title: Identification of metabolic biomarkers to diagnose epithelial ovarian cancer using a UPLC/QTOF/MS platform.
abstract: 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.
  
  
  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.
  
  
  CONCLUSIONS
  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.affiliation: Department of Epidemiology and Biostatistics, Harbin Medical University, China.
authors.forename: Lijun
authors.initials: L
authors.lastname: Fan
authors.forename: Wang
authors.initials: W
authors.lastname: Zhang
authors.forename: Mingzhu
authors.initials: M
authors.lastname: Yin
authors.forename: Tao
authors.initials: T
authors.lastname: Zhang
authors.forename: Xiaoyan
authors.initials: X
authors.lastname: Wu
authors.forename: Haiyu
authors.initials: H
authors.lastname: Zhang
authors.forename: Meng
authors.initials: M
authors.lastname: Sun
authors.forename: Zhenzi
authors.initials: Z
authors.lastname: Li
authors.forename: Yan
authors.initials: Y
authors.lastname: Hou
authors.forename: Xiaohua
authors.initials: X
authors.lastname: Zhou
authors.forename: Ge
authors.initials: G
authors.lastname: Lou
authors.forename: Kang
authors.initials: K
authors.lastname: Li
mesh_terms: D002288:Adenocarcinoma, Mucinous / Q000175:diagnosis* / Q000378:metabolism; D000328:Adult; D000368:Aged; D019540:Area Under Curve; D014408:Biomarkers, Tumor / Q000378:metabolism*; D016022:Case-Control Studies; D002853:Chromatography, Liquid; D018284:Cystadenocarcinoma, Serous / Q000175:diagnosis* / Q000378:metabolism; D016002:Discriminant Analysis; D016889:Endometrial Neoplasms / Q000175:diagnosis* / Q000378:metabolism; D005260:Female; D006801:Humans; D016018:Least-Squares Analysis; D055432:Metabolomics; D008875:Middle Aged; D060787:Neoplasm Grading; D009367:Neoplasm Staging; D010051:Ovarian Neoplasms / Q000175:diagnosis* / Q000378:metabolism; D011379:Prognosis; D012372:ROC Curve; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; D053719:Tandem Mass Spectrometry; D055815:Young Adult
chemical_list: D014408:Biomarkers, Tumor