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Stratification according to recursive partitioning analysis predicts outcome in newly diagnosed glioblastomas
Glioblastoma accounts for more than half of diffuse gliomas. The prognosis of patients with glioblastoma remains poor despite comprehensive and intensive treatments. Furthermore, the clinical significance of molecular parameters and routinely available clinical variables for the prognosis prediction...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Impact Journals LLC
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5522120/ https://www.ncbi.nlm.nih.gov/pubmed/28496000 http://dx.doi.org/10.18632/oncotarget.17322 |
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author | Yang, Fan Yang, Pei Zhang, Chuanbao Wang, Yongzhi Zhang, Wei Hu, Huimin Wang, Zhiliang Qiu, Xiaoguang Jiang, Tao |
author_facet | Yang, Fan Yang, Pei Zhang, Chuanbao Wang, Yongzhi Zhang, Wei Hu, Huimin Wang, Zhiliang Qiu, Xiaoguang Jiang, Tao |
author_sort | Yang, Fan |
collection | PubMed |
description | Glioblastoma accounts for more than half of diffuse gliomas. The prognosis of patients with glioblastoma remains poor despite comprehensive and intensive treatments. Furthermore, the clinical significance of molecular parameters and routinely available clinical variables for the prognosis prediction of glioblastomas remains limited. The authors describe a novel model may help in prognosis prediction and clinical management of glioblastoma patients. We performed a recursive partitioning analysis to generate three independent prognostic classes of 103 glioblastomas patients from TCGA dataset. Class I (MGMT promoter methylated, age <58), class II (MGMT promoter methylation, age ≥58; MGMT promoter unmethylation, age <54, KPS ≥70; MGMT promoter unmethylation, age >59, KPS ≥70), class III (MGMT promoter unmethylation, age 54-58, KPS ≥70; MGMT promoter unmethylation, KPS <70). Age, KPS and MGMT promoter methylation were the most significant prognostic factors for overall survival. The results were validated in CGGA dataset. This was the first study to combine various molecular parameters and clinical factors into recursive partitioning analysis to predict the prognosis of patients with glioblastomas. We included MGMT promoter methylation in our study, which could give better suggestion to patients for their chemotherapy. This clinical study will serve as the backbone for the future incorporation of molecular prognostic markers currently in development. Thus, our recursive partitioning analysis model for glioblastomas may aid in clinical prognosis evaluation. |
format | Online Article Text |
id | pubmed-5522120 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Impact Journals LLC |
record_format | MEDLINE/PubMed |
spelling | pubmed-55221202017-08-08 Stratification according to recursive partitioning analysis predicts outcome in newly diagnosed glioblastomas Yang, Fan Yang, Pei Zhang, Chuanbao Wang, Yongzhi Zhang, Wei Hu, Huimin Wang, Zhiliang Qiu, Xiaoguang Jiang, Tao Oncotarget Research Paper Glioblastoma accounts for more than half of diffuse gliomas. The prognosis of patients with glioblastoma remains poor despite comprehensive and intensive treatments. Furthermore, the clinical significance of molecular parameters and routinely available clinical variables for the prognosis prediction of glioblastomas remains limited. The authors describe a novel model may help in prognosis prediction and clinical management of glioblastoma patients. We performed a recursive partitioning analysis to generate three independent prognostic classes of 103 glioblastomas patients from TCGA dataset. Class I (MGMT promoter methylated, age <58), class II (MGMT promoter methylation, age ≥58; MGMT promoter unmethylation, age <54, KPS ≥70; MGMT promoter unmethylation, age >59, KPS ≥70), class III (MGMT promoter unmethylation, age 54-58, KPS ≥70; MGMT promoter unmethylation, KPS <70). Age, KPS and MGMT promoter methylation were the most significant prognostic factors for overall survival. The results were validated in CGGA dataset. This was the first study to combine various molecular parameters and clinical factors into recursive partitioning analysis to predict the prognosis of patients with glioblastomas. We included MGMT promoter methylation in our study, which could give better suggestion to patients for their chemotherapy. This clinical study will serve as the backbone for the future incorporation of molecular prognostic markers currently in development. Thus, our recursive partitioning analysis model for glioblastomas may aid in clinical prognosis evaluation. Impact Journals LLC 2017-04-21 /pmc/articles/PMC5522120/ /pubmed/28496000 http://dx.doi.org/10.18632/oncotarget.17322 Text en Copyright: © 2017 Yang et al. http://creativecommons.org/licenses/by/3.0/ This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) (CC-BY), which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Research Paper Yang, Fan Yang, Pei Zhang, Chuanbao Wang, Yongzhi Zhang, Wei Hu, Huimin Wang, Zhiliang Qiu, Xiaoguang Jiang, Tao Stratification according to recursive partitioning analysis predicts outcome in newly diagnosed glioblastomas |
title | Stratification according to recursive partitioning analysis predicts outcome in newly diagnosed glioblastomas |
title_full | Stratification according to recursive partitioning analysis predicts outcome in newly diagnosed glioblastomas |
title_fullStr | Stratification according to recursive partitioning analysis predicts outcome in newly diagnosed glioblastomas |
title_full_unstemmed | Stratification according to recursive partitioning analysis predicts outcome in newly diagnosed glioblastomas |
title_short | Stratification according to recursive partitioning analysis predicts outcome in newly diagnosed glioblastomas |
title_sort | stratification according to recursive partitioning analysis predicts outcome in newly diagnosed glioblastomas |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5522120/ https://www.ncbi.nlm.nih.gov/pubmed/28496000 http://dx.doi.org/10.18632/oncotarget.17322 |
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