Gene expression signature-based prognostic risk score in patients with glioblastoma

The present study aimed to identify genes associated with patient survival to improve our understanding of the underlying biology of gliomas. We investigated whether the expression of genes selected using random survival forests models could be used to define glioma subgroups more objectively than standard pathology. The RNA from 32 non-treated grade 4 gliomas were analyzed using the GeneChip Human Genome U133 Plus 2.0 Expression array (which contains approximately 47 000 genes). Twenty-five genes whose expressions were strongly and consistently related to patient survival were identified. The prognosis prediction score of these genes was most significant among several variables and survival analyses. The prognosis prediction score of three genes and age classifiers also revealed a strong prognostic value among grade 4 gliomas. These results were validated in an independent samples set (n = 488). Our method was effective for objectively classifying grade 4 gliomas and was a more accurate prognosis predictor than histological grading.