期刊
CANCERS
卷 12, 期 6, 页码 -出版社
MDPI
DOI: 10.3390/cancers12061644
关键词
human bile; cholangiocarcinoma; pancreatic adenocarcinoma; lipidomics; proteomics; machine-learning
类别
资金
- Instituto de Salud Carlos III (ISCIII) - Fondo Europeo de Desarrollo Regional (FEDER) Una manera de hacer Europa [PI16/01126, PI19/00819, PI15/01132, PI18/01075]
- Miguel Servet Program [CON14/00129]
- Fundacion Cientifica de la Asociacion Espanola Contra el Cancer (AECC Scientific Foundation)
- Gobierno de Navarra Salud [58/17]
- La Caixa Foundation
- AMMF The Cholangiocarcinoma Charity, UK [2018/117]
- PSC Partners US, PSC Supports UK [06119JB]
- Horizon 2020 (H2020) ESCALON project [H2020-SC1-BHC-2018-2020]
- BIOEF (Basque Foundation for Innovation and Health Research: EiTB Maratoia) [BIO15/CA/016/BD, BIO15/CA/011]
- Department of Health of the Basque Country [2017111010]
- La Caixa Foundation [LCF/PR/HP17/52190004]
- Mineco-Feder [SAF2017-87301-R]
- Fundacion BBVA
- MCIU [SEV-2016-0644]
- Generalitat Valenciana
- European Regional Development Fund (FEDER) funds (PO FEDER of Comunitat Valenciana 2014-2020)
- Gobierno de Navarra fellowship
- AECC post-doctoral fellowship
- Ramon y Cajal Program contracts [RYC-2014-15242, RYC2018-024475-1]
- Fundacion Eugenio Rodriguez Pascual
- Fundacion Echebano
- Fundacion Mario Losantos
- Fundacion M Torres and Mr. Eduardo Avila
- Comunidad de Madrid Grant [B2017/BMD-3817]
- [PT17/0019/0001]
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 (H-1-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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