MiRNome profiling of glioblastoma tissues and peritumoral regions
Ontology highlight
ABSTRACT:
INSTRUMENT(S): AB 5500xl Genetic Analyzer
ORGANISM(S): Homo sapiens
SUBMITTER: Silvia Ciafre'
PROVIDER: E-MTAB-3239 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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Project description:Purpose: The goals of this study are to delineate genes when comparing long-term (LT) and short-term (ST) PDAC survivors, and to exploit the merits of extensive integrative individual- and group-based transcriptome profiling. Method: Using a discovery cohort of 19 PDAC patients, we first performed differential gene expression (DGE) analysis comparing LT to ST PDAC groups. Second, we adopted unsupervised systems biology approaches to obtain meaningful gene modules showing associations to clinical features. Third, we created individual-level gene expression perturbation profiles and identified key regulators across the perturbed profiles of LT patients at the pathway and motif level. Furthermore, we applied two network context gene prioritization approaches for the depiction of PDAC specific regulatory genes. In particular, we used the random walk-based DADA method to develop PDAC disease modules considering the use of PDAC seed genes (via prior biological knowledge). As an alternative, we used NetICS to prioritize PDAC survival associated genes, by integrating information regarding group-based and individual-specific perturbed genes. Result: DGE analysis resulted in differences in the expression of genes involved in immune responses, cell cycle and metabolic pathways. Validation of a selection of DGEs in the molecular lab suggested a role of REG4 and TSPAN8 in PDAC survival. Detailed inspection of individual-specific omics changes across LT survivors revealed biological signatures associated with focal adhesion and extracellular matrix (ECM) receptors, commonly perturbed in at least 2 out of 9 LT survivors, implying a potential role in molecular-level heterogeneity of LT PDAC survivors. Network centric approaches such as NetICS (integrating group-based and individual-specific perturbed genes) and DADA (degree-aware disease gene prioritizing), identified various known oncogenes such as CUL1, SCF62, EGF, FOSL1, MMP9, and TGFB1. In addition, we identified TAC1, KCNH7, IRS4, DKK4, further warranting detailed follow-up investigations. Conclusion:Our proposed analytic workflow, combining clinical and omics data, and individual-level and group-level transcriptome profiling, highlighted transcriptome marks of PDAC long-term survival heterogeneity. The identified genes open up avenues towards better understanding the mechanisms underlying PDAC survival extension.
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