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Plasma sample based analysis of gastric cancer progression using targeted metabolomics.

ABSTRACT: Gastric carcinogenesis is a multifactorial process described as a stepwise progression from non-active gastritis (NAG), chronic active gastritis (CAG), precursor lesions of gastric cancer (PLGC) and gastric adenocarcinoma. Gastric cancer (GC) 5-year survival rate is highly dependent upon stage of disease at diagnosis, which is based on endoscopy, biopsy and pathological examinations. Non-invasive GC biomarkers would facilitate its diagnosis at early stages leading to improved GC prognosis. We analyzed plasma samples collected from 80 patients diagnosed with NAG without H. pylori infection (NAG-), CAG with H. pylori infection (CAG+), PLGC and GC. A panel of 208 metabolites including acylcarnitines, amino acids and biogenic amines, sphingolipids, glycerophospholipids, hexoses, and tryptophan and phenylalanine metabolites were quantified using two complementary quantitative approaches: Biocrates AbsoluteIDQ®p180 kit and a LC-MS method designed for the analysis of 29 tryptophan pathway and phenylalanine metabolites. Significantly altered metabolic profiles were found in GC patients that allowing discrimination from NAG-, CAG+ and PLGC patients. Pathway analysis showed significantly altered tryptophan and nitrogen metabolic pathways (FDR P?

SUBMITTER: Lario S

PROVIDER: S-EPMC5736578 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Plasma sample based analysis of gastric cancer progression using targeted metabolomics.

Lario Sergio S Ramírez-Lázaro Maria José MJ Sanjuan-Herráez Daniel D Brunet-Vega Anna A Pericay Carles C Gombau Lourdes L Junquera Félix F Quintás Guillermo G Calvet Xavier X

Scientific reports 20171219 1

Gastric carcinogenesis is a multifactorial process described as a stepwise progression from non-active gastritis (NAG), chronic active gastritis (CAG), precursor lesions of gastric cancer (PLGC) and gastric adenocarcinoma. Gastric cancer (GC) 5-year survival rate is highly dependent upon stage of disease at diagnosis, which is based on endoscopy, biopsy and pathological examinations. Non-invasive GC biomarkers would facilitate its diagnosis at early stages leading to improved GC prognosis. We an ...[more]

PMID: 29259332

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Project description:Rationale: Gastric cancer (GC) is preceded by a stepwise progression of precancerous gastric lesions. Distinguishing individuals with precancerous gastric lesions that have progression potential to GC is an important need. Perturbated lipid metabolism, particularly the dysregulation of de novo lipogenesis, is involved in gastric carcinogenesis. We conducted the first prospective lipidomics study exploring lipidomic signatures for the risk of gastric lesion progression and early GC. Methods: Our two-stage study of targeted lipidomics enrolled 400 subjects from the National Upper Gastrointestinal Cancer Early Detection Program in China, including 200 subjects of GC and different gastric lesions in the discovery and validation stages. Of validation stage, 152 cases with gastric lesions were prospectively followed for the progression of gastric lesions for a median follow-up of 580 days (interquartile range 390-806 days). We examined the lipidomic signatures associated with the risk of advanced gastric lesions and their progression to GC. Our published tissue proteomic data were referred to further investigate highlighted lipids with their biologically related protein expression in gastric mucosa. Results: We identified 11 plasma lipids significantly inversely associated with the risk of gastric lesion progression and GC occurrence. These lipids were integrated as latent profiles to identify 5 clusters of lipid expression that had distinct risk of gastric lesion progression. The latent profiles significantly improved the ability to predict the progression potential of gastric lesions (AUC: 0.82 vs 0.68, Delong's P = 4.6×10-4) and risk of early GC (AUC: 0.81 vs 0.55, P = 6.3×10-5). Significant associations were found between highlighted lipids, their biologically correlated proteins and the risk of GC, supporting the role of the pathways involving monocarboxylic acid metabolism and lipid transport and catabolic process in GC. Conclusions: Our study revealed the lipidomic signatures associated with the risk of gastric lesion progression and GC occurrence, exhibiting translational implications for GC prevention.

Project description:The lack of effective screening strategies for high-grade serous carcinoma (HGSC), a subtype of ovarian cancer (OC) responsible for 70-80% of OC related deaths, emphasizes the need for new diagnostic markers and a better understanding of disease pathogenesis. Capillary electrophoresis (CE) coupled with high-resolution mass spectrometry (HRMS) offers high selectivity and sensitivity for ionic compounds, thereby enhancing biomarker discovery. Recent advances in CE-MS include small, chip-based CE systems coupled with nanoelectrospray ionization (nanoESI) to provide rapid, high-resolution analysis of biological specimens. Here, we describe the development of a targeted microchip (µ) CE-HRMS method, with an acquisition time of only 3 min and sample injection volume of 4nL, to analyze 40 target metabolites in serum samples from a triple-mutant (TKO) mouse model of HGSC. Extracted ion electropherograms showed sharp, baseline resolved peak shapes, even for structural isomers such as leucine and isoleucine. All calibration curves of the analytes maintained good linearity with an average R2 of 0.994, while detection limits were in the nM range. Thirty metabolites were detected in mouse serum with recoveries ranging from 78 to 120%, indicating minimal ionization suppression and good accuracy. We applied the µCE-HRMS method to biweekly-collected serum samples from TKO and TKO control mice. A time-resolved analysis revealed characteristic temporal trends for amino acids, nucleosides, and amino acid derivatives. These metabolic alterations are indicative of altered nucleotide biosynthesis and amino acid metabolism in HGSC development and progression. A comparison of the µCE-HRMS dataset with non-targeted ultra-high performance liquid chromatography (UHPLC)-MS results showed identical temporal trends for the five metabolites detected with both platforms, indicating the µCE-HRMS method performed satisfactorily in terms of capturing metabolic reprogramming due to HGSC progression while reducing the total data collection time three-fold.

Dataset Information

Plasma sample based analysis of gastric cancer progression using targeted metabolomics.

Publications

Plasma sample based analysis of gastric cancer progression using targeted metabolomics.

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