Large-Scale Analysis Reveals Gene Signature for Survival Prediction in Primary Glioblastoma

Glioblastoma multiforme (GBM) is the most aggressive and common primary central nervous system tumour. Despite extensive therapy, GBM patients usually have poor prognosis with a median survival of 12-15 months. Novel molecular biomarkers that can improve survival prediction and help with treatment strategies are still urgently required. Here we aimed to robustly identify a gene signature panel for improved survival prediction in primary GBM patients. We identified 2166 differentially expressed genes (DEGs) using meta-analysis of microarray datasets comprising of 955 samples (biggest primary GBM cohort for such studies as per our knowledge) and 3368 DEGs from RNA-seq dataset with 165 samples. Based on the 1443 common DEGs, using univariate Cox and least absolute shrinkage and selection operator (LASSO) with multivariate Cox regression, we identified a survival associated 4-gene signature panel including IGFBP2, PTPRN, STEAP2 and SLC39A10 and thereafter established a risk score model that performed well in survival prediction. High-risk group patients had significantly poorer survival as compared with those in the low-risk group (AUC = 0.766 for 1-year prediction). Multivariate analysis demonstrated that predictive value of the 4-gene signature panel was independent of other clinical and pathological features and hence is a potential prognostic biomarker. More importantly, we validated this signature in three independent GBM cohorts to test its generality. In conclusion, our integrated analysis using meta-analysis approach maximizes the use of the available gene expression data and robustly identified a 4-gene panel for predicting survival in primary GBM.