Cholangiocarcinoma (CCA) and pancreatic adenocarcinoma (PDAC) may lead to the
development of extrahepatic obstructive cholestasis. However, biliary stenoses can also be caused
by benign conditions, and the identification of their etiology still remains a clinical challenge.
We performed metabolomic and proteomic analyses of bile from patients with benign (n = 36)
and malignant conditions, CCA (n = 36) or PDAC (n = 57), undergoing endoscopic retrograde
cholangiopancreatography with the aim of characterizing bile composition in biliopancreatic disease
and identifying biomarkers for the differential diagnosis of biliary strictures. Comprehensive analyses
of lipids, bile acids and small molecules were carried out using mass spectrometry (MS) and nuclear
magnetic resonance spectroscopy (1H-NMR) in all patients. MS analysis of bile proteome was
performed in five patients per group. We implemented artificial intelligence tools for the selection
of biomarkers and algorithms with predictive capacity. Our machine-learning pipeline included
the generation of synthetic data with properties of real data, the selection of potential biomarkers
(metabolites or proteins) and their analysis with neural networks (NN). Selected biomarkers were
then validated with real data. We identified panels of lipids (n = 10) and proteins (n = 5) that when
analyzed with NN algorithms discriminated between patients with and without cancer with an
unprecedented accurac