Abstract
PurposeDespite 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.MethodsCorrelation 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.ResultsUsing 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.ConclusionsOur 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.
Highlights
Pancreatic ductal adenocarcinoma (PDAC) is the most common tumor type of the pancreas with a five-year survival rate not exceeding 8% [1]
We report a PDAC proteomics analysis based on mass spectrometry (MS) data coupled to weighted gene co-expression network analysis (WGCNA) to define networks of highly correlated proteins with specific functions associated with patient prognosis
To obtain proteome level insight into PDAC cells, we used indepth proteomics based on label-free nanoLC-MS/MS of gelfractionated proteins to generate proteomic profiles of a cohort of 20 patients
Summary
Pancreatic ductal adenocarcinoma (PDAC) is the most common tumor type of the pancreas with a five-year survival rate not exceeding 8% [1]. Multiple statistical methods and freely available bioinformatics tools have been developed that can extrapolate important features from high-throughput data, e.g. pinpointing genes associated with clinical parameters such as cancer status or patient survival [4]. Pinpointing genes associated with clinical parameters such as cancer status or patient survival [4] In this context, networks based on co-expression data [5] have extensively been used to identify densely interconnected genes associated with phenotypic traits. Most of the available algorithms have been applied to microarray- and RNAseq-based expression data [6, 7] Using these approaches Tang et al [8], for example, identified new prognostic markers in breast cancer. An integrative analysis of co-expression networks from proteomics and transcriptomics data in Alzheimer’s disease revealed protein-specific networks in both asymptomatic and symptomatic patients [10, 11]
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