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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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Autores principales: 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, 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7352944/
https://www.ncbi.nlm.nih.gov/pubmed/32575903
http://dx.doi.org/10.3390/cancers12061644
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author 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, 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.
author_facet 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, 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.
author_sort Urman, Jesús M.
collection PubMed
description 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 ((1)H-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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spelling pubmed-73529442020-07-15 Pilot Multi-Omic Analysis of Human Bile from Benign and Malignant Biliary Strictures: A Machine-Learning Approach 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, 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. Cancers (Basel) Article 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 ((1)H-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. MDPI 2020-06-21 /pmc/articles/PMC7352944/ /pubmed/32575903 http://dx.doi.org/10.3390/cancers12061644 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
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, 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.
Pilot Multi-Omic Analysis of Human Bile from Benign and Malignant Biliary Strictures: A Machine-Learning Approach
title Pilot Multi-Omic Analysis of Human Bile from Benign and Malignant Biliary Strictures: A Machine-Learning Approach
title_full Pilot Multi-Omic Analysis of Human Bile from Benign and Malignant Biliary Strictures: A Machine-Learning Approach
title_fullStr Pilot Multi-Omic Analysis of Human Bile from Benign and Malignant Biliary Strictures: A Machine-Learning Approach
title_full_unstemmed Pilot Multi-Omic Analysis of Human Bile from Benign and Malignant Biliary Strictures: A Machine-Learning Approach
title_short Pilot Multi-Omic Analysis of Human Bile from Benign and Malignant Biliary Strictures: A Machine-Learning Approach
title_sort pilot multi-omic analysis of human bile from benign and malignant biliary strictures: a machine-learning approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7352944/
https://www.ncbi.nlm.nih.gov/pubmed/32575903
http://dx.doi.org/10.3390/cancers12061644
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