BACKGROUND: Currently available tests are insufficient to distinguish patients with epithelial ovarian cancer (EOC) from normal individuals. Metabolomics, a study of metabolic processes in biologic systems, has emerged as a key technology in the measurements of small molecular metabolites in tissues or biofluids. MATERIAL AND METHODS: To investigate the application of metabolomics on selecting EOC-associated biomarkers, 173 plasma specimens (80 newly diagnosed EOC patients and 93 normal individuals) were analyzed using ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC/QTOF/MS). A two-step strategy was performed to select EOC-associated biomarkers. The first step was to select potential biomarkers in distinguishing 42 cancer patients from 58 normal controls through partial least-squares discriminant analysis (PLS-DA) and database searching, and the second step was to validate the discrimination performance of these biomarkers in a dataset contained 38 EOCs and 35 controls. RESULTS: Eight candidate biomarkers were selected. The combination of these biomarkers resulted in the area of receiver operating characteristic curve (AUC) of 0.941, a sensitivity of 0.921, and a specificity of 0.886 at the best cut-off point for detecting EOC. DISCUSSION: Our findings suggested that sharp differences in metabolic profiles exist between EOC patients and normal controls. The identified eight metabolites associated with EOC may be served as novel biomarkers for diagnosis.
BACKGROUND: Currently available tests are insufficient to distinguish patients with epithelial ovarian cancer (EOC) from normal individuals. Metabolomics, a study of metabolic processes in biologic systems, has emerged as a key technology in the measurements of small molecular metabolites in tissues or biofluids. MATERIAL AND METHODS: To investigate the application of metabolomics on selecting EOC-associated biomarkers, 173 plasma specimens (80 newly diagnosed EOC patients and 93 normal individuals) were analyzed using ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC/QTOF/MS). A two-step strategy was performed to select EOC-associated biomarkers. The first step was to select potential biomarkers in distinguishing 42 cancerpatients from 58 normal controls through partial least-squares discriminant analysis (PLS-DA) and database searching, and the second step was to validate the discrimination performance of these biomarkers in a dataset contained 38 EOCs and 35 controls. RESULTS: Eight candidate biomarkers were selected. The combination of these biomarkers resulted in the area of receiver operating characteristic curve (AUC) of 0.941, a sensitivity of 0.921, and a specificity of 0.886 at the best cut-off point for detecting EOC. DISCUSSION: Our findings suggested that sharp differences in metabolic profiles exist between EOC patients and normal controls. The identified eight metabolites associated with EOC may be served as novel biomarkers for diagnosis.
Authors: David B Liesenfeld; Nina Habermann; Robert W Owen; Augustin Scalbert; Cornelia M Ulrich Journal: Cancer Epidemiol Biomarkers Prev Date: 2013-10-04 Impact factor: 4.254
Authors: Gunjal Garg; Ali Yilmaz; Praveen Kumar; Onur Turkoglu; David G Mutch; Matthew A Powell; Barry Rosen; Ray O Bahado-Singh; Stewart F Graham Journal: Metabolomics Date: 2018-11-24 Impact factor: 4.290
Authors: Fan Zhang; Yuanyuan Zhang; Chaofu Ke; Ang Li; Wenjie Wang; Kai Yang; Huijuan Liu; Hongyu Xie; Kui Deng; Weiwei Zhao; Chunyan Yang; Ge Lou; Yan Hou; Kang Li Journal: Metabolomics Date: 2018-04-26 Impact factor: 4.290
Authors: Oana A Zeleznik; Clary B Clish; Peter Kraft; Julian Avila-Pacheco; A Heather Eliassen; Shelley S Tworoger Journal: J Natl Cancer Inst Date: 2020-06-01 Impact factor: 13.506
Authors: Jeremy K Nicholson; Elaine Holmes; James M Kinross; Ara W Darzi; Zoltan Takats; John C Lindon Journal: Nature Date: 2012-11-15 Impact factor: 49.962