title: Plasma proteomic pattern as biomarkers for ovarian cancer.
abstract: Early detection of ovarian cancer remains a challenge. Pathologic changes within an organ might be reflected in proteomic patterns in serum or plasma. The objective of this study was to identify new plasma biomarkers in ovarian cancer patients using mass spectrometry (MS) protein profiling and artificial intelligence. The study included 35 women with ovarian cancer and 30 age-matched disease-free controls. For plasma protein signature analysis, the protein chip array surface-enhanced laser desorption/ionization (SELDI) analysis was performed. The strong anion exchange (SAX) and weak cation exchange (WCX) chips were used for analysis. After a training analysis by SAX and WCX protein chips, learning algorithm and clustering analysis was performed to reach a discriminate pattern of protein signature. SELDI mass spectroscopy was highly reproducible in detecting ovarian tumor-specific protein profiles. Four specific protein peaks were identified in plasma of women with ovarian cancer, but not in controls, with relative molecular masses of 6190.48, 5147.06, 11522.6, and 11537.7 d. Two peaks, with Mr 5295.5 and 8780.48 d, were present in plasma of control but not in women with ovarian cancer. A sensitivity of 90-96.3% and specificity of 100% for this studied cases and controls were reached. This study clearly demonstrates that the combined technology of SELDI-MS and artificial intelligence is effective in distinguishing protein expression between normal and ovary cancer plasma. The identified gained and lost protein peaks in plasma may provide as candidate proteins to be used for the detection or monitoring ovarian cancer.
authors.affiliation: Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei, Taiwan, Republic of China.
authors.forename: Y-W
authors.initials: YW
authors.lastname: Lin
authors.forename: C-Y
authors.initials: CY
authors.lastname: Lin
authors.forename: H-C
authors.initials: HC
authors.lastname: Lai
authors.forename: J-Y
authors.initials: JY
authors.lastname: Chiou
authors.forename: C-C
authors.initials: CC
authors.lastname: Chang
authors.forename: M-H
authors.initials: MH
authors.lastname: Yu
authors.forename: T-Y
authors.initials: TY
authors.lastname: Chu
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000032:analysis*; D001798:Blood Proteins / Q000032:analysis*; D003663:Decision Trees; D005260:Female; D006801:Humans; D008875:Middle Aged; D008954:Models, Biological; D009363:Neoplasm Proteins / Q000032:analysis*; D009375:Neoplasms, Glandular and Epithelial / Q000097:blood / Q000175:diagnosis*; D010051:Ovarian Neoplasms / Q000097:blood / Q000175:diagnosis*; D040081:Protein Array Analysis; D040901:Proteomics / Q000379:methods; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
chemical_list: D014408:Biomarkers, Tumor; D001798:Blood Proteins; D009363:Neoplasm Proteins