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Identification of a multidimensional transcriptome signature for survival prediction of postoperative glioblastoma multiforme patients
BACKGROUND: Glioblastoma multiform (GBM) is a devastating brain tumor with maximum surgical resection, radiotherapy plus concomitant and adjuvant temozolomide (TMZ) as the standard treatment. Diverse clinicopathological and molecular features are major obstacles to accurate predict survival and eval...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302404/ https://www.ncbi.nlm.nih.gov/pubmed/30572911 http://dx.doi.org/10.1186/s12967-018-1744-8 |
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author | Gao, Wei-Zhen Guo, Lie-Mei Xu, Tian-Qi Yin, Yu-Hua Jia, Feng |
author_facet | Gao, Wei-Zhen Guo, Lie-Mei Xu, Tian-Qi Yin, Yu-Hua Jia, Feng |
author_sort | Gao, Wei-Zhen |
collection | PubMed |
description | BACKGROUND: Glioblastoma multiform (GBM) is a devastating brain tumor with maximum surgical resection, radiotherapy plus concomitant and adjuvant temozolomide (TMZ) as the standard treatment. Diverse clinicopathological and molecular features are major obstacles to accurate predict survival and evaluate the efficacy of chemotherapy or radiotherapy. Reliable prognostic biomarkers are urgently needed for postoperative GBM patients. METHODS: The protein coding genes (PCGs) and long non-coding RNA (lncRNA) gene expression profiles of 233 GBM postoperative patients were obtained from The Cancer Genome Atlas (TCGA), TANRIC and Gene Expression Omnibus (GEO) database. We randomly divided the TCGA set into a training (n = 76) and a test set (n = 77) and used GSE7696 (n = 80) as an independent validation set. Survival analysis and the random survival forest algorithm were performed to screen survival associated signature. RESULTS: Six PCGs (EIF2AK3, EPRS, GALE, GUCY2C, MTHFD2, RNF212) and five lncRNAs (CTD-2140B24.6, LINC02015, AC068888.1, CERNA1, LINC00618) were screened out by a risk score model and formed a PCG-lncRNA signature for its predictive power was strongest (AUC = 0.78 in the training dataset). The PCG-lncRNA signature could divide patients into high- risk or low-risk group with significantly different survival (median 7.47 vs. 18.27 months, log-rank test P < 0.001) in the training dataset. Similar result was observed in the test dataset (median 11.40 vs. 16.80 months, log-rank test P = 0.001) and the independent set (median 8.93 vs. 16.22 months, log-rank test P = 0.007). Multivariable Cox regression analysis verified that it was an independent prognostic factor for the postsurgical patients with GBM. Compared with IDH mutation status, O-(6)-methylguanine DNA methyltransferase promoter methylation status and age, the signature was proved to have a superior predictive power. And stratified analysis found that the signature could further separated postoperative GBM patients who received TMZ-chemoradiation into high- and low-risk groups in TCGA and GEO dataset. CONCLUSIONS: The PCG-lncRNA signature was a novel prognostic marker to predict survival and TMZ-chemoradiation response in GBM patients after surgery. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12967-018-1744-8) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6302404 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-63024042018-12-31 Identification of a multidimensional transcriptome signature for survival prediction of postoperative glioblastoma multiforme patients Gao, Wei-Zhen Guo, Lie-Mei Xu, Tian-Qi Yin, Yu-Hua Jia, Feng J Transl Med Research BACKGROUND: Glioblastoma multiform (GBM) is a devastating brain tumor with maximum surgical resection, radiotherapy plus concomitant and adjuvant temozolomide (TMZ) as the standard treatment. Diverse clinicopathological and molecular features are major obstacles to accurate predict survival and evaluate the efficacy of chemotherapy or radiotherapy. Reliable prognostic biomarkers are urgently needed for postoperative GBM patients. METHODS: The protein coding genes (PCGs) and long non-coding RNA (lncRNA) gene expression profiles of 233 GBM postoperative patients were obtained from The Cancer Genome Atlas (TCGA), TANRIC and Gene Expression Omnibus (GEO) database. We randomly divided the TCGA set into a training (n = 76) and a test set (n = 77) and used GSE7696 (n = 80) as an independent validation set. Survival analysis and the random survival forest algorithm were performed to screen survival associated signature. RESULTS: Six PCGs (EIF2AK3, EPRS, GALE, GUCY2C, MTHFD2, RNF212) and five lncRNAs (CTD-2140B24.6, LINC02015, AC068888.1, CERNA1, LINC00618) were screened out by a risk score model and formed a PCG-lncRNA signature for its predictive power was strongest (AUC = 0.78 in the training dataset). The PCG-lncRNA signature could divide patients into high- risk or low-risk group with significantly different survival (median 7.47 vs. 18.27 months, log-rank test P < 0.001) in the training dataset. Similar result was observed in the test dataset (median 11.40 vs. 16.80 months, log-rank test P = 0.001) and the independent set (median 8.93 vs. 16.22 months, log-rank test P = 0.007). Multivariable Cox regression analysis verified that it was an independent prognostic factor for the postsurgical patients with GBM. Compared with IDH mutation status, O-(6)-methylguanine DNA methyltransferase promoter methylation status and age, the signature was proved to have a superior predictive power. And stratified analysis found that the signature could further separated postoperative GBM patients who received TMZ-chemoradiation into high- and low-risk groups in TCGA and GEO dataset. CONCLUSIONS: The PCG-lncRNA signature was a novel prognostic marker to predict survival and TMZ-chemoradiation response in GBM patients after surgery. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12967-018-1744-8) contains supplementary material, which is available to authorized users. BioMed Central 2018-12-20 /pmc/articles/PMC6302404/ /pubmed/30572911 http://dx.doi.org/10.1186/s12967-018-1744-8 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Gao, Wei-Zhen Guo, Lie-Mei Xu, Tian-Qi Yin, Yu-Hua Jia, Feng Identification of a multidimensional transcriptome signature for survival prediction of postoperative glioblastoma multiforme patients |
title | Identification of a multidimensional transcriptome signature for survival prediction of postoperative glioblastoma multiforme patients |
title_full | Identification of a multidimensional transcriptome signature for survival prediction of postoperative glioblastoma multiforme patients |
title_fullStr | Identification of a multidimensional transcriptome signature for survival prediction of postoperative glioblastoma multiforme patients |
title_full_unstemmed | Identification of a multidimensional transcriptome signature for survival prediction of postoperative glioblastoma multiforme patients |
title_short | Identification of a multidimensional transcriptome signature for survival prediction of postoperative glioblastoma multiforme patients |
title_sort | identification of a multidimensional transcriptome signature for survival prediction of postoperative glioblastoma multiforme patients |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302404/ https://www.ncbi.nlm.nih.gov/pubmed/30572911 http://dx.doi.org/10.1186/s12967-018-1744-8 |
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