Deeper insights into long-term survival heterogeneity of Pancreatic Ductal Adenocarcinoma (PDAC) patients using integrative individual- and group-level transcriptome network analyses

Abstract

SummaryBackgroundPancreatic ductal adenocarcinoma (PDAC) is categorized as the seventh leading cause of cancer mortality worldwide. Its predictive markers for long-term survival are not well known. Therefore, it is interesting to delineate individual-specific perturbed genes when comparing long-term (LT) and short-term (ST) PDAC survivors, and to exploit the integrative individual- and group-based transcriptome profiling.MethodUsing a discovery cohort of 19 PDAC patients from CHU-Liège (Belgium), we first performed differential gene expression (DGE) analysis comparing LT to ST survivor. Second, we adopted unsupervised systems biology approaches to obtain gene modules linked to clinical features. Third, we created individual-specific perturbation profiles and identified key regulators across the LT patients. Furthermore, we applied two gene prioritization approaches: random walk-based Degree-Aware disease gene prioritizing (DADA) method to develop PDAC disease modules; Network-based Integration of Multi-omics Data (NetICS) to integrate group-based and individual-specific perturbed genes in relation to PDAC LT survival.FindingsWe identified 173 differentially expressed genes (DEGs) in ST and LT survivors and five modules (including 38 DEGs) showing associations to clinical traits such as tumor size and chemotherapy. DGE analysis identified differences in genes involved in metabolic and cell cycle activity. Validation of DEGs in the molecular lab suggested a role of REG4 and TSPAN8 in PDAC survival. Individual-specific omics changes across LT survivors revealed biological signatures such as focal adhesion and extracellular matrix receptors, implying a potential role in molecular-level heterogeneity of LT PDAC survivors. Via NetICS and DADA we not only identified various known oncogenes such as CUL1, SCF62, EGF, FOSL1, MMP9, and TGFB1, but also highlighted novel genes (TAC1, KCNH7, IRS4, DKK4).InterpretationOur proposed analytic workflow shows the advantages of combining clinical and omics data as well as individual- and group-level transcriptome profiling. It suggested novel potential transcriptome marks of LT survival heterogeneity in PDAC.FundingTélévie-FRS-FNRS