A ten-microRNA expression signature predicts survival in XXXX .

@entity1 @entity463 @entity5 @entity1313
@entity463 ( @entity463 ) is the most common and aggressive primary @entity1313 with very poor @entity1 median survival. To identify a microRNA (miRNA) expression signature that can predict @entity463 @entity1 survival, we analyzed the miRNA expression data of @entity463 @entity1 (n=222) derived from The @entity5 (TCGA) dataset. We divided the @entity1 randomly into training and testing sets with equal number in each group. We identified 10 significant miRNAs using Cox regression analysis on the training set and formulated a risk score based on the expression signature of these miRNAs that segregated the @entity1 into high and low risk groups with significantly different survival times (hazard ratio [HR]=2.4; 95% CI=1.4-3.8; p<0.0001). Of these 10 miRNAs, 7 were found to be risky miRNAs and 3 were found to be protective. This signature was independently validated in the testing set (HR=1.7; 95% CI=1.1-2.8; p=0.002). @entity463 @entity1 with high risk scores had overall poor survival compared to the @entity1 with low risk scores. Overall survival among the entire @entity1 set was 35.0% at 2 years, 21.5% at 3 years, 18.5% at 4 years and 11.8% at 5 years in the low risk group, versus 11.0%, 5.5%, 0.0 and 0.0% respectively in the high risk group (HR=2.0; 95% CI=1.4-2.8; p<0.0001). Cox multivariate analysis with @entity1 age as a covariate on the entire @entity1 set identified risk score based on the 10 miRNA expression signature to be an independent predictor of @entity1 survival (HR=1.120; 95% CI=1.04-1.20; p=0.003). Thus we have identified a miRNA expression signature that can predict @entity463 @entity1 survival. These findings may have implications in the understanding of gliomagenesis, development of targeted therapy and selection of high risk @entity5 @entity1 for adjuvant therapy.
