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Identification of a long noncoding RNA signature to predict outcomes of glioblastoma

Long noncoding RNAs (lncRNAs) are a novel class of gene regulators involved in tumor biogenesis. Glioblastoma is the most common and malignant type of brain tumor. The function and prognostic significance of lncRNAs in glioblastoma remain unclear. In the present study, updated gene annotations were...

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Autores principales: Li, Depei, Lu, Jie, Li, Hong, Qi, Songtao, Yu, Lei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: D.A. Spandidos 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6522932/
https://www.ncbi.nlm.nih.gov/pubmed/31059035
http://dx.doi.org/10.3892/mmr.2019.10184
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author Li, Depei
Lu, Jie
Li, Hong
Qi, Songtao
Yu, Lei
author_facet Li, Depei
Lu, Jie
Li, Hong
Qi, Songtao
Yu, Lei
author_sort Li, Depei
collection PubMed
description Long noncoding RNAs (lncRNAs) are a novel class of gene regulators involved in tumor biogenesis. Glioblastoma is the most common and malignant type of brain tumor. The function and prognostic significance of lncRNAs in glioblastoma remain unclear. In the present study, updated gene annotations were adopted to investigate lncRNA expression profiles in publicly available glioma microarray datasets from the Gene Expression Omnibus and the Repository for Molecular Brain Neoplasia Data. In a training set of 108 samples of glioblastoma, using univariate Cox regression analysis with a permutation P<0.005, four lncRNAs, including insulin-like growth factor binding protein 7-antisense 1 (IGFBP7-AS1), were significantly associated with patient overall survival. These four lncRNAs were integrated as an expression-based molecular signature to divide patients in the training set into high-risk and low-risk subgroups, with distinct survival rates (hazard ratio, 2.72; 95% CI, 1.71–4.31; P<0.001). The prognostic value of the lncRNA signature was confirmed in two additional datasets comprising a total of 147 samples from patients with glioblastoma. The prognostic value of this signature was independent of age and Karnofsky performance status. This signature was also able to predict different outcomes in cases of glioblastoma associated with an isocitrate dehydrogenase 1 mutation. Further bioinformatics analyses revealed that ‘epithelial-mesenchymal transition’ and ‘p53 pathway’ gene sets were enriched in glioblastoma samples with higher IGFBP7-AS1 expression. Furthermore, in vitro experiments demonstrated that knockdown of IGFBP7-AS1 inhibited the viability, migration and invasion of U87 and U251 glioma cells. In conclusion, the present study identified a lncRNA signature able to predict glioblastoma outcomes, and provided novel information regarding the role of IGFBP7-AS1 in glioma development.
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spelling pubmed-65229322019-06-18 Identification of a long noncoding RNA signature to predict outcomes of glioblastoma Li, Depei Lu, Jie Li, Hong Qi, Songtao Yu, Lei Mol Med Rep Articles Long noncoding RNAs (lncRNAs) are a novel class of gene regulators involved in tumor biogenesis. Glioblastoma is the most common and malignant type of brain tumor. The function and prognostic significance of lncRNAs in glioblastoma remain unclear. In the present study, updated gene annotations were adopted to investigate lncRNA expression profiles in publicly available glioma microarray datasets from the Gene Expression Omnibus and the Repository for Molecular Brain Neoplasia Data. In a training set of 108 samples of glioblastoma, using univariate Cox regression analysis with a permutation P<0.005, four lncRNAs, including insulin-like growth factor binding protein 7-antisense 1 (IGFBP7-AS1), were significantly associated with patient overall survival. These four lncRNAs were integrated as an expression-based molecular signature to divide patients in the training set into high-risk and low-risk subgroups, with distinct survival rates (hazard ratio, 2.72; 95% CI, 1.71–4.31; P<0.001). The prognostic value of the lncRNA signature was confirmed in two additional datasets comprising a total of 147 samples from patients with glioblastoma. The prognostic value of this signature was independent of age and Karnofsky performance status. This signature was also able to predict different outcomes in cases of glioblastoma associated with an isocitrate dehydrogenase 1 mutation. Further bioinformatics analyses revealed that ‘epithelial-mesenchymal transition’ and ‘p53 pathway’ gene sets were enriched in glioblastoma samples with higher IGFBP7-AS1 expression. Furthermore, in vitro experiments demonstrated that knockdown of IGFBP7-AS1 inhibited the viability, migration and invasion of U87 and U251 glioma cells. In conclusion, the present study identified a lncRNA signature able to predict glioblastoma outcomes, and provided novel information regarding the role of IGFBP7-AS1 in glioma development. D.A. Spandidos 2019-06 2019-04-24 /pmc/articles/PMC6522932/ /pubmed/31059035 http://dx.doi.org/10.3892/mmr.2019.10184 Text en Copyright: © Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
spellingShingle Articles
Li, Depei
Lu, Jie
Li, Hong
Qi, Songtao
Yu, Lei
Identification of a long noncoding RNA signature to predict outcomes of glioblastoma
title Identification of a long noncoding RNA signature to predict outcomes of glioblastoma
title_full Identification of a long noncoding RNA signature to predict outcomes of glioblastoma
title_fullStr Identification of a long noncoding RNA signature to predict outcomes of glioblastoma
title_full_unstemmed Identification of a long noncoding RNA signature to predict outcomes of glioblastoma
title_short Identification of a long noncoding RNA signature to predict outcomes of glioblastoma
title_sort identification of a long noncoding rna signature to predict outcomes of glioblastoma
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6522932/
https://www.ncbi.nlm.nih.gov/pubmed/31059035
http://dx.doi.org/10.3892/mmr.2019.10184
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