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Development of an Immune-Related Prognostic Index Associated With Glioblastoma

Background: Although the tumor microenvironment (TME) is known to influence the prognosis of glioblastoma (GBM), the underlying mechanisms are not clear. This study aims to identify hub genes in the TME that affect the prognosis of GBM. Methods: The transcriptome profiles of the central nervous syst...

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Autores principales: Jiang, Zhengye, Shi, Yanxi, Zhao, Wenpeng, Zhang, Yaya, Xie, Yuanyuan, Zhang, Bingchang, Tan, Guowei, Wang, Zhanxiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8172186/
https://www.ncbi.nlm.nih.gov/pubmed/34093386
http://dx.doi.org/10.3389/fneur.2021.610797
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author Jiang, Zhengye
Shi, Yanxi
Zhao, Wenpeng
Zhang, Yaya
Xie, Yuanyuan
Zhang, Bingchang
Tan, Guowei
Wang, Zhanxiang
author_facet Jiang, Zhengye
Shi, Yanxi
Zhao, Wenpeng
Zhang, Yaya
Xie, Yuanyuan
Zhang, Bingchang
Tan, Guowei
Wang, Zhanxiang
author_sort Jiang, Zhengye
collection PubMed
description Background: Although the tumor microenvironment (TME) is known to influence the prognosis of glioblastoma (GBM), the underlying mechanisms are not clear. This study aims to identify hub genes in the TME that affect the prognosis of GBM. Methods: The transcriptome profiles of the central nervous systems of GBM patients were downloaded from The Cancer Genome Atlas (TCGA). The ESTIMATE scoring algorithm was used to calculate immune and stromal scores. The application of these scores in histology classification was tested. Univariate Cox regression analysis was conducted to identify genes with prognostic value. Subsequently, functional enrichment analysis and protein–protein interaction (PPI) network analysis were performed to reveal the pathways and biological functions associated with the genes. Next, these prognosis genes were validated in an independent GBM cohort from the Chinese Glioma Genome Atlas (CGGA). Finally, the efficacy of current antitumor drugs targeting these genes against glioma was evaluated. Results: Gene expression profiles and clinical data of 309 GBM samples were obtained from TCGA database. Higher immune and stromal scores were found to be significantly correlated with tissue type and poor overall survival (OS) (p = 0.15 and 0.77, respectively). Functional enrichment analysis identified 860 upregulated and 162 downregulated cross genes, which were mainly linked to immune response, inflammatory response, cell membrane, and receptor activity. Survival analysis identified 228 differentially expressed genes associated with the prognosis of GBM (p ≤ 0.05). A total of 48 hub genes were identified by the Cytoscape tool, and pathway enrichment analysis of the genes was performed using Database for Annotation, Visualization and Integrated Discovery (DAVID). The 228 genes were validated in an independent GBM cohort from the CGGA. In total, 10 genes were found to be significantly associated with prognosis of GBM. Finally, 14 antitumor drugs were identified by drug–gene interaction analysis. Conclusions: Here, 10 TME-related genes and 14 corresponding antitumor agents were found to be associated with the prognosis and OS of GBM.
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spelling pubmed-81721862021-06-03 Development of an Immune-Related Prognostic Index Associated With Glioblastoma Jiang, Zhengye Shi, Yanxi Zhao, Wenpeng Zhang, Yaya Xie, Yuanyuan Zhang, Bingchang Tan, Guowei Wang, Zhanxiang Front Neurol Neurology Background: Although the tumor microenvironment (TME) is known to influence the prognosis of glioblastoma (GBM), the underlying mechanisms are not clear. This study aims to identify hub genes in the TME that affect the prognosis of GBM. Methods: The transcriptome profiles of the central nervous systems of GBM patients were downloaded from The Cancer Genome Atlas (TCGA). The ESTIMATE scoring algorithm was used to calculate immune and stromal scores. The application of these scores in histology classification was tested. Univariate Cox regression analysis was conducted to identify genes with prognostic value. Subsequently, functional enrichment analysis and protein–protein interaction (PPI) network analysis were performed to reveal the pathways and biological functions associated with the genes. Next, these prognosis genes were validated in an independent GBM cohort from the Chinese Glioma Genome Atlas (CGGA). Finally, the efficacy of current antitumor drugs targeting these genes against glioma was evaluated. Results: Gene expression profiles and clinical data of 309 GBM samples were obtained from TCGA database. Higher immune and stromal scores were found to be significantly correlated with tissue type and poor overall survival (OS) (p = 0.15 and 0.77, respectively). Functional enrichment analysis identified 860 upregulated and 162 downregulated cross genes, which were mainly linked to immune response, inflammatory response, cell membrane, and receptor activity. Survival analysis identified 228 differentially expressed genes associated with the prognosis of GBM (p ≤ 0.05). A total of 48 hub genes were identified by the Cytoscape tool, and pathway enrichment analysis of the genes was performed using Database for Annotation, Visualization and Integrated Discovery (DAVID). The 228 genes were validated in an independent GBM cohort from the CGGA. In total, 10 genes were found to be significantly associated with prognosis of GBM. Finally, 14 antitumor drugs were identified by drug–gene interaction analysis. Conclusions: Here, 10 TME-related genes and 14 corresponding antitumor agents were found to be associated with the prognosis and OS of GBM. Frontiers Media S.A. 2021-05-19 /pmc/articles/PMC8172186/ /pubmed/34093386 http://dx.doi.org/10.3389/fneur.2021.610797 Text en Copyright © 2021 Jiang, Shi, Zhao, Zhang, Xie, Zhang, Tan and Wang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neurology
Jiang, Zhengye
Shi, Yanxi
Zhao, Wenpeng
Zhang, Yaya
Xie, Yuanyuan
Zhang, Bingchang
Tan, Guowei
Wang, Zhanxiang
Development of an Immune-Related Prognostic Index Associated With Glioblastoma
title Development of an Immune-Related Prognostic Index Associated With Glioblastoma
title_full Development of an Immune-Related Prognostic Index Associated With Glioblastoma
title_fullStr Development of an Immune-Related Prognostic Index Associated With Glioblastoma
title_full_unstemmed Development of an Immune-Related Prognostic Index Associated With Glioblastoma
title_short Development of an Immune-Related Prognostic Index Associated With Glioblastoma
title_sort development of an immune-related prognostic index associated with glioblastoma
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8172186/
https://www.ncbi.nlm.nih.gov/pubmed/34093386
http://dx.doi.org/10.3389/fneur.2021.610797
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