title: Metabolomic study for diagnostic model of oesophageal cancer using gas chromatography/mass spectrometry.
abstract: The prognosis for oesophageal cancer is poor. Attempts have been made for the identification of biomarkers for early diagnosis. Metabolomic panel has been evaluated as potential candidate biomarkers. With gas chromatography/mass spectrometry (GC/MS) as a sensitive modality for metabolomics, various tissue metabolites can be detected and identified. We hypothesized that tissue metabolomic biomarkers may be identifiable and diagnostically useful for oesophageal cancer. We present a metabolomic method of chemical derivatization followed by GC/MS to analyze the metabolic difference in biopsied specimens between oesophageal cancer and corresponding normal mucosae obtained from 20 oesophageal cancer patients. The GC/MS data was analyzed using a two sample t-test to explore the potential metabolic biomarkers for oesophageal cancer. A diagnostic model was constructed to discriminate normal from malignant samples, using principal component analysis (PCA) and receiver-operating characteristic (ROC) curves. t-Test showed a total of 20 marker metabolites detected were found to be different with statistical significance (P<0.05). The multivariate logistic analysis yielded a complete distinction between the two groups. The diagnostic model could discriminate tumors from normal mucosae with an area under the curve (AUC) value of 1. Our findings suggest that this assay may potentially provide a new metabolomic biomarker for oesophageal cancer.
authors.affiliation: Department of Gastroenterology, Zhongshan Hospital, Shanghai Medical College, Fudan University, Fenglin Road 180, Shanghai 200032, China.
authors.forename: Hao
authors.initials: H
authors.lastname: Wu
authors.forename: Ruyi
authors.initials: R
authors.lastname: Xue
authors.forename: Chunlai
authors.initials: C
authors.lastname: Lu
authors.forename: Chunhui
authors.initials: C
authors.lastname: Deng
authors.forename: Taotao
authors.initials: T
authors.lastname: Liu
authors.forename: Huazong
authors.initials: H
authors.lastname: Zeng
authors.forename: Qun
authors.initials: Q
authors.lastname: Wang
authors.forename: Xizhong
authors.initials: X
authors.lastname: Shen
mesh_terms: D000328:Adult; D000368:Aged; D019540:Area Under Curve; D014408:Biomarkers, Tumor / Q000032:analysis* / Q000378:metabolism; D004938:Esophageal Neoplasms / Q000737:chemistry / Q000175:diagnosis / Q000378:metabolism*; D005260:Female; D008401:Gas Chromatography-Mass Spectrometry / Q000379:methods*; D006801:Humans; D008297:Male; D055432:Metabolomics / Q000379:methods*; D008875:Middle Aged; D009092:Mucous Membrane / Q000378:metabolism; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D012372:ROC Curve; D015203:Reproducibility of Results
chemical_list: D014408:Biomarkers, Tumor