Cargando…

Identification of key genes involved in the recurrence of glioblastoma multiforme using weighted gene co-expression network analysis and differential expression analysis

Glioblastoma multiforme (GBM) is the most fatal malignancy, and despite extensive treatment, tumors inevitably recur. This study aimed to identify recurrence-associated molecules in GBM. The gene expression profile GSE139533, containing 70 primary and 47 recurrent GBM tissues and their corresponding...

Descripción completa

Detalles Bibliográficos
Autores principales: Ren, Peng, Wang, JingYa, Li, Lei, Lin, XiaoWan, Wu, GuangHan, Chen, JiaYi, Zeng, ZhiRui, Zhang, HongMei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8806787/
https://www.ncbi.nlm.nih.gov/pubmed/34238116
http://dx.doi.org/10.1080/21655979.2021.1943986
_version_ 1784643539312836608
author Ren, Peng
Wang, JingYa
Li, Lei
Lin, XiaoWan
Wu, GuangHan
Chen, JiaYi
Zeng, ZhiRui
Zhang, HongMei
author_facet Ren, Peng
Wang, JingYa
Li, Lei
Lin, XiaoWan
Wu, GuangHan
Chen, JiaYi
Zeng, ZhiRui
Zhang, HongMei
author_sort Ren, Peng
collection PubMed
description Glioblastoma multiforme (GBM) is the most fatal malignancy, and despite extensive treatment, tumors inevitably recur. This study aimed to identify recurrence-associated molecules in GBM. The gene expression profile GSE139533, containing 70 primary and 47 recurrent GBM tissues and their corresponding clinical traits, was downloaded from the Gene Expression Omnibus (GEO) database and used for weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis. After identifying the hub genes which differentially expressed in recurrent GBM tissues and in the gene modules correlated with recurrence, data from the Chinese Glioma Genome Atlas (CCGA) and The Cancer Genome Atlas (TCGA) databases were analyzed with GSE43378 to determine the relationship between hub genes and patient prognosis. The diagnostic value of the identified hub genes was verified using 52 GBM tissues. Three gene modules were correlated with recurrence and 2623 genes were clustered in these clinically significant modules. Among these, 13 genes – EHF, TRPM1, FXYD4, CDH15, LHX5, TP73, FBN3, TLX1, C1QL4, COL2A, SEC61G, NEUROD4 and GPR139 – were differentially expressed in recurrent GBM samples; low LHX5 and TLX1 expression predicted poor outcomes. LHX5 and TLX1 expression showed weak positive relationships with Karnofsky performance scale scores. Additionally, LHX5 and TLX1 expression was found to be decreased in our recurrent GBM samples compared with that in primary samples; these genes exhibited high diagnostic value in distinguishing recurrent samples from primary samples. Our findings indicate that LHX5 and TLX1 might be involved in GBM recurrence and act as potential biomarkers for this condition.
format Online
Article
Text
id pubmed-8806787
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Taylor & Francis
record_format MEDLINE/PubMed
spelling pubmed-88067872022-02-02 Identification of key genes involved in the recurrence of glioblastoma multiforme using weighted gene co-expression network analysis and differential expression analysis Ren, Peng Wang, JingYa Li, Lei Lin, XiaoWan Wu, GuangHan Chen, JiaYi Zeng, ZhiRui Zhang, HongMei Bioengineered Research Paper Glioblastoma multiforme (GBM) is the most fatal malignancy, and despite extensive treatment, tumors inevitably recur. This study aimed to identify recurrence-associated molecules in GBM. The gene expression profile GSE139533, containing 70 primary and 47 recurrent GBM tissues and their corresponding clinical traits, was downloaded from the Gene Expression Omnibus (GEO) database and used for weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis. After identifying the hub genes which differentially expressed in recurrent GBM tissues and in the gene modules correlated with recurrence, data from the Chinese Glioma Genome Atlas (CCGA) and The Cancer Genome Atlas (TCGA) databases were analyzed with GSE43378 to determine the relationship between hub genes and patient prognosis. The diagnostic value of the identified hub genes was verified using 52 GBM tissues. Three gene modules were correlated with recurrence and 2623 genes were clustered in these clinically significant modules. Among these, 13 genes – EHF, TRPM1, FXYD4, CDH15, LHX5, TP73, FBN3, TLX1, C1QL4, COL2A, SEC61G, NEUROD4 and GPR139 – were differentially expressed in recurrent GBM samples; low LHX5 and TLX1 expression predicted poor outcomes. LHX5 and TLX1 expression showed weak positive relationships with Karnofsky performance scale scores. Additionally, LHX5 and TLX1 expression was found to be decreased in our recurrent GBM samples compared with that in primary samples; these genes exhibited high diagnostic value in distinguishing recurrent samples from primary samples. Our findings indicate that LHX5 and TLX1 might be involved in GBM recurrence and act as potential biomarkers for this condition. Taylor & Francis 2021-07-08 /pmc/articles/PMC8806787/ /pubmed/34238116 http://dx.doi.org/10.1080/21655979.2021.1943986 Text en © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Paper
Ren, Peng
Wang, JingYa
Li, Lei
Lin, XiaoWan
Wu, GuangHan
Chen, JiaYi
Zeng, ZhiRui
Zhang, HongMei
Identification of key genes involved in the recurrence of glioblastoma multiforme using weighted gene co-expression network analysis and differential expression analysis
title Identification of key genes involved in the recurrence of glioblastoma multiforme using weighted gene co-expression network analysis and differential expression analysis
title_full Identification of key genes involved in the recurrence of glioblastoma multiforme using weighted gene co-expression network analysis and differential expression analysis
title_fullStr Identification of key genes involved in the recurrence of glioblastoma multiforme using weighted gene co-expression network analysis and differential expression analysis
title_full_unstemmed Identification of key genes involved in the recurrence of glioblastoma multiforme using weighted gene co-expression network analysis and differential expression analysis
title_short Identification of key genes involved in the recurrence of glioblastoma multiforme using weighted gene co-expression network analysis and differential expression analysis
title_sort identification of key genes involved in the recurrence of glioblastoma multiforme using weighted gene co-expression network analysis and differential expression analysis
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8806787/
https://www.ncbi.nlm.nih.gov/pubmed/34238116
http://dx.doi.org/10.1080/21655979.2021.1943986
work_keys_str_mv AT renpeng identificationofkeygenesinvolvedintherecurrenceofglioblastomamultiformeusingweightedgenecoexpressionnetworkanalysisanddifferentialexpressionanalysis
AT wangjingya identificationofkeygenesinvolvedintherecurrenceofglioblastomamultiformeusingweightedgenecoexpressionnetworkanalysisanddifferentialexpressionanalysis
AT lilei identificationofkeygenesinvolvedintherecurrenceofglioblastomamultiformeusingweightedgenecoexpressionnetworkanalysisanddifferentialexpressionanalysis
AT linxiaowan identificationofkeygenesinvolvedintherecurrenceofglioblastomamultiformeusingweightedgenecoexpressionnetworkanalysisanddifferentialexpressionanalysis
AT wuguanghan identificationofkeygenesinvolvedintherecurrenceofglioblastomamultiformeusingweightedgenecoexpressionnetworkanalysisanddifferentialexpressionanalysis
AT chenjiayi identificationofkeygenesinvolvedintherecurrenceofglioblastomamultiformeusingweightedgenecoexpressionnetworkanalysisanddifferentialexpressionanalysis
AT zengzhirui identificationofkeygenesinvolvedintherecurrenceofglioblastomamultiformeusingweightedgenecoexpressionnetworkanalysisanddifferentialexpressionanalysis
AT zhanghongmei identificationofkeygenesinvolvedintherecurrenceofglioblastomamultiformeusingweightedgenecoexpressionnetworkanalysisanddifferentialexpressionanalysis