Cargando…

Does single gene expression omnibus data mining analysis apply for only tumors and not mental illness? A preliminary study on bipolar disorder based on bioinformatics methodology

BACKGROUND: Bipolar disorder (BD), a common kind of mood disorder with frequent recurrence, high rates of additional comorbid conditions and poor compliance, has an unclear pathogenesis. The Gene Expression Omnibus (GEO) database is a gene expression database created and maintained by the National C...

Descripción completa

Detalles Bibliográficos
Autores principales: You, Xu, Zhang, Yunqiao, Long, Qing, Liu, Zijun, Feng, Ziqiao, Zhang, Wengyu, Teng, Zhaowei, Zeng, Yong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7458177/
https://www.ncbi.nlm.nih.gov/pubmed/32871949
http://dx.doi.org/10.1097/MD.0000000000021989
_version_ 1783576140272631808
author You, Xu
Zhang, Yunqiao
Long, Qing
Liu, Zijun
Feng, Ziqiao
Zhang, Wengyu
Teng, Zhaowei
Zeng, Yong
author_facet You, Xu
Zhang, Yunqiao
Long, Qing
Liu, Zijun
Feng, Ziqiao
Zhang, Wengyu
Teng, Zhaowei
Zeng, Yong
author_sort You, Xu
collection PubMed
description BACKGROUND: Bipolar disorder (BD), a common kind of mood disorder with frequent recurrence, high rates of additional comorbid conditions and poor compliance, has an unclear pathogenesis. The Gene Expression Omnibus (GEO) database is a gene expression database created and maintained by the National Center for Biotechnology Information. Researchers can download expression data online for bioinformatics analysis, especially for cancer research. However, there is little research on the use of such bioinformatics analysis methodologies for mental illness by downloading differential expression data from the GEO database. METHODS: Publicly available data were downloaded from the GEO database (GSE12649, GSE5388 and GSE5389), and differentially expressed genes (DEGs) were extracted by using the online tool GEO2R. A Venn diagram was used to screen out common DEGs between postmortem brain tissues and normal tissues. Functional annotation and pathway enrichment analysis of DEGs were performed by using Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses, respectively. Furthermore, a protein-protein interaction network was constructed to identify hub genes. RESULTS: A total of 289 DEGs were found, among which 5 of 10 hub genes [HSP90AA1, HSP90AB 1, UBE2N, UBE3A, and CUL1] were identified as susceptibility genes whose expression was downregulated. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that variations in these 5 hub genes were obviously enriched in protein folding, protein polyubiquitination, apoptotic process, protein binding, the ubiquitin-mediated proteolysis pathway, and protein processing in the endoplasmic reticulum pathway. These findings strongly suggested that HSP90AA1, UBE3A, and CUL 1, which had large areas under the curve in receiver operator curves (P < .05), were potential diagnostic markers for BD. CONCLUSION: Although there are 3 hub genes [HSP90AA1, UBE3A, and CUL 1] that are tightly correlated with the occurrence of BD, mainly based on routine bioinformatics methods for cancer-related disease, the feasibility of applying this single GEO bioinformatics approach for mental illness is questionable, given the significant differences between mental illness and cancer-related diseases.
format Online
Article
Text
id pubmed-7458177
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Lippincott Williams & Wilkins
record_format MEDLINE/PubMed
spelling pubmed-74581772020-09-11 Does single gene expression omnibus data mining analysis apply for only tumors and not mental illness? A preliminary study on bipolar disorder based on bioinformatics methodology You, Xu Zhang, Yunqiao Long, Qing Liu, Zijun Feng, Ziqiao Zhang, Wengyu Teng, Zhaowei Zeng, Yong Medicine (Baltimore) 5300 BACKGROUND: Bipolar disorder (BD), a common kind of mood disorder with frequent recurrence, high rates of additional comorbid conditions and poor compliance, has an unclear pathogenesis. The Gene Expression Omnibus (GEO) database is a gene expression database created and maintained by the National Center for Biotechnology Information. Researchers can download expression data online for bioinformatics analysis, especially for cancer research. However, there is little research on the use of such bioinformatics analysis methodologies for mental illness by downloading differential expression data from the GEO database. METHODS: Publicly available data were downloaded from the GEO database (GSE12649, GSE5388 and GSE5389), and differentially expressed genes (DEGs) were extracted by using the online tool GEO2R. A Venn diagram was used to screen out common DEGs between postmortem brain tissues and normal tissues. Functional annotation and pathway enrichment analysis of DEGs were performed by using Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses, respectively. Furthermore, a protein-protein interaction network was constructed to identify hub genes. RESULTS: A total of 289 DEGs were found, among which 5 of 10 hub genes [HSP90AA1, HSP90AB 1, UBE2N, UBE3A, and CUL1] were identified as susceptibility genes whose expression was downregulated. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that variations in these 5 hub genes were obviously enriched in protein folding, protein polyubiquitination, apoptotic process, protein binding, the ubiquitin-mediated proteolysis pathway, and protein processing in the endoplasmic reticulum pathway. These findings strongly suggested that HSP90AA1, UBE3A, and CUL 1, which had large areas under the curve in receiver operator curves (P < .05), were potential diagnostic markers for BD. CONCLUSION: Although there are 3 hub genes [HSP90AA1, UBE3A, and CUL 1] that are tightly correlated with the occurrence of BD, mainly based on routine bioinformatics methods for cancer-related disease, the feasibility of applying this single GEO bioinformatics approach for mental illness is questionable, given the significant differences between mental illness and cancer-related diseases. Lippincott Williams & Wilkins 2020-08-28 /pmc/articles/PMC7458177/ /pubmed/32871949 http://dx.doi.org/10.1097/MD.0000000000021989 Text en Copyright © 2020 the Author(s). Published by Wolters Kluwer Health, Inc. http://creativecommons.org/licenses/by-nc/4.0 This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc/4.0
spellingShingle 5300
You, Xu
Zhang, Yunqiao
Long, Qing
Liu, Zijun
Feng, Ziqiao
Zhang, Wengyu
Teng, Zhaowei
Zeng, Yong
Does single gene expression omnibus data mining analysis apply for only tumors and not mental illness? A preliminary study on bipolar disorder based on bioinformatics methodology
title Does single gene expression omnibus data mining analysis apply for only tumors and not mental illness? A preliminary study on bipolar disorder based on bioinformatics methodology
title_full Does single gene expression omnibus data mining analysis apply for only tumors and not mental illness? A preliminary study on bipolar disorder based on bioinformatics methodology
title_fullStr Does single gene expression omnibus data mining analysis apply for only tumors and not mental illness? A preliminary study on bipolar disorder based on bioinformatics methodology
title_full_unstemmed Does single gene expression omnibus data mining analysis apply for only tumors and not mental illness? A preliminary study on bipolar disorder based on bioinformatics methodology
title_short Does single gene expression omnibus data mining analysis apply for only tumors and not mental illness? A preliminary study on bipolar disorder based on bioinformatics methodology
title_sort does single gene expression omnibus data mining analysis apply for only tumors and not mental illness? a preliminary study on bipolar disorder based on bioinformatics methodology
topic 5300
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7458177/
https://www.ncbi.nlm.nih.gov/pubmed/32871949
http://dx.doi.org/10.1097/MD.0000000000021989
work_keys_str_mv AT youxu doessinglegeneexpressionomnibusdatamininganalysisapplyforonlytumorsandnotmentalillnessapreliminarystudyonbipolardisorderbasedonbioinformaticsmethodology
AT zhangyunqiao doessinglegeneexpressionomnibusdatamininganalysisapplyforonlytumorsandnotmentalillnessapreliminarystudyonbipolardisorderbasedonbioinformaticsmethodology
AT longqing doessinglegeneexpressionomnibusdatamininganalysisapplyforonlytumorsandnotmentalillnessapreliminarystudyonbipolardisorderbasedonbioinformaticsmethodology
AT liuzijun doessinglegeneexpressionomnibusdatamininganalysisapplyforonlytumorsandnotmentalillnessapreliminarystudyonbipolardisorderbasedonbioinformaticsmethodology
AT fengziqiao doessinglegeneexpressionomnibusdatamininganalysisapplyforonlytumorsandnotmentalillnessapreliminarystudyonbipolardisorderbasedonbioinformaticsmethodology
AT zhangwengyu doessinglegeneexpressionomnibusdatamininganalysisapplyforonlytumorsandnotmentalillnessapreliminarystudyonbipolardisorderbasedonbioinformaticsmethodology
AT tengzhaowei doessinglegeneexpressionomnibusdatamininganalysisapplyforonlytumorsandnotmentalillnessapreliminarystudyonbipolardisorderbasedonbioinformaticsmethodology
AT zengyong doessinglegeneexpressionomnibusdatamininganalysisapplyforonlytumorsandnotmentalillnessapreliminarystudyonbipolardisorderbasedonbioinformaticsmethodology