Proteostasis Dysregulation in Pancreatic Cancer

The most common form of pancreatic cancer, pancreatic ductal adenocarcinoma (PDAC), has a dismal 5-year survival rate of less than 5%. Radical surgical resection, in combination with adjuvant chemotherapy, provides the best option for long-term patient survival. However, only approximately 20% of patients are resectable at the time of diagnosis, due to locally advanced or metastatic disease. There is an urgent need for the identification of new, specific, and more sensitive biomarkers for diagnosis, prognosis, and prediction to improve the treatment options for pancreatic cancer patients. Dysregulation of proteostasis is linked to many pathophysiological conditions, including various types of cancer. In this review, we report on findings relating to the main cellular protein degradation systems, the ubiquitin-proteasome system (UPS) and autophagy, in pancreatic cancer. The expression of several components of the proteolytic network, including E3 ubiquitinligases and deubiquitinating enzymes, are dysregulated in PDAC, which accounts for approximately 90% of all pancreatic malignancies. In the future, a deeper understanding of the emerging role of proteostasis in pancreatic cancer has the potential to provide clinically relevant biomarkers and new strategies for combinatorial therapeutic options to better help treat the patients.Peer reviewe

Integrating bioinformatics approaches for a comprehensive interpretation of metabolomics datasets

Access to high quality metabolomics data has become a routine component for biological studies. However, interpreting those datasets in biological contexts remains a challenge, especially because many identified metabolites are not found in biochemical pathway databases. Starting from statistical analyses, a range of new tools are available, including metabolite set enrichment analysis, pathway and network visualization, pathway prediction, biochemical databases and text mining. Integrating these approaches into comprehensive and unbiased interpretations must carefully consider both caveats of the metabolomics dataset itself as well as the structure and properties of the biological study design. Special considerations need to be taken when adopting approaches from genomics for use in metabolomics. R and Python programming language are enabling an easier exchange of diverse tools to deploy integrated workflows. This review summarizes the key ideas and latest developments in regards to these approaches