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.

The standard enthalpy and entropy of formation of Rh2O3 - A third-law optimization

The standard Gibbs energy of formation of Rh203 at high temperature has been determined recently with high precision. The new data are significantly different from those given in thermodynamic compilations.Accurate values for enthalpy and entropy of formation at 298.15 K could not be evaluated from the new data,because reliable values for heat capacity of Rh2O3 were not available. In this article, a new measurement of the high temperature heat capacity of Rh2O3 using differential scanning calorimetry (DSC) is presented.The new values for heat capacity also differ significantly from those given in compilations. The information on heat capacity is coupled with standard Gibbs energy of formation to evaluate values for standard enthalpy and entropy of formation at 289.15 K using a multivariate analysis. The results suggest a major revision in thermodynamic data for Rh2O3. For example, it is recommended that the standard entropy of Rh203 at 298.15 K be changed from 106.27 J mol-' K-'given in the compilations of Barin and Knacke et al. to 75.69 J mol-' K". The recommended revision in the standard enthalpy of formation is from -355.64 kJ mol-'to -405.53 kJ mol".

Fourteen gene GBM prognostic signature identifies association of immune response pathway and mesenchymal subtype with high risk group

Background: Recent research on glioblastoma (GBM) has focused on deducing gene signatures predicting prognosis. The present study evaluated the mRNA expression of selected genes and correlated with outcome to arrive at a prognostic gene signature. Methods: Patients with GBM (n = 123) were prospectively recruited, treated with a uniform protocol and followed up. Expression of 175 genes in GBM tissue was determined using qRT-PCR. A supervised principal component analysis followed by derivation of gene signature was performed. Independent validation of the signature was done using TCGA data. Gene Ontology and KEGG pathway analysis was carried out among patients from TCGA cohort. Results: A 14 gene signature was identified that predicted outcome in GBM. A weighted gene (WG) score was found to be an independent predictor of survival in multivariate analysis in the present cohort (HR = 2.507; B = 0.919; p < 0.001) and in TCGA cohort. Risk stratification by standardized WG score classified patients into low and high risk predicting survival both in our cohort (p = <0.001) and TCGA cohort (p = 0.001). Pathway analysis using the most differentially regulated genes (n = 76) between the low and high risk groups revealed association of activated inflammatory/immune response pathways and mesenchymal subtype in the high risk group. Conclusion: We have identified a 14 gene expression signature that can predict survival in GBM patients. A network analysis revealed activation of inflammatory response pathway specifically in high risk group. These findings may have implications in understanding of gliomagenesis, development of targeted therapies and selection of high risk cancer patients for alternate adjuvant therapies.

A DNA methylation prognostic signature of glioblastoma: identification of NPTX2-PTEN-NF-kappa B nexus

Glioblastoma (GBM) is the most common, malignant adult primary tumor with dismal patient survival, yet the molecular determinants of patient survival are poorly characterized. Global methylation profile of GBM samples (our cohort; n = 44) using high-resolution methylation microarrays was carried out. Cox regression analysis identified a 9-gene methylation signature that predicted survival in GBM patients. A risk-score derived from methylation signature predicted survival in univariate analysis in our and The Cancer Genome Atlas (TCGA) cohort. Multivariate analysis identified methylation risk score as an independent survival predictor in TCGA cohort. Methylation risk score stratified the patients into low-risk and high-risk groups with significant survival difference. Network analysis revealed an activated NF-kappa B pathway association with high-risk group. NF-kappa B inhibition reversed glioma chemoresistance, and RNA interference studies identified interleukin-6 and intercellular adhesion molecule-1 as key NF-kappa B targets in imparting chemoresistance. Promoter hypermethylation of neuronal pentraxin II (NPTX2), a risky methylated gene, was confirmed by bisulfite sequencing in GBMs. GBMs and glioma cell lines had low levels of NPTX2 transcripts, which could be reversed upon methylation inhibitor treatment. NPTX2 overexpression induced apoptosis, inhibited proliferation and anchorage-independent growth, and rendered glioma cells chemosensitive. Furthermore, NPTX2 repressed NF-kappa B activity by inhibiting AKT through a p53-PTEN-dependent pathway, thus explaining the hypermethylation and downregulation of NPTX2 in NF-kappa B-activated high-risk GBMs. Taken together, a 9-gene methylation signature was identified as an independent GBM prognosticator and could be used for GBM risk stratification. Prosurvival NF-kappa B pathway activation characterized high-risk patients with poor prognosis, indicating it to be a therapeutic target. (C) 2013 AACR.

Phytoplankton size structure in the southern Bay of Bengal modified by the Summer Monsoon Current and associated eddies: Implications on the vertical biogenic flux

The present study combines field and satellite observations to investigate how hydrographical transformations influence phytoplankton size structure in the southern Bay of Bengal during the peak Southwest Monsoon/Summer Monsoon (July-August). The intrusion of the Summer Monsoon Current (SMC) into the Bay of Bengal and associated changes in sea surface chemistry, traceable eastward up to 90 degrees E along 8 degrees N, seems to influence biology of the region significantly. Both in situ and satellite (MODIS) data revealed low surface chlorophyll except in the area influenced by the SMC During the study period, two well-developed cydonic eddies (north) and an anti-cyclonic eddy (south), closely linked to the main eastward flow of the SMC, were sampled. Considering the capping effect of the low-saline surface water that is characteristic of the Bay of Bengal, the impact of the cyclonic eddy, estimated in terms of enhanced nutrients and chlorophyll, was mostly restricted to the subsurface waters (below 20 m depth). Conversely, the anti-cyclonic eddy aided by the SMC was characterized by considerably higher nutrient concentration and chlorophyll in the upper water column (upper 60 m), which was contrary to the general characteristic of such eddies. Albeit smaller phytoplankton predominated the southern Bay of Bengal (60-95% of the total chlorophyll), the contribution of large phytoplankton was double in the regions influenced by the SMC and associated eddies. Multivariate analysis revealed the extent to which SMC-associated eddies spatially influence phytoplankton community structure. The study presents the first direct quantification of the size structure of phytoplankton from the southern Bay of Bengal and demonstrates that the SMC-associated hydrographical ramifications significantly increase the phytoplankton biomass contributed by larger phytoplankton and thereby influence the vertical opal and organic carbon flux in the region. (C) 2014 Elsevier B.V. All rights reserved.

Associated jet and subjet rates in light-quark and gluon jet discrimination

We show that in studies of light quark- and gluon-initiated jet discrimination, it is important to include the information on softer reconstructed jets (associated jets) around a primary hard jet. This is particularly relevant while adopting a small radius parameter for reconstructing hadronic jets. The probability of having an associated jet as a function of the primary jet transverse momentum (PT) and radius, the minimum associated jet pi, and the association radius is computed up to next-to-double logarithmic accuracy (NDLA), and the predictions are compared with results from Herwig++, Pythia6 and Pythia8 Monte Carlos (MC). We demonstrate the improvement in quark-gluon discrimination on using the associated jet rate variable with the help of a multivariate analysis. The associated jet rates are found to be only mildly sensitive to the choice of parton shower and hadronization algorithms, as well as to the effects of initial state radiation and underlying event. In addition, the number of k(t) subjets of an anti-k(t) jet is found to be an observable that leads to a rather uniform prediction across different MC's, broadly being in agreement with predictions in NDLA, as compared to the often used number of charged tracks observable.