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Identification of high risk anaplastic gliomas by a diagnostic and prognostic signature derived from mRNA expression profiling.


ABSTRACT: Anaplastic gliomas are characterized by variable clinical and genetic features, but there are few studies focusing on the substratification of anaplastic gliomas. To identify a more objective and applicable classification of anaplastic gliomas, we analyzed whole genome mRNA expression profiling of four independent datasets. Univariate Cox regression, linear risk score formula and receiver operating characteristic (ROC) curve were applied to derive a gene signature with best prognostic performance. The corresponding clinical and molecular information were further analyzed for interpretation of the different prognosis and the independence of the signature. Gene ontology (GO), Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA) were performed for functional annotation of the differences. We found a three-gene signature, by applying which, the anaplastic gliomas could be divided into low risk and high risk groups. The two groups showed a high concordance with grade II and grade IV gliomas, respectively. The high risk group was more aggressive and complex. The three-gene signature showed diagnostic and prognostic value in anaplastic gliomas.

SUBMITTER: Zhang CB 

PROVIDER: S-EPMC4742201 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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Identification of high risk anaplastic gliomas by a diagnostic and prognostic signature derived from mRNA expression profiling.

Zhang Chuan-Bao CB   Zhu Ping P   Yang Pei P   Cai Jin-Quan JQ   Wang Zhi-Liang ZL   Li Qing-Bin QB   Bao Zhao-Shi ZS   Zhang Wei W   Jiang Tao T  

Oncotarget 20151101 34


Anaplastic gliomas are characterized by variable clinical and genetic features, but there are few studies focusing on the substratification of anaplastic gliomas. To identify a more objective and applicable classification of anaplastic gliomas, we analyzed whole genome mRNA expression profiling of four independent datasets. Univariate Cox regression, linear risk score formula and receiver operating characteristic (ROC) curve were applied to derive a gene signature with best prognostic performanc  ...[more]

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