Stratification according to recursive partitioning analysis predicts outcome in newly diagnosed glioblastomas
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Fan Yang1,*, Pei Yang1,*, Chuanbao Zhang1,*, Yongzhi Wang2, Wei Zhang2, Huimin Hu1, Zhiliang Wang1, Xiaoguang Qiu3 and Tao Jiang1,2,4,5
1Department of Molecular Pathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
2Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
3Department of Radiation Therapy, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
4China National Clinical Research Center for Neurological Diseases, Beijing, China
5Center of Brain Tumor, Beijing Institute for Brain Disorders, Beijing, China
*These authors have contributed equally to this work
Tao Jiang, email: firstname.lastname@example.org
Keywords: glioblastoma, prognosis, recursive partitioning analysis, molecular marker, MGMT
Received: July 18, 2016 Accepted: December 16, 2016 Published: April 21, 2017
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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