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Identification of an IFN-β-associated gene signature for the prediction of overall survival among glioblastoma patients

BACKGROUND: Brain glioblastoma multiforme (GBM) is the most common primary malignant intracranial tumor. The prognosis of this disease is extremely poor. While the introduction of β-interferon (IFN-β) regimen in the treatment of gliomas has significantly improved the outcome of patients; The mechani...

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Autores principales: Cheng, Lijing, Yuan, Meiling, Li, Shu, Lian, Zhiying, Chen, Junjing, Lin, Weibiao, Zhang, Jianbo, Zhong, Shupeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8263857/
https://www.ncbi.nlm.nih.gov/pubmed/34350240
http://dx.doi.org/10.21037/atm-21-1986
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author Cheng, Lijing
Yuan, Meiling
Li, Shu
Lian, Zhiying
Chen, Junjing
Lin, Weibiao
Zhang, Jianbo
Zhong, Shupeng
author_facet Cheng, Lijing
Yuan, Meiling
Li, Shu
Lian, Zhiying
Chen, Junjing
Lin, Weibiao
Zhang, Jianbo
Zhong, Shupeng
author_sort Cheng, Lijing
collection PubMed
description BACKGROUND: Brain glioblastoma multiforme (GBM) is the most common primary malignant intracranial tumor. The prognosis of this disease is extremely poor. While the introduction of β-interferon (IFN-β) regimen in the treatment of gliomas has significantly improved the outcome of patients; The mechanism by which IFN-β induces increased TMZ sensitivity has not been described. Therefore, the main objective of the study was to elucidate the molecular mechanisms responsible for the beneficial effect of IFNβ in GBM. METHODS: Messenger RNA expression profiles and clinicopathological data were downloaded from The Cancer Genome Atlas (TCGA) GBM and GSE83300 dataset from the Gene Expression Omnibus. Univariate Cox regression analysis and lasso Cox regression model established a novel 4-gene IFN-β signature (peroxiredoxin 1, Sec61 subunit beta, X-ray repair cross-complementing 5, and Bcl-2-like protein 2) for GBM prognosis prediction. Further, GBM samples (n=50) and normal brain tissues (n=50) were then used for real-time polymerase chain reaction experiments. Gene set enrichment analysis (GSEA) was performed to further understand the underlying molecular mechanisms. Pearson correlation was applied to calculate the correlation between the long non-coding RNAs (lncRNAs) and IFN-β-associated genes. An lncRNA with a correlation coefficient |R(2)|>0.3 and P<0.05 was considered to be an IFN-β-associated lncRNA. RESULTS: Patients in the high-risk group had significantly poorer survival than patients in the low-risk group. The signature was found to be an independent prognostic factor for GBM survival. Furthermore, GSEA revealed several significantly enriched pathways, which might help explain the underlying mechanisms. Our study identified a novel robust 4-gene IFN-β signature for GBM prognosis prediction. The signature might contain potential biomarkers for metabolic therapy and treatment response prediction for GBM patients. CONCLUSIONS: In the present study, we established a novel IFN-β-associated gene signature to predict the overall survival of GBM patients, which may help in clinical decision making for individual treatment.
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spelling pubmed-82638572021-08-03 Identification of an IFN-β-associated gene signature for the prediction of overall survival among glioblastoma patients Cheng, Lijing Yuan, Meiling Li, Shu Lian, Zhiying Chen, Junjing Lin, Weibiao Zhang, Jianbo Zhong, Shupeng Ann Transl Med Original Article BACKGROUND: Brain glioblastoma multiforme (GBM) is the most common primary malignant intracranial tumor. The prognosis of this disease is extremely poor. While the introduction of β-interferon (IFN-β) regimen in the treatment of gliomas has significantly improved the outcome of patients; The mechanism by which IFN-β induces increased TMZ sensitivity has not been described. Therefore, the main objective of the study was to elucidate the molecular mechanisms responsible for the beneficial effect of IFNβ in GBM. METHODS: Messenger RNA expression profiles and clinicopathological data were downloaded from The Cancer Genome Atlas (TCGA) GBM and GSE83300 dataset from the Gene Expression Omnibus. Univariate Cox regression analysis and lasso Cox regression model established a novel 4-gene IFN-β signature (peroxiredoxin 1, Sec61 subunit beta, X-ray repair cross-complementing 5, and Bcl-2-like protein 2) for GBM prognosis prediction. Further, GBM samples (n=50) and normal brain tissues (n=50) were then used for real-time polymerase chain reaction experiments. Gene set enrichment analysis (GSEA) was performed to further understand the underlying molecular mechanisms. Pearson correlation was applied to calculate the correlation between the long non-coding RNAs (lncRNAs) and IFN-β-associated genes. An lncRNA with a correlation coefficient |R(2)|>0.3 and P<0.05 was considered to be an IFN-β-associated lncRNA. RESULTS: Patients in the high-risk group had significantly poorer survival than patients in the low-risk group. The signature was found to be an independent prognostic factor for GBM survival. Furthermore, GSEA revealed several significantly enriched pathways, which might help explain the underlying mechanisms. Our study identified a novel robust 4-gene IFN-β signature for GBM prognosis prediction. The signature might contain potential biomarkers for metabolic therapy and treatment response prediction for GBM patients. CONCLUSIONS: In the present study, we established a novel IFN-β-associated gene signature to predict the overall survival of GBM patients, which may help in clinical decision making for individual treatment. AME Publishing Company 2021-06 /pmc/articles/PMC8263857/ /pubmed/34350240 http://dx.doi.org/10.21037/atm-21-1986 Text en 2021 Annals of Translational Medicine. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Cheng, Lijing
Yuan, Meiling
Li, Shu
Lian, Zhiying
Chen, Junjing
Lin, Weibiao
Zhang, Jianbo
Zhong, Shupeng
Identification of an IFN-β-associated gene signature for the prediction of overall survival among glioblastoma patients
title Identification of an IFN-β-associated gene signature for the prediction of overall survival among glioblastoma patients
title_full Identification of an IFN-β-associated gene signature for the prediction of overall survival among glioblastoma patients
title_fullStr Identification of an IFN-β-associated gene signature for the prediction of overall survival among glioblastoma patients
title_full_unstemmed Identification of an IFN-β-associated gene signature for the prediction of overall survival among glioblastoma patients
title_short Identification of an IFN-β-associated gene signature for the prediction of overall survival among glioblastoma patients
title_sort identification of an ifn-β-associated gene signature for the prediction of overall survival among glioblastoma patients
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8263857/
https://www.ncbi.nlm.nih.gov/pubmed/34350240
http://dx.doi.org/10.21037/atm-21-1986
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