A Ten-microRNA Expression Signature Predicts Survival in Glioblastoma

Glioblastoma (GBM) is the most common and aggressive primary brain tumor with very poor patient median survival. To identify a microRNA (miRNA) expression signature that can predict GBM patient survival, we analyzed the miRNA expression data of GBM patients (n = 222) derived from The Cancer Genome Atlas (TCGA) dataset. We divided the patients 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 patients 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). GBM patients with high risk scores had overall poor survival compared to the patients with low risk scores. Overall survival among the entire patient 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 patient age as a covariate on the entire patient set identified risk score based on the 10 miRNA expression signature to be an independent predictor of patient survival (HR = 1.120; 95% CI = 1.04-1.20; p = 0.003). Thus we have identified a miRNA expression signature that can predict GBM patient survival. These findings may have implications in the understanding of gliomagenesis, development of targeted therapy and selection of high risk cancer patients for adjuvant therapy.

Stacking Interactions in RNA and DNA: Roll-Slide Energy Hyperspace for Ten Unique Dinucleotide Steps

Understanding dinucleotide sequence directed structures of nuleic acids and their variability from experimental observation remained ineffective due to unavailability of statistically meaningful data. We have attempted to understand this from energy scan along twist, roll, and slide degrees of freedom which are mostly dependent on dinucleotide sequence using ab initio density functional theory. We have carried out stacking energy analysis in these dinucleotide parameter phase space for all ten unique dinucleotide steps in DNA and RNA using DFT-D by B97X-D/6-31G(2d,2p), which appears to satisfactorily explain conformational preferences for AU/AU step in our recent study. We show that values of roll, slide, and twist of most of the dinucleotide sequences in crystal structures fall in the low energy region. The minimum energy regions with large twist values are associated with the roll and slide values of B-DNA, whereas, smaller twist values correspond to higher stability to RNA and A-DNA like conformations. Incorporation of solvent effect by CPCM method could explain the preference shown by some sequences to occur in B-DNA or A-DNA conformations. Conformational preference of BII sub-state in B-DNA is preferentially displayed mainly by pyrimidine-purine steps and partly by purine-purine steps. The purine-pyrimidine steps show largest effect of 5-methyl group of thymine in stacking energy and the introduction of solvent reduces this effect significantly. These predicted structures and variabilities can explain the effect of sequence on DNA and RNA functionality. (c) 2014 Wiley Periodicals, Inc. Biopolymers 103: 134-147, 2015.