AIMS: This review aims to identify metabolomic biomarkers of oesophago-gastric (OG) cancer in human biological samples, and to discuss the dominant metabolic pathways associated with the observed changes. METHODS: A systematic review of the literature, up to and including 9th November 2012, was conducted for experimental studies investigating the metabolomic profile of human biological samples from patients with OG cancer compared to a control group. Inclusion criteria for analytical platforms were mass spectrometry or nuclear magnetic resonance spectroscopy. The QUADAS-2 tool was used to assess the quality of the included studies. RESULTS: Twenty studies met the inclusion criteria and samples utilised for metabolomic analysis included tissue (n = 11), serum (n = 8), urine (n = 1) and gastric content (n = 1). Several metabolites of glycolysis, the tricarboxylic acid cycle, anaerobic respiration and protein/lipid metabolism were found to be significantly different between cancer and control samples. Lactate and fumurate were the most commonly recognised biomarkers of OG cancer related to cellular respiration. Valine, glutamine and glutamate were the most commonly identified amino acid biomarkers. Products of lipid metabolism including saturated and un-saturated free fatty acids, ketones and aldehydes and triacylglycerides were also identified as biomarkers of OG cancer. Unclear risk of bias for patient selection was reported for the majority of studies due to the lack of clarity regarding patient recruitment. CONCLUSION: The application of metabolomics for biomarker detection in OG cancer presents new opportunities for the purposes of screening and therapeutic monitoring. Future studies should provide clear details of patient selection and develop metabolite assays suitable for progress beyond phase 1 pre-clinical exploratory studies.
AIMS: This review aims to identify metabolomic biomarkers of oesophago-gastric (OG) cancer in human biological samples, and to discuss the dominant metabolic pathways associated with the observed changes. METHODS: A systematic review of the literature, up to and including 9th November 2012, was conducted for experimental studies investigating the metabolomic profile of human biological samples from patients with OG cancer compared to a control group. Inclusion criteria for analytical platforms were mass spectrometry or nuclear magnetic resonance spectroscopy. The QUADAS-2 tool was used to assess the quality of the included studies. RESULTS: Twenty studies met the inclusion criteria and samples utilised for metabolomic analysis included tissue (n = 11), serum (n = 8), urine (n = 1) and gastric content (n = 1). Several metabolites of glycolysis, the tricarboxylic acid cycle, anaerobic respiration and protein/lipid metabolism were found to be significantly different between cancer and control samples. Lactate and fumurate were the most commonly recognised biomarkers of OG cancer related to cellular respiration. Valine, glutamine and glutamate were the most commonly identified amino acid biomarkers. Products of lipid metabolism including saturated and un-saturated free fatty acids, ketones and aldehydes and triacylglycerides were also identified as biomarkers of OG cancer. Unclear risk of bias for patient selection was reported for the majority of studies due to the lack of clarity regarding patient recruitment. CONCLUSION: The application of metabolomics for biomarker detection in OG cancer presents new opportunities for the purposes of screening and therapeutic monitoring. Future studies should provide clear details of patient selection and develop metabolite assays suitable for progress beyond phase 1 pre-clinical exploratory studies.
Authors: Matthew F Buas; Haiwei Gu; Danijel Djukovic; Jiangjiang Zhu; Lynn Onstad; Brian J Reid; Daniel Raftery; Thomas L Vaughan Journal: Metabolomics Date: 2017-01-20 Impact factor: 4.290
Authors: Rachel S Kelly; Matthew G Vander Heiden; Edward Giovannucci; Lorelei A Mucci Journal: Cancer Epidemiol Biomarkers Prev Date: 2016-04-06 Impact factor: 4.254
Authors: Paul L Feingold; Deborah R Surman; Kate Brown; Yuan Xu; Lucas A McDuffie; Vivek Shukla; Emily S Reardon; Daniel R Crooks; Jane B Trepel; Sunmin Lee; Min-Jung Lee; Shaojian Gao; Sichuan Xi; Kaitlin C McLoughlin; Laurence P Diggs; David G Beer; Derek J Nancarrow; Leonard M Neckers; Jeremy L Davis; Chuong D Hoang; Jonathan M Hernandez; David S Schrump; R Taylor Ripley Journal: Mol Cancer Ther Date: 2018-06-22 Impact factor: 6.261