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Identification of IDH-mutant gliomas by a prognostic signature according to gene expression profiling.


ABSTRACT: BACKGROUND:Isocitrate dehydrogenase (IDH) mutations are the most common genetic aberrations in gliomagenesis. We aimed to build a high-efficiency prediction gene signature in patients with IDH-mutant glioma. METHODS:In total, 167 gliomas from Chinese Glioma Genome Atlas (CGGA) dataset were included for discovery. The Cancer Genome Atlas (TCGA) dataset was used for validation. R language was the main software environment for our statistical operation and graphics. RESULTS:We applied the Time-Dependent ROC Curve (timeROC) method to estimate the gene prediction accuracy of 3 years and 5 years in two datasets. Seven genes were selected for further analysis (AUC ? 0.7 in two datasets). A seven-gene enrichment score was established to predict the overall survival of 3 years and 5 years for IDH- mutant glioma patients. Moreover, the seven-gene signature was an independent prognostic indicator for patients with IDH-mutant glioma. Gene Ontology (GO) Analysis of associated genes revealed signature-related biological process of cell cycle and division. CONCLUSION:We have identified a seven-gene signature that can provide a more accurate predictor of 3 years and 5 years for patients with IDH-mutant glioma. Moreover, the signature may potentially help neurosurgeons with the clinical personalized management of gliomas.

SUBMITTER: Wang Q 

PROVIDER: S-EPMC6128431 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Identification of IDH-mutant gliomas by a prognostic signature according to gene expression profiling.

Wang Qiangwei Q   Wang Zhiliang Z   Li Guanzhang G   Zhang Chuanbao C   Bao Zhaoshi Z   Wang Zheng Z   You Gan G   Jiang Tao T  

Aging 20180801 8


<h4>Background</h4>Isocitrate dehydrogenase (IDH) mutations are the most common genetic aberrations in gliomagenesis. We aimed to build a high-efficiency prediction gene signature in patients with IDH-mutant glioma.<h4>Methods</h4>In total, 167 gliomas from Chinese Glioma Genome Atlas (CGGA) dataset were included for discovery. The Cancer Genome Atlas (TCGA) dataset was used for validation. R language was the main software environment for our statistical operation and graphics.<h4>Results</h4>We  ...[more]

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