Pilot Multi-Omic Analysis of Human Bile from Benign and Malignant Biliary Strictures: A Machine-Learning Approach

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...

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Detalhes bibliográficos
Autores: Urman, Jesús M., Herranz, José M., Uriarte, Iker, Rullán, María, Oyón, Daniel, González, Belén, Fernandez-Urién, Ignacio, Carrascosa, Juan, Bolado, Federico, Zabalza, Lucía, Arechederra, María, Alvarez-Sola, Gloria, Colyn, Leticia, Latasa, María U., Puchades-Carrasco, Leonor, Pineda-Lucena, Antonio, Iraburu, María J., Iruarrizaga-Lejarreta, Marta, Alonso, Cristina, Sangro, Bruno, Purroy, Ana, Gil, Isabel, Carmona, Lorena, Cubero Palero, Francisco Javier, Martínez-Chantar, María L., Banales, Jesús M., Romero, Marta R., Macias, Rocio I.R., Monte, Maria J., Marín, Jose J. G., Vila, Juan J., Corrales, Fernando J., Berasain, Carmen, Fernández-Barrena, Maite G., Avila, Matías A.
Tipo de documento: artigo
Data de publicação:2020
País:España
Recursos:Universidad Complutense de Madrid (UCM)
Repositório:Docta Complutense
Idioma:inglês
OAI Identifier:oai:docta.ucm.es:20.500.14352/8342
Acesso em linha:https://hdl.handle.net/20.500.14352/8342
Access Level:Acceso aberto
Palavra-chave:Human bile
cholangiocarcinoma
pancreatic adenocarcinoma
lipidomics
proteomics
machine-learning
Gastroenterología y hepatología
Oncología
3205.03 Gastroenterología
3201.01 Oncología
Descrição
Resumo: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 accuracy.

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