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A risk signature with four autophagy‐related genes for predicting survival of glioblastoma multiforme

Glioblastoma multiforme (GBM) is a devastating brain tumour without effective treatment. Recent studies have shown that autophagy is a promising therapeutic strategy for GBM. Therefore, it is necessary to identify novel biomarkers associated with autophagy in GBM. In this study, we downloaded autoph...

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Autores principales: Wang, Yulin, Zhao, Weijiang, Xiao, Zhe, Guan, Gefei, Liu, Xin, Zhuang, Minghua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7171404/
https://www.ncbi.nlm.nih.gov/pubmed/32065482
http://dx.doi.org/10.1111/jcmm.14938
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author Wang, Yulin
Zhao, Weijiang
Xiao, Zhe
Guan, Gefei
Liu, Xin
Zhuang, Minghua
author_facet Wang, Yulin
Zhao, Weijiang
Xiao, Zhe
Guan, Gefei
Liu, Xin
Zhuang, Minghua
author_sort Wang, Yulin
collection PubMed
description Glioblastoma multiforme (GBM) is a devastating brain tumour without effective treatment. Recent studies have shown that autophagy is a promising therapeutic strategy for GBM. Therefore, it is necessary to identify novel biomarkers associated with autophagy in GBM. In this study, we downloaded autophagy‐related genes from Human Autophagy Database (HADb) and Gene Set Enrichment Analysis (GSEA) website. Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis were performed to identify genes for constructing a risk signature. A nomogram was developed by integrating the risk signature with clinicopathological factors. Time‐dependent receiver operating characteristic (ROC) curve and calibration plot were used to evaluate the efficiency of the prognostic model. Finally, four autophagy‐related genes (DIRAS3, LGALS8, MAPK8 and STAM) were identified and were used for constructing a risk signature, which proved to be an independent risk factor for GBM patients. Furthermore, a nomogram was developed based on the risk signature and clinicopathological factors (IDH1 status, age and history of radiotherapy or chemotherapy). ROC curve and calibration plot suggested the nomogram could accurately predict 1‐, 3‐ and 5‐year survival rate of GBM patients. For function analysis, the risk signature was associated with apoptosis, necrosis, immunity, inflammation response and MAPK signalling pathway. In conclusion, the risk signature with 4 autophagy‐related genes could serve as an independent prognostic factor for GBM patients. Moreover, we developed a nomogram based on the risk signature and clinical traits which was validated to perform better for predicting 1‐, 3‐ and 5‐year survival rate of GBM.
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spelling pubmed-71714042020-04-21 A risk signature with four autophagy‐related genes for predicting survival of glioblastoma multiforme Wang, Yulin Zhao, Weijiang Xiao, Zhe Guan, Gefei Liu, Xin Zhuang, Minghua J Cell Mol Med Original Articles Glioblastoma multiforme (GBM) is a devastating brain tumour without effective treatment. Recent studies have shown that autophagy is a promising therapeutic strategy for GBM. Therefore, it is necessary to identify novel biomarkers associated with autophagy in GBM. In this study, we downloaded autophagy‐related genes from Human Autophagy Database (HADb) and Gene Set Enrichment Analysis (GSEA) website. Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis were performed to identify genes for constructing a risk signature. A nomogram was developed by integrating the risk signature with clinicopathological factors. Time‐dependent receiver operating characteristic (ROC) curve and calibration plot were used to evaluate the efficiency of the prognostic model. Finally, four autophagy‐related genes (DIRAS3, LGALS8, MAPK8 and STAM) were identified and were used for constructing a risk signature, which proved to be an independent risk factor for GBM patients. Furthermore, a nomogram was developed based on the risk signature and clinicopathological factors (IDH1 status, age and history of radiotherapy or chemotherapy). ROC curve and calibration plot suggested the nomogram could accurately predict 1‐, 3‐ and 5‐year survival rate of GBM patients. For function analysis, the risk signature was associated with apoptosis, necrosis, immunity, inflammation response and MAPK signalling pathway. In conclusion, the risk signature with 4 autophagy‐related genes could serve as an independent prognostic factor for GBM patients. Moreover, we developed a nomogram based on the risk signature and clinical traits which was validated to perform better for predicting 1‐, 3‐ and 5‐year survival rate of GBM. John Wiley and Sons Inc. 2020-02-17 2020-04 /pmc/articles/PMC7171404/ /pubmed/32065482 http://dx.doi.org/10.1111/jcmm.14938 Text en © 2020 The Authors. Journal of Cellular and Molecular Medicine published by John Wiley & Sons Ltd and Foundation for Cellular and Molecular Medicine. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Articles
Wang, Yulin
Zhao, Weijiang
Xiao, Zhe
Guan, Gefei
Liu, Xin
Zhuang, Minghua
A risk signature with four autophagy‐related genes for predicting survival of glioblastoma multiforme
title A risk signature with four autophagy‐related genes for predicting survival of glioblastoma multiforme
title_full A risk signature with four autophagy‐related genes for predicting survival of glioblastoma multiforme
title_fullStr A risk signature with four autophagy‐related genes for predicting survival of glioblastoma multiforme
title_full_unstemmed A risk signature with four autophagy‐related genes for predicting survival of glioblastoma multiforme
title_short A risk signature with four autophagy‐related genes for predicting survival of glioblastoma multiforme
title_sort risk signature with four autophagy‐related genes for predicting survival of glioblastoma multiforme
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7171404/
https://www.ncbi.nlm.nih.gov/pubmed/32065482
http://dx.doi.org/10.1111/jcmm.14938
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