Identifying Serum Biomarkers for Ovarian Cancer by Screening With Surface-Enhanced Laser Desorption/Ionization Mass Spectrometry and the Artificial Neural Network. Issue 4 (1st May 2013)

• Record Type: Journal Article
• Authors: Yang, Jing; Zhu, Yanhui; Guo, Hongyan; Wang, Xiuyun; Gao, Ronglian; Zhang, Lufang; Zhao, Yangyu; Zhang, Xiaowei
• Is Part Of: International journal of gynecological cancer. Volume 23:Issue 4(2013)
• Page Start: 667
• Page End: 672
• Publication Date: 2013-05-01
• Subjects: Ovarian cancer -- Artificial neural network -- SELDI-TOF MS -- Proteomic model; Generative organs, Female -- Cancer -- Periodicals; 616.99465
• DOI: 10.1097/IGC.0b013e31827e1989
• Languages: English
• ISSNs: 1048-891X

Abstract

Abstract : Objective: The purpose of this study was to screen potential serum tumor biomarkers for the diagnosis of ovarian cancer. Methods: The study includes 3 sets. The first set of patients included 37 ovarian cancers and 31 healthy women (healthy controls). The second set included 42 ovarian cancers, 33 patients with benign ovarian tumor, and 29 healthy women (noncancer controls). The third set included 39 ovarian cancers and 35 patients with benign ovarian tumor (benign controls). Serum samples from ovarian cancers, healthy controls, noncancer controls, and benign controls were analyzed by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. Results: A 3-peak model (peaks of mass-to-charge ratio values at 5766.379 d, 5912.586 d, and 11695.56 d) was established in the training set that discriminated cancer from noncancer with high sensitivity (10/11, 90.90%) and specificity (19/20, 95.00%). The peaks corresponding to 3 potential biomarkers increased significantly with the degree of malignancy. Conclusions: The proteins represented by these 3 peaks are biomarker candidates for ovarian cancer diagnosis and/or monitoring treatment response.