1. Metabolomic Analysis of Gastric Cancer Progression within the Correa’s Cascade Using Ultraperformance Liquid Chromatography–Mass Spectrometry
- Author
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L. Gombau, María José Ramírez-Lázaro, María A. Vázquez-Sánchez, Carles Pericay, Félix Junquera, Anna Brunet-Vega, Daniel Sanjuan-Herráez, Xavier Calvet, Sergio Lario, Julia Kuligowski, and Guillermo Quintás
- Subjects
Male ,0301 basic medicine ,medicine.medical_specialty ,Glutamine ,Chronic gastritis ,Adenocarcinoma ,Biochemistry ,Gastroenterology ,Mass Spectrometry ,Helicobacter Infections ,Plasma ,03 medical and health sciences ,0302 clinical medicine ,Atrophy ,Stomach Neoplasms ,Internal medicine ,Biomarkers, Tumor ,medicine ,Humans ,Metabolomics ,Chromatography, High Pressure Liquid ,Kynurenine ,Helicobacter pylori ,biology ,Tryptophan ,Cancer ,Intestinal metaplasia ,General Chemistry ,Middle Aged ,medicine.disease ,biology.organism_classification ,030104 developmental biology ,Dysplasia ,Gastritis ,030220 oncology & carcinogenesis ,Disease Progression ,Female ,medicine.symptom ,Precancerous Conditions - Abstract
Gastric cancer (GC) is among the most common cancers worldwide. Gastric carcinogenesis is a multistep and multifactorial process beginning with chronic gastritis induced by Helicobacter pylori (H. pylori) infection. This process is often described via a sequence of events known as Correas's cascade, a stepwise progression from nonactive gastritis, chronic active gastritis, precursor lesions of gastric cancer (atrophy, intestinal metaplasia, and dysplasia), and finally adenocarcinoma. Our aim was to identify a plasma metabolic pattern characteristic of GC through disease progression within the Correa's cascade. This study involved the analysis of plasma samples collected from 143 patients classified in four groups: patients with nonactive gastritis and no H. pylori infection, H. pylori infected patients with chronic active gastritis, infected or noninfected patients with precursor lesions of gastric cancer, and GC. Independent partial least-squares-discriminant binary models of UPLC-ESI(+)-TOFMS metabolic profiles, implemented in a decision-directed acyclic graph, allowed the identification of tryptophan and kynurenine as discriminant metabolites that could be attributed to indoleamine-2,3-dioxygenase upregulation in cancer patients leading to tryptophan depletion and kynurenine metabolites generation. Furthermore, phenylacetylglutamine was also classified as a discriminant metabolite. Our data suggest the use of tryptophan, kynurenine, and phenylacetylglutamine as potential GC biomarkers.
- Published
- 2016
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