A proteome resource of ovarian cancer ascites: integrated proteomic and bioinformatic analyses to identify putative biomarkers. Export

@article{citeulike:3033572, abstract = {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.}, address = {Ontario Cancer Institute, Division of Cancer Genomics and Proteomics, Canada.}, author = {Gortzak-Uzan, L. and Ignatchenko, A. and Evangelou, A. I. and Agochiya, M. and Brown, K. A. and St Onge, P. and Kireeva, I. and Schmitt-Ulms, G. and Brown, T. J. and Murphy, J. and Rosen, B. and Shaw, P. and Jurisica, I. and Kislinger, T.}, citeulike-article-id = {3033572}, citeulike-linkout-0 = {http://dx.doi.org/10.1021/pr0703223}, citeulike-linkout-1 = {http://pubs.acs.org/doi/abs/10.1021/pr0703223}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/18076136}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=18076136}, doi = {10.1021/pr0703223}, issn = {1535-3893}, journal = {Journal of proteome research}, keywords = {ascites, biomarker, ismb2008, microarray, ovarian-cancer, proteomics}, month = {January}, number = {1}, pages = {339--351}, posted-at = {2008-07-22 21:08:57}, priority = {2}, title = {A proteome resource of ovarian cancer ascites: integrated proteomic and bioinformatic analyses to identify putative biomarkers.}, url = {http://dx.doi.org/10.1021/pr0703223}, volume = {7}, year = {2008} }

TY - JOUR ID - citeulike:3033572 L3 - citeulike-article-id:3033572 N2 - 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. IS - 1 JF - Journal of proteome research SN - 1535-3893 AD - Ontario Cancer Institute, Division of Cancer Genomics and Proteomics, Canada. EP - 351 TI - A proteome resource of ovarian cancer ascites: integrated proteomic and bioinformatic analyses to identify putative biomarkers. VL - 7 SP - 339 KW - ascites KW - biomarker KW - ismb2008 KW - microarray KW - ovarian-cancer KW - proteomics AU - Gortzak-Uzan, L AU - Ignatchenko, A AU - Evangelou, AI AU - Agochiya, M AU - Brown, KA AU - St Onge, P AU - Kireeva, I AU - Schmitt-Ulms, G AU - Brown, TJ AU - Murphy, J AU - Rosen, B AU - Shaw, P AU - Jurisica, I AU - Kislinger, T PY - 2008/01// UR - http://dx.doi.org/10.1021/pr0703223 ER -