A glycolysis-based ten-gene signature correlates with the clinical outcome, molecular subtype and IDH1 mutation in glioblastoma

Reprogrammed metabolism is a hallmark of cancer. Glioblastoma（GBM） tumor cells predominantly utilize aerobic glycolysis for the biogenesis of energy and intermediate nutrients. However, in GBM, the clinical significance of glycolysis and its underlying relations with the molecular features such as IDH1 mutation and subtype have not been elucidated yet. Herein, based on glioma datasets including TCGA（The Cancer Genome Atlas）, REMBRANDT（Repository for Molecular Brain Neoplasia Data） and GSE16011 we established a glycolytic gene expression signature score（GGESS） by incorporating ten glycolytic genes. Then we performed survival analyses and investigated the correlations between GGESS and IDH1 mutation as well as the molecular subtypes in GBMs. The results showed that GGESS independently predicted unfavorable prognosis and poor response to chemotherapy of GBM patients. Notably, GGESS was high in GBMs of mesenchymal subtype but low in IDH1-mutant GBMs. Furthermore, we found that the promoter regions of tumor-promoting glycolytic genes were hypermethylated in IDH1-mutant GBMs.Finally, we found that high GGESS also predicted poor prognosis and poor response to chemotherapy when investigating IDH1-wild type GBM patients only. Collectively, glycolysis represented by GGESS predicts unfavorable clinical outcome of GBM patients and is closely associated with mesenchymal subtype and IDH1 mutation in GBMs.

Successful treatment of a severely injured victim from 8.12 Tianjin Port Explosion, China

A male patient, 55 years old, was found from a container yard 65 h later following a chemical warehouse explosion in Tianjin, China on August 12, 2015. He was about 50 m away from the explosion center. He was subjected to compound multiple trauma, multi-viscera function damage, multiple fractures. hemothorax, traumatic wet lung. respiratory failure 1, hypovolemic shock and impaired liver and kidney functions. After a series of successful treatments, he was rescued and recovered well.

Adaptive total variation constraint hypergraph regularized NMF for single-cell RNA-seq data analysis

Background:Single-cell RNA sequencing(scRNA-seq)data provides a whole new view to study disease and cell differentiation development.With the explosive increment of scRNA-seq data,effective models are demanded for mining the intrinsic biological information.Methods:This paper proposes a novel non-negative matrix factorization(NMF)method for clustering and gene coexpression network analysis,termed Adaptive Total Variation Constraint Hypergraph Regularized NMF(ATV-HNMF).ATV-HNMF can adaptively select the different schemes to denoise the cluster or preserve the cluster boundary information between clusters based on the gradient information.Besides,ATV-HNMF incorporates hypergraph regularization,which can consider high-order relationships between cells to reserve the intrinsic structure of the space.Results:Experiments show that the performances on clustering outperform other compared methods,and the network construction results are consistent with previous studies,which illustrate that our model is effective and useful.Conclusion:From the clustering results,we can see that ATV-HNMF outperforms other methods,which can help us to understand the heterogeneity.We can discover many disease-related genes from the constructed network,and some are worthy of further clinical exploration.