through its association with the immune response and protein targeting. |
format | Online Article Text |
id | pubmed-5571993 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Wolters Kluwer Health |
record_format | MEDLINE/PubMed |
spelling | pubmed-55719932017-09-06 Bioinformatics analysis of gene expression profiling for identification of potential key genes among ischemic stroke Zhai, Kaihua Kong, Xiangli Liu, Boyu Lou, Jiyu Medicine (Baltimore) 5300 This study aimed to identify the key differentially expressed genes (DEGs) following ischemic stroke (IS). The GSE22255 microarray dataset, which contains samples from peripheral blood mononuclear cells of 20 IS patients and 20 sex- and age-matched controls, was downloaded from the Gene Expression Omnibus. After data pre-processing, DEGs were identified using the Linear Models for Microarray Data package in R. The Search Tool for the Retrieval of Interacting Genes database was used to predict the interactions among the products of DEGs, and then Cytoscape software was used to visualize the protein–protein interaction (PPI) network. DEGs in the PPI network were then analyzed using the Database for Annotation, Visualization, and Integrated Discovery online software to predict their underlying functions through functional and pathway enrichment analyses. A total of 144 DEGs were identified in IS samples compared with control samples, including 75 upregulated and 69 downregulated genes. Genes with higher degrees in the PPI network included FOS (degree = 26), TP53 (degree = 22), JUN (degree = 20), EGR1 (degree = 18), JUNB (degree = 16), and ATF3 (degree = 15), and these genes may function in IS by interacting with each other (e.g., EGR1-JUN, EGR1-TP53, ATF3-FOS, and JUNB-FOS). Functional enrichment analysis indicated that the downregulated TP53 gene was enriched in immune response and protein targeting categories. ATF3 and EGR1 may have an important protective effect on IS, whereas FOS, JUN, and JUNB may be associated with the development of IS. In addition, TP53 may function as an indicator of poor prognosis for IS through its association with the immune response and protein targeting. Wolters Kluwer Health 2017-08-25 /pmc/articles/PMC5571993/ /pubmed/28834871 http://dx.doi.org/10.1097/MD.0000000000007564 Text en Copyright © 2017 the Author(s). Published by Wolters Kluwer Health, Inc. http://creativecommons.org/licenses/by-nc-nd/4.0 This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0 |
spellingShingle | 5300 Zhai, Kaihua Kong, Xiangli Liu, Boyu Lou, Jiyu Bioinformatics analysis of gene expression profiling for identification of potential key genes among ischemic stroke |
title | Bioinformatics analysis of gene expression profiling for identification of potential key genes among ischemic stroke |
title_full | Bioinformatics analysis of gene expression profiling for identification of potential key genes among ischemic stroke |
title_fullStr | Bioinformatics analysis of gene expression profiling for identification of potential key genes among ischemic stroke |
title_full_unstemmed | Bioinformatics analysis of gene expression profiling for identification of potential key genes among ischemic stroke |
title_short | Bioinformatics analysis of gene expression profiling for identification of potential key genes among ischemic stroke |
title_sort | bioinformatics analysis of gene expression profiling for identification of potential key genes among ischemic stroke |
topic | 5300 |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5571993/ https://www.ncbi.nlm.nih.gov/pubmed/28834871 http://dx.doi.org/10.1097/MD.0000000000007564 |
work_keys_str_mv | AT zhaikaihua bioinformaticsanalysisofgeneexpressionprofilingforidentificationofpotentialkeygenesamongischemicstroke AT kongxiangli bioinformaticsanalysisofgeneexpressionprofilingforidentificationofpotentialkeygenesamongischemicstroke AT liuboyu bioinformaticsanalysisofgeneexpressionprofilingforidentificationofpotentialkeygenesamongischemicstroke AT loujiyu bioinformaticsanalysisofgeneexpressionprofilingforidentificationofpotentialkeygenesamongischemicstroke |