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Gene Expression Profiling Stratifies IDH-Wildtype Glioblastoma With Distinct Prognoses

Objectives: In the present study, we aimed to determine the candidate genes that may function as biomarkers to further distinguish patients with isocitrate dehydrogenase (IDH)-wildtype glioblastoma (GBM), which are heterogeneous with respect to clinical outcomes. Materials and Methods: We selected 4...

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Autores principales: Liu, Yu-Qing, Wu, Fan, Li, Jing-Jun, Li, Yang-Fang, Liu, Xing, Wang, Zheng, Chai, Rui-Chao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6929203/
https://www.ncbi.nlm.nih.gov/pubmed/31921684
http://dx.doi.org/10.3389/fonc.2019.01433
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author Liu, Yu-Qing
Wu, Fan
Li, Jing-Jun
Li, Yang-Fang
Liu, Xing
Wang, Zheng
Chai, Rui-Chao
author_facet Liu, Yu-Qing
Wu, Fan
Li, Jing-Jun
Li, Yang-Fang
Liu, Xing
Wang, Zheng
Chai, Rui-Chao
author_sort Liu, Yu-Qing
collection PubMed
description Objectives: In the present study, we aimed to determine the candidate genes that may function as biomarkers to further distinguish patients with isocitrate dehydrogenase (IDH)-wildtype glioblastoma (GBM), which are heterogeneous with respect to clinical outcomes. Materials and Methods: We selected 41 candidate genes associated with overall survival (OS) using univariate Cox regression from IDH-wildtype GBM patients based on RNA sequencing (RNAseq) expression data from the Chinese Glioma Genome Atlas (CGGA, n = 105) and The Cancer Genome Atlas (TCGA, n = 139) cohorts. Next, a seven-gene-based risk signature was formulated according to Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm in the CGGA RNAseq database as a training set, while another 525 IDH-wildtype GBM patient TCGA datasets, consisting of RNA sequencing and microarray data, were used for validation. Patient survival in the low- and high-risk groups was calculated using Kaplan-Meier survival curve analysis and the log-rank test. Uni-and multivariate Cox regression analysis was used to assess the prognosis value. Gene oncology (GO) and gene set enrichment analysis (GSEA) were performed for the functional analysis of the seven-gene-based risk signature. Results: We developed a seven-gene-based signature, which allocated each patient to a risk group (low or high). Patients in the high-risk group had dramatically shorter overall survival than their low-risk counterparts in three independent cohorts. Univariate and multivariate analysis showed that the seven-gene signature remained an independent prognostic factor. Moreover, the seven-gene risk signature exhibited a striking prognostic validity, with AUC of 78.4 and 73.9%, which was higher than for traditional “age” (53.7%, 62.4%) and “GBM sub-type” (57.7%, 52.9%) in the CGGA- and TCGA-RNAseq databases, respectively. Subsequent bioinformatics analysis predicted that the seven-gene signature was involved in the inflammatory response, immune response, cell adhesion, and apoptotic process. Conclusions: Our findings indicate that the seven-gene signature could be a potential prognostic biomarker. This study refined the current classification system of IDH-wildtype GBM and may provide a novel perspective for the research and individual therapy of IDH-wildtype GBM.
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spelling pubmed-69292032020-01-09 Gene Expression Profiling Stratifies IDH-Wildtype Glioblastoma With Distinct Prognoses Liu, Yu-Qing Wu, Fan Li, Jing-Jun Li, Yang-Fang Liu, Xing Wang, Zheng Chai, Rui-Chao Front Oncol Oncology Objectives: In the present study, we aimed to determine the candidate genes that may function as biomarkers to further distinguish patients with isocitrate dehydrogenase (IDH)-wildtype glioblastoma (GBM), which are heterogeneous with respect to clinical outcomes. Materials and Methods: We selected 41 candidate genes associated with overall survival (OS) using univariate Cox regression from IDH-wildtype GBM patients based on RNA sequencing (RNAseq) expression data from the Chinese Glioma Genome Atlas (CGGA, n = 105) and The Cancer Genome Atlas (TCGA, n = 139) cohorts. Next, a seven-gene-based risk signature was formulated according to Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm in the CGGA RNAseq database as a training set, while another 525 IDH-wildtype GBM patient TCGA datasets, consisting of RNA sequencing and microarray data, were used for validation. Patient survival in the low- and high-risk groups was calculated using Kaplan-Meier survival curve analysis and the log-rank test. Uni-and multivariate Cox regression analysis was used to assess the prognosis value. Gene oncology (GO) and gene set enrichment analysis (GSEA) were performed for the functional analysis of the seven-gene-based risk signature. Results: We developed a seven-gene-based signature, which allocated each patient to a risk group (low or high). Patients in the high-risk group had dramatically shorter overall survival than their low-risk counterparts in three independent cohorts. Univariate and multivariate analysis showed that the seven-gene signature remained an independent prognostic factor. Moreover, the seven-gene risk signature exhibited a striking prognostic validity, with AUC of 78.4 and 73.9%, which was higher than for traditional “age” (53.7%, 62.4%) and “GBM sub-type” (57.7%, 52.9%) in the CGGA- and TCGA-RNAseq databases, respectively. Subsequent bioinformatics analysis predicted that the seven-gene signature was involved in the inflammatory response, immune response, cell adhesion, and apoptotic process. Conclusions: Our findings indicate that the seven-gene signature could be a potential prognostic biomarker. This study refined the current classification system of IDH-wildtype GBM and may provide a novel perspective for the research and individual therapy of IDH-wildtype GBM. Frontiers Media S.A. 2019-12-17 /pmc/articles/PMC6929203/ /pubmed/31921684 http://dx.doi.org/10.3389/fonc.2019.01433 Text en Copyright © 2019 Liu, Wu, Li, Li, Liu, Wang and Chai. http://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 Oncology
Liu, Yu-Qing
Wu, Fan
Li, Jing-Jun
Li, Yang-Fang
Liu, Xing
Wang, Zheng
Chai, Rui-Chao
Gene Expression Profiling Stratifies IDH-Wildtype Glioblastoma With Distinct Prognoses
title Gene Expression Profiling Stratifies IDH-Wildtype Glioblastoma With Distinct Prognoses
title_full Gene Expression Profiling Stratifies IDH-Wildtype Glioblastoma With Distinct Prognoses
title_fullStr Gene Expression Profiling Stratifies IDH-Wildtype Glioblastoma With Distinct Prognoses
title_full_unstemmed Gene Expression Profiling Stratifies IDH-Wildtype Glioblastoma With Distinct Prognoses
title_short Gene Expression Profiling Stratifies IDH-Wildtype Glioblastoma With Distinct Prognoses
title_sort gene expression profiling stratifies idh-wildtype glioblastoma with distinct prognoses
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6929203/
https://www.ncbi.nlm.nih.gov/pubmed/31921684
http://dx.doi.org/10.3389/fonc.2019.01433
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