Gortzak-Uzan Limor, Ignatchenko Alex, Evangelou Andreas I, Agochiya Mahima, Brown Kevin A, St Onge Peter, Kireeva Inga, Schmitt-Ulms Gerold, Brown Theodore J, Murphy Joan, Rosen Barry, Shaw Patricia, Jurisica Igor, Kislinger Thomas
Ontario Cancer Institute, Division of Cancer Genomics and Proteomics, Canada.
J Proteome Res. 2008 Jan;7(1):339-51. doi: 10.1021/pr0703223. Epub 2007 Dec 13.
Epithelial ovarian cancer is the most lethal gynecological malignancy, and disease-specific biomarkers are urgently needed to improve diagnosis, prognosis, and to predict and monitor treatment efficiency. We present an in-depth proteomic analysis of selected biochemical fractions of human ovarian cancer ascites, resulting in the stringent and confident identification of over 2500 proteins. Rigorous filter schemes were applied to objectively minimize the number of false-positive identifications, and we only report proteins with substantial peptide evidence. Integrated computational analysis of the ascites proteome combined with several recently published proteomic data sets of human plasma, urine, 59 ovarian cancer related microarray data sets, and protein-protein interactions from the Interologous Interaction Database I (2)D ( http://ophid.utoronto.ca/i2d) resulted in a short-list of 80 putative biomarkers. The presented proteomics analysis provides a significant resource for ovarian cancer research, and a framework for biomarker discovery.
上皮性卵巢癌是最致命的妇科恶性肿瘤，迫切需要疾病特异性生物标志物来改善诊断、预后，并预测和监测治疗效果。我们对人卵巢癌腹水的选定生化组分进行了深入的蛋白质组学分析，严格且可靠地鉴定出了2500多种蛋白质。应用了严格的筛选方案以客观地减少假阳性鉴定的数量，并且我们只报告有大量肽段证据的蛋白质。对腹水蛋白质组进行综合计算分析，并结合最近发表的几个人血浆、尿液蛋白质组数据集、59个卵巢癌相关微阵列数据集以及来自同源相互作用数据库I（2）D（http://ophid.utoronto.ca/i2d）的蛋白质-蛋白质相互作用，得出了一份包含80种假定生物标志物的简短清单。所呈现的蛋白质组学分析为卵巢癌研究提供了重要资源，以及生物标志物发现的框架。
