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Integrated Analysis to Obtain Potential Prognostic Signature in Glioblastoma
Glioblastoma multiforme (GBM) is the most malignant and multiple tumors of the central nervous system. The survival rate for GBM patients is less than 15 months. We aimed to uncover the potential mechanism of GBM in tumor microenvironment and provide several candidate biomarkers for GBM prognosis. I...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8766324/ https://www.ncbi.nlm.nih.gov/pubmed/35069135 http://dx.doi.org/10.3389/fnint.2021.717629 |
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author | Chen, Jia-Qi Zhang, Nuo Su, Zhi-Lin Qiu, Hui-Guo Zhuang, Xin-Guo Tao, Zhi-hua |
author_facet | Chen, Jia-Qi Zhang, Nuo Su, Zhi-Lin Qiu, Hui-Guo Zhuang, Xin-Guo Tao, Zhi-hua |
author_sort | Chen, Jia-Qi |
collection | PubMed |
description | Glioblastoma multiforme (GBM) is the most malignant and multiple tumors of the central nervous system. The survival rate for GBM patients is less than 15 months. We aimed to uncover the potential mechanism of GBM in tumor microenvironment and provide several candidate biomarkers for GBM prognosis. In this study, ESTIMATE analysis was used to divide the GBM patients into high and low immune or stromal score groups. Microenvironment associated genes were filtered through differential analysis. Weighted gene co-expression network analysis (WGCNA) was performed to correlate the genes and clinical traits. The candidate genes’ functions were annotated by enrichment analyses. The potential prognostic biomarkers were assessed by survival analysis. We obtained 81 immune associated differentially expressed genes (DEGs) for subsequent WGCNA analysis. Ten out of these DEGs were significantly associated with targeted molecular therapy of GBM patients. Three genes (S100A4, FCGR2B, and BIRC3) out of these genes were associated with overall survival and the independent test set testified the result. Here, we obtained three crucial genes that had good prognostic efficacy of GBM and may help to improve the prognostic prediction of GBM. |
format | Online Article Text |
id | pubmed-8766324 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-87663242022-01-20 Integrated Analysis to Obtain Potential Prognostic Signature in Glioblastoma Chen, Jia-Qi Zhang, Nuo Su, Zhi-Lin Qiu, Hui-Guo Zhuang, Xin-Guo Tao, Zhi-hua Front Integr Neurosci Neuroscience Glioblastoma multiforme (GBM) is the most malignant and multiple tumors of the central nervous system. The survival rate for GBM patients is less than 15 months. We aimed to uncover the potential mechanism of GBM in tumor microenvironment and provide several candidate biomarkers for GBM prognosis. In this study, ESTIMATE analysis was used to divide the GBM patients into high and low immune or stromal score groups. Microenvironment associated genes were filtered through differential analysis. Weighted gene co-expression network analysis (WGCNA) was performed to correlate the genes and clinical traits. The candidate genes’ functions were annotated by enrichment analyses. The potential prognostic biomarkers were assessed by survival analysis. We obtained 81 immune associated differentially expressed genes (DEGs) for subsequent WGCNA analysis. Ten out of these DEGs were significantly associated with targeted molecular therapy of GBM patients. Three genes (S100A4, FCGR2B, and BIRC3) out of these genes were associated with overall survival and the independent test set testified the result. Here, we obtained three crucial genes that had good prognostic efficacy of GBM and may help to improve the prognostic prediction of GBM. Frontiers Media S.A. 2022-01-05 /pmc/articles/PMC8766324/ /pubmed/35069135 http://dx.doi.org/10.3389/fnint.2021.717629 Text en Copyright © 2022 Chen, Zhang, Su, Qiu, Zhuang and Tao. 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 | Neuroscience Chen, Jia-Qi Zhang, Nuo Su, Zhi-Lin Qiu, Hui-Guo Zhuang, Xin-Guo Tao, Zhi-hua Integrated Analysis to Obtain Potential Prognostic Signature in Glioblastoma |
title | Integrated Analysis to Obtain Potential Prognostic Signature in Glioblastoma |
title_full | Integrated Analysis to Obtain Potential Prognostic Signature in Glioblastoma |
title_fullStr | Integrated Analysis to Obtain Potential Prognostic Signature in Glioblastoma |
title_full_unstemmed | Integrated Analysis to Obtain Potential Prognostic Signature in Glioblastoma |
title_short | Integrated Analysis to Obtain Potential Prognostic Signature in Glioblastoma |
title_sort | integrated analysis to obtain potential prognostic signature in glioblastoma |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8766324/ https://www.ncbi.nlm.nih.gov/pubmed/35069135 http://dx.doi.org/10.3389/fnint.2021.717629 |
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