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A novel methylation signature predicts radiotherapy sensitivity in glioma
Glioblastoma (GBM) is the most common and malignant cancer of the central nervous system, and radiotherapy is widely applied in GBM treatment; however, the sensitivity to radiotherapy varies in different patients. To solve this clinical dilemma, a radiosensitivity prediction signature was constructe...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7683673/ https://www.ncbi.nlm.nih.gov/pubmed/33230136 http://dx.doi.org/10.1038/s41598-020-77259-9 |
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author | Feng, Yuemei Li, Guanzhang Shi, Zhongfang Yan, Xu Wang, Zhiliang Jiang, Haoyu Chen, Ye Li, Renpeng Zhai, You Chang, Yuanhao Zhang, Wei Yuan, Fang |
author_facet | Feng, Yuemei Li, Guanzhang Shi, Zhongfang Yan, Xu Wang, Zhiliang Jiang, Haoyu Chen, Ye Li, Renpeng Zhai, You Chang, Yuanhao Zhang, Wei Yuan, Fang |
author_sort | Feng, Yuemei |
collection | PubMed |
description | Glioblastoma (GBM) is the most common and malignant cancer of the central nervous system, and radiotherapy is widely applied in GBM treatment; however, the sensitivity to radiotherapy varies in different patients. To solve this clinical dilemma, a radiosensitivity prediction signature was constructed in the present study based on genomic methylation. In total, 1044 primary GBM samples with clinical and methylation microarray data were involved in this study. LASSO-COX, GSVA, Kaplan–Meier survival curve analysis, and COX regression were performed for the construction and verification of predictive models. The R programming language was used as the main tool for statistical analysis and graphical work. Via the integration analysis of methylation and the survival data of primary GBM, a novel prognostic and radiosensitivity prediction signature was constructed. This signature was found to be stable in prognosis prediction in the TCGA and CGGA databases. The possible mechanism was also explored, and it was found that this signature is closely related to DNA repair functions. Most importantly, this signature could predict whether GBM patients could benefit from radiotherapy. In summary, a radiosensitivity prediction signature for GBM patients based on five methylated probes was constructed, and presents great potential for clinical application. |
format | Online Article Text |
id | pubmed-7683673 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-76836732020-11-24 A novel methylation signature predicts radiotherapy sensitivity in glioma Feng, Yuemei Li, Guanzhang Shi, Zhongfang Yan, Xu Wang, Zhiliang Jiang, Haoyu Chen, Ye Li, Renpeng Zhai, You Chang, Yuanhao Zhang, Wei Yuan, Fang Sci Rep Article Glioblastoma (GBM) is the most common and malignant cancer of the central nervous system, and radiotherapy is widely applied in GBM treatment; however, the sensitivity to radiotherapy varies in different patients. To solve this clinical dilemma, a radiosensitivity prediction signature was constructed in the present study based on genomic methylation. In total, 1044 primary GBM samples with clinical and methylation microarray data were involved in this study. LASSO-COX, GSVA, Kaplan–Meier survival curve analysis, and COX regression were performed for the construction and verification of predictive models. The R programming language was used as the main tool for statistical analysis and graphical work. Via the integration analysis of methylation and the survival data of primary GBM, a novel prognostic and radiosensitivity prediction signature was constructed. This signature was found to be stable in prognosis prediction in the TCGA and CGGA databases. The possible mechanism was also explored, and it was found that this signature is closely related to DNA repair functions. Most importantly, this signature could predict whether GBM patients could benefit from radiotherapy. In summary, a radiosensitivity prediction signature for GBM patients based on five methylated probes was constructed, and presents great potential for clinical application. Nature Publishing Group UK 2020-11-23 /pmc/articles/PMC7683673/ /pubmed/33230136 http://dx.doi.org/10.1038/s41598-020-77259-9 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Feng, Yuemei Li, Guanzhang Shi, Zhongfang Yan, Xu Wang, Zhiliang Jiang, Haoyu Chen, Ye Li, Renpeng Zhai, You Chang, Yuanhao Zhang, Wei Yuan, Fang A novel methylation signature predicts radiotherapy sensitivity in glioma |
title | A novel methylation signature predicts radiotherapy sensitivity in glioma |
title_full | A novel methylation signature predicts radiotherapy sensitivity in glioma |
title_fullStr | A novel methylation signature predicts radiotherapy sensitivity in glioma |
title_full_unstemmed | A novel methylation signature predicts radiotherapy sensitivity in glioma |
title_short | A novel methylation signature predicts radiotherapy sensitivity in glioma |
title_sort | novel methylation signature predicts radiotherapy sensitivity in glioma |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7683673/ https://www.ncbi.nlm.nih.gov/pubmed/33230136 http://dx.doi.org/10.1038/s41598-020-77259-9 |
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