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: 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 recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/8342
Acceso en línea:https://hdl.handle.net/20.500.14352/8342
Access Level:acceso abierto
Palabra clave:Human bile
cholangiocarcinoma
pancreatic adenocarcinoma
lipidomics
proteomics
machine-learning
Gastroenterología y hepatología
Oncología
3205.03 Gastroenterología
3201.01 Oncología
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oai_identifier_str oai:docta.ucm.es:20.500.14352/8342
network_acronym_str ES
network_name_str España
repository_id_str
spelling Pilot Multi-Omic Analysis of Human Bile from Benign and Malignant Biliary Strictures: A Machine-Learning ApproachUrman, Jesús M.Herranz, José M.Uriarte, IkerRullán, MaríaOyón, DanielGonzález, BelénFernandez-Urién, IgnacioCarrascosa, JuanBolado, FedericoZabalza, LucíaArechederra, MaríaAlvarez-Sola, GloriaColyn, LeticiaLatasa, María U.Puchades-Carrasco, LeonorPineda-Lucena, AntonioIraburu, María J.Iruarrizaga-Lejarreta, MartaAlonso, CristinaSangro, BrunoPurroy, AnaGil, IsabelCarmona, LorenaCubero Palero, Francisco JavierMartínez-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, CarmenFernández-Barrena, Maite G.Avila, Matías A.Human bilecholangiocarcinomapancreatic adenocarcinomalipidomicsproteomicsmachine-learningGastroenterología y hepatologíaOncología3205.03 Gastroenterología3201.01 OncologíaCholangiocarcinoma (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.MDPIUniversidad Complutense de Madrid20202020-06-2120202020-06-21journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/8342reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Atribución 3.0 Españahttps://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/83422026-06-02T12:44:21Z
dc.title.none.fl_str_mv 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
spellingShingle Pilot Multi-Omic Analysis of Human Bile from Benign and Malignant Biliary Strictures: A Machine-Learning Approach
Urman, Jesús M.
Human bile
cholangiocarcinoma
pancreatic adenocarcinoma
lipidomics
proteomics
machine-learning
Gastroenterología y hepatología
Oncología
3205.03 Gastroenterología
3201.01 Oncología
title_short 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_sort Pilot Multi-Omic Analysis of Human Bile from Benign and Malignant Biliary Strictures: A Machine-Learning Approach
dc.creator.none.fl_str_mv 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.
author Urman, Jesús M.
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 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.
author_role author
author2 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.
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv Human bile
cholangiocarcinoma
pancreatic adenocarcinoma
lipidomics
proteomics
machine-learning
Gastroenterología y hepatología
Oncología
3205.03 Gastroenterología
3201.01 Oncología
topic Human bile
cholangiocarcinoma
pancreatic adenocarcinoma
lipidomics
proteomics
machine-learning
Gastroenterología y hepatología
Oncología
3205.03 Gastroenterología
3201.01 Oncología
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 (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.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020-06-21
2020
2020-06-21
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/8342
url https://hdl.handle.net/20.500.14352/8342
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución 3.0 España
https://creativecommons.org/licenses/by/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución 3.0 España
https://creativecommons.org/licenses/by/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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