Dataset Information

Co-expression analysis of label-free proteomics data reveals prognostic biomarkers in pancreatic ductal adenocarcinoma

ABSTRACT: Despite extensive biological and clinical studies, including comprehensive genomics and transcriptomics analysis, pancreatic ductal adenocarcinoma (PDAC) remains a devastating disease, with poor survival and no effective therapies to date. Correlation networks are emerging as a powerful approach to infer tumor biology and to prioritize candidate genes as biomarkers or drug targets. In this study we applied a weighted co-expression analysis to the functionally relevant proteome of 20 surgically resected patients with PDAC. We obtained twelve modules with overlapping yet distinct biology, which implicated metabolism and ECM complexes in several modules. Notably, one module enriched for metabolic processes and epithelial-mesenchymal-transition (EMT) was significantly associated with overall survival (p=0.01) and was validated in public RNA data (p=0.02). The prognostic value of three proteins (SPTBN1, KHSRP and PYGL) belonging to this module was confirmed using immunohistochemistry in a cohort of 82 radically resected patients.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Connie Ramona Jimenez

PROVIDER: MSV000096427 | MassIVE | Fri Nov 15 07:33:00 GMT 2024

SECONDARY ACCESSION(S): PXD015744

REPOSITORIES: MassIVE

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Co-expression analysis of pancreatic cancer proteome reveals biology and prognostic biomarkers.

Mantini G G Vallés A M AM Le Large T Y S TYS Capula M M Funel N N Pham T V TV Piersma S R SR Kazemier G G Bijlsma M F MF Giovannetti E E Jimenez C R CR

Cellular oncology (Dordrecht, Netherlands) 20200829 6

<h4>Purpose</h4>Despite extensive biological and clinical studies, including comprehensive genomic and transcriptomic profiling efforts, pancreatic ductal adenocarcinoma (PDAC) remains a devastating disease, with a poor survival and limited therapeutic options. The goal of this study was to assess co-expressed PDAC proteins and their associations with biological pathways and clinical parameters.<h4>Methods</h4>Correlation network analysis is emerging as a powerful approach to infer tumor biology ...[more]

PMID: 32860207

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Project description:A six-gene signature predicts survival of patients with localized pancreatic ductal adenocarcinoma Pancreatic ductal adenocarcinoma (PDAC), comprising over 90% of all pancreatic cancers, remains a lethal disease with an estimated 232,000 new cases, 227,000 deaths per year worldwide, and a less than 5% five-year survival rate. Currently the standard of care for the 20% of patients with localized disease is surgery followed by chemotherapy, and in some cases radiation. Unfortunately despite the use of adjuvant therapy, median survival remains at best 23 months. It is important to note however, that up to 27% of patients with resected PDAC can survive for five years. However, in these studies examining actual long-term survivors, only two have found that adjuvant therapy was associated with improved survival. In addition, randomized controlled trials of gemcitabine-based chemotherapy demonstrate an improvement in median survival of at best 3 months. One possible conclusion from these studies is that tumor biology dictates outcome and that our current adjuvant therapy has only a modest impact on altering a patient's course.Hypothesizing that the dismal outcome of patients with localized disease is due to the presence of micrometastasic disease, current clinical investigation has focused on preoperative or neoadjuvant therapy. This approach, where patients who cannot tolerate the stress of therapy or develop metastatic disease during treatment are spared surgery, has demonstrated an overall survival of 34 months in this highly selected patient population. Therefore the ability to select patients who would most benefit from a neoadjuvant approach may be important. One way to do this is to define a prognostic gene signature that can identify patients with more aggressive tumor biology upfront. reference x sample The 30 Nebraska and UNC samples were not analyzed with clinical data so it is not provided. One of the PE samples is missing some clinical data.

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.