Allelic imbalance metre (Allim), a new tool for measuring allele‐specific gene expression with RNA‐seq data

Estimating differences in gene expression among alleles is of high interest for many areas in biology and medicine. Here, we present a user‐friendly software tool, Allim, to estimate allele‐specific gene expression. Because mapping bias is a major problem for reliable estimates of allele‐specific gene expression using RNA‐seq, Allim combines two different strategies to account for the mapping biases. In order to reduce the mapping bias, Allim first generates a polymorphism‐aware reference genome that accounts for the sequence variation between the alleles. Then, a sequence‐specific simulation tool estimates the residual mapping bias. Statistical tests for allelic imbalance are provided that can be used with the bias corrected RNA‐seq data.     

Pan‐cancer RNA‐seq data stratifies tumours by some hallmarks of cancer

Numerous genetic and epigenetic alterations cause functional changes in cell biology underlying cancer. These hallmark functional changes constitute potentially tissue‐independent anticancer therapeutic targets. We hypothesized that RNA‐Seq identifies gene expression changes that underly those hallmarks, and thereby defines relevant therapeutic targets. To test this hypothesis, we analysed the publicly available TCGA‐TARGET‐GTEx gene expression data set from the University of California Santa CruzToil recompute project using WGCNA to delineate co‐correlated ‘modules’ from tumour gene expression profiles and functional enrichment of these modules to hierarchically cluster tumours. This stratified tumours according to T cell activation, NK‐cell activation, complement cascade, ATM, Rb, angiogenic, MAPK, ECM receptor and histone modification signalling. These correspond to the cancer hallmarks of avoiding immune destruction, tumour‐promoting inflammation, evading growth suppressors, inducing angiogenesis, sustained proliferative signalling, activating invasion and metastasis, and genome instability and mutation. This approach did not detect pathways corresponding to the cancer enabling replicative immortality, resisting cell death or deregulating cellular energetics hallmarks. We conclude that RNA‐Seq stratifies tumours along some, but not all, hallmarks of cancer and, therefore, could be used in conjunction with other analyses collectively to inform precision therapy.     

Combined analysis of RNA‐sequence and microarray data reveals effective metabolism‐based prognostic signature for neuroblastoma

The relationship between metabolism reprogramming and neuroblastoma (NB) is largely unknown. In this study, one RNA‐sequence data set (n = 153) was used as discovery cohort and two microarray data sets (n = 498 and n = 223) were used as validation cohorts. Differentially expressed metabolic genes were identified by comparing stage 4s and stage 4 NBs. Twelve metabolic genes were selected by LASSO regression analysis and integrated into the prognostic signature. The metabolic gene signature successfully stratifies NB patients into two risk groups and performs well in predicting survival of NB patients. The prognostic value of the metabolic gene signature is also independent with other clinical risk factors. Nine metabolism‐related long non‐coding RNAs (lncRNAs) were also identified and integrated into the metabolism‐related lncRNA signature. The lncRNA signature also performs well in predicting survival of NB patients. These results suggest that the metabolic signatures have the potential to be used for risk stratification of NB. Gene set enrichment analysis (GSEA) reveals that multiple metabolic processes (including oxidative phosphorylation and tricarboxylic acid cycle, both of which are emerging targets for cancer therapy) are enriched in the high‐risk NB group, and no metabolic process is enriched in the low‐risk NB group. This result indicates that metabolism reprogramming is associated with the progression of NB and targeting certain metabolic pathways might be a promising therapy for NB.