Dataset Information

Metabolic/Proteomic Signature Defines Two Glioblastoma Subtypes With Different Clinical Outcome.

ABSTRACT: Glioblastoma (GBM) is one of the deadliest human cancers. Because of the extremely unfavorable prognosis of GBM, it is important to develop more effective diagnostic and therapeutic strategies based on biologically and clinically relevant subclassification systems. Analyzing a collection of seventeen patient-derived glioblastoma stem-like cells (GSCs) by gene expression profiling, NMR spectroscopy and signal transduction pathway activation, we identified two GSC clusters, one characterized by a pro-neural-like phenotype and the other showing a mesenchymal-like phenotype. Evaluating the levels of proteins differentially expressed by the two GSC clusters in the TCGA GBM sample collection, we found that SRC activation is associated with a GBM subgroup showing better prognosis whereas activation of RPS6, an effector of mTOR pathway, identifies a subgroup with a worse prognosis. The two clusters are also differentiated by NMR spectroscopy profiles suggesting a potential prognostic stratification based on metabolic evaluation. Our data show that the metabolic/proteomic profile of GSCs is informative of the genomic/proteomic GBM landscape, which differs among tumor subtypes and is associated with clinical outcome.

SUBMITTER: Marziali G

PROVIDER: S-EPMC4746700 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Metabolic/Proteomic Signature Defines Two Glioblastoma Subtypes With Different Clinical Outcome.

Marziali G G Signore M M Buccarelli M M Grande S S Palma A A Biffoni M M Rosi A A D'Alessandris Q G QG Martini M M Larocca L M LM De Maria R R Pallini R R Ricci-Vitiani L L

Scientific reports 20160209

Glioblastoma (GBM) is one of the deadliest human cancers. Because of the extremely unfavorable prognosis of GBM, it is important to develop more effective diagnostic and therapeutic strategies based on biologically and clinically relevant subclassification systems. Analyzing a collection of seventeen patient-derived glioblastoma stem-like cells (GSCs) by gene expression profiling, NMR spectroscopy and signal transduction pathway activation, we identified two GSC clusters, one characterized by a ...[more]

PMID: 26857460

Similar Datasets

Project description:Little is known about the molecular pathogenesis of schizophrenia, possibly because of unrecognized heterogeneity in diagnosed patient populations. We analyzed gene expression data collected from the dorsolateral prefrontal cortex (DLPFC) of post-mortem frozen brains of 189 adult diagnosed schizophrenics and 206 matched controls. Transcripts from 633 genes are differentially expressed in the DLPFC of schizophrenics as compared to controls at Bonferroni-corrected significance levels. Seventeen of those genes are differentially expressed at very high significance levels (<10-8 after Bonferroni correction). The findings were closely replicated in a dataset from an entirely unrelated source. The statistical significance of this differential gene expression is being driven by about half of the schizophrenic DLPFC samples, and importantly, it is the same half of the samples that is driving the significance for almost all of the differentially expressed transcripts. Weighted gene co-expression network analysis (WGCNA) of the schizophrenic subjects, based on the transcripts differentially expressed in the schizophrenics as compared to controls, divides them into two groups. "Type 1" schizophrenics have a DLPFC transcriptome similar to that of controls with only four differentially expressed genes identified. "Type 2" schizophrenics have a DLPFC transcriptome dramatically different from that of controls, with 3529 expression array probes to 3092 genes detecting transcripts that are differentially expressed at very high significance levels. These findings were re-tested and replicated in a separate independent cohort, using the RNAseq data from the DLPFC of an independent set of schizophrenics and control subjects. We suggest the hypothesis that these striking differences in DLPFC transcriptomes, identified and replicated in two populations, imply a fundamental biologic difference between these two groups of diagnosed schizophrenics, and we propose specific paths for further testing and expanding the hypothesis.

Project description:Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer death in the US. Despite multiple large-scale genetic sequencing studies, identification of predictors of patient survival remains challenging. We performed a comprehensive assessment and integrative analysis of large-scale gene expression datasets, across multiple platforms, to enable discovery of a prognostic gene signature for patient survival in pancreatic cancer. PDAC RNA-Sequencing data from The Cancer Genome Atlas was stratified into Survival+ (>2-year survival) and Survival-(<1-year survival) cohorts (n = 47). Comparisons of RNA expression profiles between survival groups and normal pancreatic tissue expression data from the Gene Expression Omnibus generated an initial PDAC specific prognostic differential expression gene list. The candidate prognostic gene list was then trained on the Australian pancreatic cancer dataset from the ICGC database (n = 103), using iterative sampling based algorithms, to derive a gene signature predictive of patient survival. The gene signature was validated in 2 independent patient cohorts and against existing PDAC subtype classifications. We identified 707 candidate prognostic genes exhibiting differential expression in tumor versus normal tissue. A substantial fraction of these genes was also found to be differentially methylated between survival groups. From the candidate gene list, a 5-gene signature (ADM, ASPM, DCBLD2, E2F7, and KRT6A) was identified. Our signature demonstrated significant power to predict patient survival in two distinct patient cohorts and was independent of AJCC TNM staging. Cross-validation of our gene signature reported a better ROC AUC (? 0.8) when compared to existing PDAC survival signatures. Furthermore, validation of our signature through immunohistochemical analysis of patient tumor tissue and existing gene expression subtyping data in PDAC, demonstrated a correlation to the presence of vascular invasion and the aggressive squamous tumor subtype. Assessment of these genes in patient biopsies could help further inform risk-stratification and treatment decisions in pancreatic cancer.

Dataset Information

Metabolic/Proteomic Signature Defines Two Glioblastoma Subtypes With Different Clinical Outcome.

Publications

Metabolic/Proteomic Signature Defines Two Glioblastoma Subtypes With Different Clinical Outcome.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets