用于卵巢癌诊断的血小板蛋白质生物标志物组合
Platelet protein biomarker panel for ovarian cancer diagnosis.
作者信息
Lomnytska Marta, Pinto Rui, Becker Susanne, Engström Ulla, Gustafsson Sonja, Björklund Christina, Templin Markus, Bergstrand Jan, Xu Lei, Widengren Jerker, Epstein Elisabeth, Franzén Bo, Auer Gert
机构信息
1Department of Obstetrics and Gynaecology, Academical Uppsala University Hospital, Uppsala University, SE-751 85 Uppsala, Sweden.
2Institute of Women's and Children's Health, Karolinska Institute, SE-171 76 Stockholm, Sweden.
出版信息
Biomark Res. 2018 Jan 12;6:2. doi: 10.1186/s40364-018-0118-y. eCollection 2018.
BACKGROUND
Platelets support cancer growth and spread making platelet proteins candidates in the search for biomarkers.
METHODS
Two-dimensional (2D) gel electrophoresis, Partial Least Squares Discriminant Analysis (PLS-DA), Western blot, DigiWest.
RESULTS
PLS-DA of platelet protein expression in 2D gels suggested differences between the International Federation of Gynaecology and Obstetrics (FIGO) stages III-IV of ovarian cancer, compared to benign adnexal lesions with a sensitivity of 96% and a specificity of 88%. A PLS-DA-based model correctly predicted 7 out of 8 cases of FIGO stages I-II of ovarian cancer after verification by western blot. Receiver-operator curve (ROC) analysis indicated a sensitivity of 83% and specificity of 76% at cut-off >0.5 (area under the curve (AUC) = 0.831, < 0.0001) for detecting these cases. Validation on an independent set of samples by DigiWest with PLS-DA differentiated benign adnexal lesions and ovarian cancer, FIGO stages III-IV, with a sensitivity of 70% and a specificity of 83%.
CONCLUSION
We identified a group of platelet protein biomarker candidates that can quantify the differential expression between ovarian cancer cases as compared to benign adnexal lesions.
背景
血小板支持癌症的生长和扩散，这使得血小板蛋白成为寻找生物标志物的候选对象。
方法
二维（2D）凝胶电泳、偏最小二乘判别分析（PLS-DA）、蛋白质免疫印迹法、数字免疫印迹法。
结果
二维凝胶中血小板蛋白表达的PLS-DA分析表明，与良性附件病变相比，国际妇产科联合会（FIGO）III-IV期卵巢癌存在差异，灵敏度为96%，特异性为88%。经蛋白质免疫印迹法验证后，基于PLS-DA的模型正确预测了8例FIGO I-II期卵巢癌病例中的7例。受试者工作特征曲线（ROC）分析表明，在临界值>0.5时，检测这些病例的灵敏度为83%，特异性为76%（曲线下面积（AUC）=0.831，P<0.0001）。通过数字免疫印迹法对一组独立样本进行验证，结合PLS-DA可区分良性附件病变和FIGO III-IV期卵巢癌，灵敏度为70%，特异性为83%。
结论
我们鉴定出一组血小板蛋白生物标志物候选物，它们可以量化卵巢癌病例与良性附件病变之间的差异表达。
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