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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...

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Autores principales: Yang, Fan, Yang, Pei, Zhang, Chuanbao, Wang, Yongzhi, Zhang, Wei, Hu, Huimin, Wang, Zhiliang, Qiu, Xiaoguang, Jiang, Tao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Impact Journals LLC 2017
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.
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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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