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Reactive oxygen species metabolism-based prediction model and drug for patients with recurrent glioblastoma
Background: Tumor recurrence is the main cause of poor prognosis of GBM. Finding the characteristics of recurrent GBM that provide early warning of tumor recurrence can provide guidance for the clinical treatment of recurrent GBM. Results: Reactive oxygen species (ROS) biosynthetic processes was sig...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Impact Journals
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6932921/ https://www.ncbi.nlm.nih.gov/pubmed/31801111 http://dx.doi.org/10.18632/aging.102506 |
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author | Tan, Nian Liu, Jianwei Li, Ping Sun, Zhaoying Pan, Jianming Zhao, Wei |
author_facet | Tan, Nian Liu, Jianwei Li, Ping Sun, Zhaoying Pan, Jianming Zhao, Wei |
author_sort | Tan, Nian |
collection | PubMed |
description | Background: Tumor recurrence is the main cause of poor prognosis of GBM. Finding the characteristics of recurrent GBM that provide early warning of tumor recurrence can provide guidance for the clinical treatment of recurrent GBM. Results: Reactive oxygen species (ROS) biosynthetic processes was significantly elevated in recurrent GBM. The recurrent risk score based on the ROS biosynthetic process was closely related to tumor purity and tumor immune functions. The quantitative risk assessment system could be used to predict the recurrence time of GBM. Gallic acid, a compound with high anti-oxidation activity and low cytotoxicity, was screened as a potential chemotherapy sensitizer for recurrent GBM. Conclusion: The quantitative risk assessment system based on ROS biosynthetic process could be used for early warning of GBM recurrence. Combination of low-dose gallic acid and temozolomide could improve therapeutic outcomes in recurrent GBM. Methods: A total of 663 primary and recurrent GBM samples with clinical and microarray data were included in this study. GSVA, LASSO-COX, and Kaplan-Meier survive curve were performed to construct and verify a quantitative risk assessment system for GBM recurrence prediction. An antioxidant capacity test and cell viability test were used to discover potential drugs for recurrent GBM. |
format | Online Article Text |
id | pubmed-6932921 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Impact Journals |
record_format | MEDLINE/PubMed |
spelling | pubmed-69329212020-01-03 Reactive oxygen species metabolism-based prediction model and drug for patients with recurrent glioblastoma Tan, Nian Liu, Jianwei Li, Ping Sun, Zhaoying Pan, Jianming Zhao, Wei Aging (Albany NY) Research Paper Background: Tumor recurrence is the main cause of poor prognosis of GBM. Finding the characteristics of recurrent GBM that provide early warning of tumor recurrence can provide guidance for the clinical treatment of recurrent GBM. Results: Reactive oxygen species (ROS) biosynthetic processes was significantly elevated in recurrent GBM. The recurrent risk score based on the ROS biosynthetic process was closely related to tumor purity and tumor immune functions. The quantitative risk assessment system could be used to predict the recurrence time of GBM. Gallic acid, a compound with high anti-oxidation activity and low cytotoxicity, was screened as a potential chemotherapy sensitizer for recurrent GBM. Conclusion: The quantitative risk assessment system based on ROS biosynthetic process could be used for early warning of GBM recurrence. Combination of low-dose gallic acid and temozolomide could improve therapeutic outcomes in recurrent GBM. Methods: A total of 663 primary and recurrent GBM samples with clinical and microarray data were included in this study. GSVA, LASSO-COX, and Kaplan-Meier survive curve were performed to construct and verify a quantitative risk assessment system for GBM recurrence prediction. An antioxidant capacity test and cell viability test were used to discover potential drugs for recurrent GBM. Impact Journals 2019-12-04 /pmc/articles/PMC6932921/ /pubmed/31801111 http://dx.doi.org/10.18632/aging.102506 Text en Copyright © 2019 Tan et al. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Paper Tan, Nian Liu, Jianwei Li, Ping Sun, Zhaoying Pan, Jianming Zhao, Wei Reactive oxygen species metabolism-based prediction model and drug for patients with recurrent glioblastoma |
title | Reactive oxygen species metabolism-based prediction model and drug for patients with recurrent glioblastoma |
title_full | Reactive oxygen species metabolism-based prediction model and drug for patients with recurrent glioblastoma |
title_fullStr | Reactive oxygen species metabolism-based prediction model and drug for patients with recurrent glioblastoma |
title_full_unstemmed | Reactive oxygen species metabolism-based prediction model and drug for patients with recurrent glioblastoma |
title_short | Reactive oxygen species metabolism-based prediction model and drug for patients with recurrent glioblastoma |
title_sort | reactive oxygen species metabolism-based prediction model and drug for patients with recurrent glioblastoma |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6932921/ https://www.ncbi.nlm.nih.gov/pubmed/31801111 http://dx.doi.org/10.18632/aging.102506 |
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