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

G. Mantini, A. M. Vallés, T. Y.S. Le Large, M. Capula, N. Funel, T. V. Pham, S. R. Piersma, G. Kazemier, M. F. Bijlsma, E. Giovannetti, C. R. Jimenez

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18 Citations (Scopus)


Purpose: 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. Methods: Correlation network analysis is emerging as a powerful approach to infer tumor biology from omics data and to prioritize candidate genes as biomarkers or drug targets. In this study, we applied a weighted gene co-expression network analysis (WGCNA) to the proteome of 20 surgically resected PDAC specimens (PXD015744) and confirmed its clinical value in 82 independent primary cases. Results: Using WGCNA, we obtained twelve co-expressed clusters with a distinct biology. Notably, we found that one module enriched for metabolic processes and epithelial-mesenchymal-transition (EMT) was significantly associated with overall survival (p = 0.01) and disease-free survival (p = 0.03). The prognostic value of three proteins (SPTBN1, KHSRP and PYGL) belonging to this module was confirmed using immunohistochemistry in a cohort of 82 independent resected patients. Risk score evaluation of the prognostic signature confirmed its association with overall survival in multivariate analyses. Finally, immunofluorescence analysis confirmed co-expression of SPTBN1 and KHSRP in Hs766t PDAC cells. Conclusions: Our WGCNA analysis revealed a PDAC module enriched for metabolic and EMT-associated processes. In addition, we found that three of the proteins involved were associated with PDAC survival.

Original languageEnglish
Pages (from-to)1147-1159
Number of pages13
JournalCellular oncology
Issue number6
Early online date2020
Publication statusPublished - Dec 2020


  • Pancreatic cancer
  • Prognostic biomarkers
  • Protein co-expression
  • Proteomics
  • Systems biology

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