Ma Yingying, Sun Zeyu, de Matos Ricardo, Zhang Jing, Odunsi Kunle, Lin Biaoyang
1 System Biology Division, Zhejiang-California International Nanosystem Institute (ZCNI), Zhejiang University , Hangzhou, China .
OMICS. 2014 May;18(5):280-97. doi: 10.1089/omi.2013.0164. Epub 2014 Mar 24.
Epithelial ovarian cancer is the most deadly gynecological cancer around the world, with high morbidity in industrialized countries. Early diagnosis is key in reducing its morbidity rate. Yet, robust biomarkers, diagnostics, and animal models are still limited for ovarian cancer. This calls for broader omics and systems science oriented diagnostics strategies. In this vein, the domestic chicken has been used as an ovarian cancer animal model, owing to its high rate of developing spontaneous epithelial ovarian tumors. Chicken blood has thus been considered a surrogate reservoir from which cancer biomarkers can be identified. However, the presence of highly abundant proteins in chicken blood has compromised the applicability of proteomics tools to study chicken blood owing to a lack of immunodepletion methods. Here, we demonstrate that a combinatorial peptide ligand library (CPLL) can efficiently remove highly abundant proteins from chicken blood samples, consequently doubling the number of identified proteins. Using an integrated CPLL-1DGE-LC-MSMS workflow, we identified a catalog of 264 unique proteins. Functional analyses further suggested that most proteins were coagulation and complement factors, blood transport and binding proteins, immune- and defense-related proteins, proteases, protease inhibitors, cellular enzymes, or cell structure and adhesion proteins. Semiquantitative spectral counting analysis identified 10 potential biomarkers from the present chicken ovarian cancer model. Additionally, many human homologs of chicken blood proteins we have identified have been independently suggested as diagnostic biomarkers for ovarian cancer, further triangulating our novel observations reported here. In conclusion, the CPLL-assisted proteomic workflow using the chicken ovarian cancer model provides a feasible platform for translational research to identify ovarian cancer biomarkers and understand ovarian cancer biology. To the best of our knowledge, we report here the most comprehensive survey of the chicken blood proteome to date.
上皮性卵巢癌是全球最致命的妇科癌症,在工业化国家发病率很高。早期诊断是降低其发病率的关键。然而,用于卵巢癌的强大生物标志物、诊断方法和动物模型仍然有限。这就需要更广泛的以组学和系统科学为导向的诊断策略。有鉴于此,家鸡因其自发发生上皮性卵巢肿瘤的比率高,已被用作卵巢癌动物模型。因此,鸡血被认为是一个替代来源,可以从中识别癌症生物标志物。然而,由于缺乏免疫去除方法,鸡血中高丰度蛋白质的存在影响了蛋白质组学工具在研究鸡血中的适用性。在这里,我们证明了组合肽配体库(CPLL)可以有效地从鸡血样本中去除高丰度蛋白质,从而使鉴定出的蛋白质数量增加一倍。使用集成的CPLL-1DGE-LC-MSMS工作流程,我们鉴定出了264种独特蛋白质的目录。功能分析进一步表明,大多数蛋白质是凝血因子和补体因子、血液运输和结合蛋白、免疫和防御相关蛋白、蛋白酶、蛋白酶抑制剂、细胞酶或细胞结构和粘附蛋白。半定量光谱计数分析从当前的鸡卵巢癌模型中鉴定出10种潜在生物标志物。此外,我们鉴定出的许多鸡血蛋白的人类同源物已被独立地建议作为卵巢癌的诊断生物标志物,这进一步证实了我们在此报告的新观察结果。总之,使用鸡卵巢癌模型的CPLL辅助蛋白质组学工作流程为转化研究提供了一个可行的平台,以识别卵巢癌生物标志物并了解卵巢癌生物学。据我们所知,我们在此报告了迄今为止对鸡血蛋白质组最全面的调查。