Data Management And Sharing System of China National Clinical Research Center for Neurological Diseases
Identification of a Good-Prognosis IDH-Mutant-Like Population of Patients with Diffuse Gliomas
First author：Wang Z., Z.
Corresponding author：Jiang T., T.
essential_information
- Person in charge：Wang Z., Z.
- Data Sources： OTHERS
- Number of papers：OTHERS20190529
- Published time：2018-03-09
2018-03-09
Published articles
Journal：Current Molecular Medicine
Publication type: Research Article
IF(IF): 2.222
PMID: 29437008 Link To Pubmed Full text Download
Background And Purpose
Method
RNA sequencing data of WHO grade II-IV gliomas from the Chinese Glioma Genome Atlas (CGGA, N=325) were used to assess differentially expressed genes between IDH mutant and wild type gliomas and to construct a gene expression-based classifier to detect IDH mutant samples with high sensitivity and specificity. The classifier was validated in independent RNA sequencing data from the Cancer Genome Atlas (TCGA, N=699), and the prognostic value of the classifier was also assessed in the two datasets.
Conclusion
A characteristic gene expression signature is associated with and accurately predicts IDH mutation status. This suggests a common biology between these tumors and adds prognostic and biologic information that is not captured by the mutation status alone. These results may help in population stratification for clinical trials. As RNA-seq is more and more prevalent and cost-effective in glioma molecular diagnosis, this gene signature would provide a precise method to predict IDH mutation status with RNA-seq data.
Main results
A 58-gene-pair IDH mutation signature was developed by using the top scoring pairs algorithm. In CGGA dataset, 98.5% and 100% IDH mutant samples were also predicted to be mutant by gene expression based IDH status in grade II-III and grade IV gliomas, respectively. In TCGA dataset, the proportions were 99.8% and 100%, respectively. The signature remained to be a prognostic marker in multivariate cox analysis both in CGGA and TCGA datasets.