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Comprehensive development and validation of gene signature for predicting survival in patients with glioblastoma
Glioblastoma (GBM) is the most common brain tumor, with rapid proliferation and fatal invasiveness. Large-scale genetic and epigenetic profiling studies have identified targets among molecular subgroups, yet agents developed against these targets have failed in late clinical development. We obtained...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9399759/ https://www.ncbi.nlm.nih.gov/pubmed/36035145 http://dx.doi.org/10.3389/fgene.2022.900911 |
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author | Jin, Yi Wang, Zhanwang Xiang, Kaimin Zhu, Yuxing Cheng, Yaxin Cao, Ke Jiang, Jiaode |
author_facet | Jin, Yi Wang, Zhanwang Xiang, Kaimin Zhu, Yuxing Cheng, Yaxin Cao, Ke Jiang, Jiaode |
author_sort | Jin, Yi |
collection | PubMed |
description | Glioblastoma (GBM) is the most common brain tumor, with rapid proliferation and fatal invasiveness. Large-scale genetic and epigenetic profiling studies have identified targets among molecular subgroups, yet agents developed against these targets have failed in late clinical development. We obtained the genomic and clinical data of GBM patients from the Chinese Glioma Genome Atlas (CGGA) and performed the least absolute shrinkage and selection operator (LASSO) Cox analysis to establish a risk model incorporating 17 genes in the CGGA693 RNA-seq cohort. This risk model was successfully validated using the CGGA325 validation set. Based on Cox regression analysis, this risk model may be an independent indicator of clinical efficacy. We also developed a survival nomogram prediction model that combines the clinical features of OS. To determine the novel classification based on the risk model, we classified the patients into two clusters using ConsensusClusterPlus, and evaluated the tumor immune environment with ESTIMATE and CIBERSORT. We also constructed clinical traits-related and co-expression modules through WGCNA analysis. We identified eight genes (ANKRD20A4, CLOCK, CNTRL, ICA1, LARP4B, RASA2, RPS6, and SET) in the blue module and three genes (MSH2, ZBTB34, and DDX31) in the turquoise module. Based on the public website TCGA, two biomarkers were significantly associated with poorer OS. Finally, through GSCALite, we re-evaluated the prognostic value of the essential biomarkers and verified MSH2 as a hub biomarker. |
format | Online Article Text |
id | pubmed-9399759 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93997592022-08-25 Comprehensive development and validation of gene signature for predicting survival in patients with glioblastoma Jin, Yi Wang, Zhanwang Xiang, Kaimin Zhu, Yuxing Cheng, Yaxin Cao, Ke Jiang, Jiaode Front Genet Genetics Glioblastoma (GBM) is the most common brain tumor, with rapid proliferation and fatal invasiveness. Large-scale genetic and epigenetic profiling studies have identified targets among molecular subgroups, yet agents developed against these targets have failed in late clinical development. We obtained the genomic and clinical data of GBM patients from the Chinese Glioma Genome Atlas (CGGA) and performed the least absolute shrinkage and selection operator (LASSO) Cox analysis to establish a risk model incorporating 17 genes in the CGGA693 RNA-seq cohort. This risk model was successfully validated using the CGGA325 validation set. Based on Cox regression analysis, this risk model may be an independent indicator of clinical efficacy. We also developed a survival nomogram prediction model that combines the clinical features of OS. To determine the novel classification based on the risk model, we classified the patients into two clusters using ConsensusClusterPlus, and evaluated the tumor immune environment with ESTIMATE and CIBERSORT. We also constructed clinical traits-related and co-expression modules through WGCNA analysis. We identified eight genes (ANKRD20A4, CLOCK, CNTRL, ICA1, LARP4B, RASA2, RPS6, and SET) in the blue module and three genes (MSH2, ZBTB34, and DDX31) in the turquoise module. Based on the public website TCGA, two biomarkers were significantly associated with poorer OS. Finally, through GSCALite, we re-evaluated the prognostic value of the essential biomarkers and verified MSH2 as a hub biomarker. Frontiers Media S.A. 2022-08-10 /pmc/articles/PMC9399759/ /pubmed/36035145 http://dx.doi.org/10.3389/fgene.2022.900911 Text en Copyright © 2022 Jin, Wang, Xiang, Zhu, Cheng, Cao and Jiang. 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 | Genetics Jin, Yi Wang, Zhanwang Xiang, Kaimin Zhu, Yuxing Cheng, Yaxin Cao, Ke Jiang, Jiaode Comprehensive development and validation of gene signature for predicting survival in patients with glioblastoma |
title | Comprehensive development and validation of gene signature for predicting survival in patients with glioblastoma |
title_full | Comprehensive development and validation of gene signature for predicting survival in patients with glioblastoma |
title_fullStr | Comprehensive development and validation of gene signature for predicting survival in patients with glioblastoma |
title_full_unstemmed | Comprehensive development and validation of gene signature for predicting survival in patients with glioblastoma |
title_short | Comprehensive development and validation of gene signature for predicting survival in patients with glioblastoma |
title_sort | comprehensive development and validation of gene signature for predicting survival in patients with glioblastoma |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9399759/ https://www.ncbi.nlm.nih.gov/pubmed/36035145 http://dx.doi.org/10.3389/fgene.2022.900911 |
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