Urinary Metabolomic Analysis of Human Gastric Cancer Mouse Models and Patients Using Gas Chromatography/Mass SpectrometryJin-Lian Chen1,2*, Jing Fan2, Hui-Qing Tang3, Jun-Duo Hu2 and Jian-Zhong Gu3
- *Corresponding Author:
- Jin-Lian Chen, MD, PhD
Department of Gastroenterology, Shanghai East Hospital
Tongji University, Shanghai 200120, China
E-mail: [email protected]
Received date: August 23, 2011; Accepted date: September 28, 2011; Published date: December 30, 2011
Citation: Chen JL, Fan J, Tang HQ, Hu JD, Gu JZ (2011) Urinary Metabolomic Analysis of Human Gastric Cancer Mouse Models and Patients Using Gas Chromatography/Mass Spectrometry. J Mol Biomark Diagn S2:003. doi:10.4172/2155-9929.S2-003
Copyright: © 2011 Chen JL, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Gastric cancer is the second cause of cancer deaths in China. To identify potential markers for screening or diagnosis of gastric cancer, we coupled xenotransplantation mouse models with a urine metabolomic approach. SGC-7901 gastric cancer cells were subcutaneously or orthotopically implanted into nude mice to establish metastasis and non-metastasis mouse models. Urine samples from mice bearing tumors or gastric cancer patients and their healthy controls were collected and subjected to gas chromatography and mass spectrometry (GC/MS) analysis. Metabolic data were analyzed using Mann-Whitney test to find urinary biomarkers for gastric cancer. Diagnostic models for gastric cancer mice and patients were constructed using principal components analysis (PCA) and validated with the area under the curve (AUC) of receiver operating characteristic (ROC) curves. The results indicated these metabolites mainly include lactic acid, serine, proline, malic acid, and fatty acids. The PCA models discriminated all gastric cancer mice or most gastric cancer patients including six of seven early stage patients, from their healthy controls with AUC value of 1.0 or 0.996, respectively. In addition, they were able to differentiate between metastatic and non-metastatic mice with AUC value of 1.0, as well as between invasive/metastatic and non-invasive cancers with AUC value of 0.982. Our data suggest that there are significant metabolic alterations during progression of gastric cancer and the potential metabolic biomarkers could be useful for screening and early diagnosis of gastric cancer progression.