1. ¹Department of Neurosurgery, Brain Research Institute, Niigata University, Niigata City, Japan
2. ²Pharmacology Division, National Cancer Center Research Institute, Chuo-ku, Tokyo, Japan
3. ³Mitsubishi Chemical Safety Institute, Ibaraki, Japan

Correspondence: Dr R Yamanaka, Department of Neurosurgery, Brain Research Institute, Niigata University, Asahimachi-dori 1-757, Niigata City 951-8585, Japan. E-mail: ryaman@bri.niigata-u.ac.jp

Received 7 November 2005; Revised 8 March 2006; Accepted 8 March 2006; Published online 1 May 2006.

Abstract

Better understanding of the underlying biology of malignant gliomas is critical for the development of early detection strategies and new therapeutics. This study aimed to define genes associated with survival. We investigated whether genes coupled with a class prediction model could be used to define subgroups of high-grade gliomas in a more objective manner than standard pathology. RNAs from 29 malignant gliomas were analysed using Agilent microarrays. We identified 21 genes whose expression was most strongly and consistently related to patient survival based on univariate proportional hazards models. In six out of 10 genes, changes in gene expression were validated by quantitative real-time PCR. After adjusting for clinical covariates based on a multivariate analysis, we finally obtained a statistical significance level for DDR1 (discoidin domain receptor family, member 1), DYRK3 (dual-specificity tyrosine-(Y)-phosphorylation-regulated kinase 3) and KSP37 (Ksp37 protein). In independent samples, it was confirmed that DDR1 protein expression was also correlated to the prognosis of glioma patients detected by immunohistochemical staining. Furthermore, we analysed the efficacy of the short interfering RNA (siRNA)-mediated inhibition of DDR1 mRNA synthesis in glioma cell lines. Cell proliferation and invasion were significantly suppressed by siRNA against DDR1. Thus, DDR1 can be a novel molecular target of therapy as well as an important predictive marker for survival in patients with glioma. Our method was effective at classifying high-grade gliomas objectively, and provided a more accurate predictor of prognosis than histological grading.