Neuhaus J, Schiffer E, Siwy J, Mannello F, Horn L-C, Stolzenburg J-U
Department für Operative Medizin, Klinik und Poliklinik für Urologie, Universitätsklinikum Leipzig AöR, Liebigstraße 20, 04103, Leipzig, Deutschland,
Urologe A. 2013 Sep;52(9):1251-5. doi: 10.1007/s00120-013-3308-0.
Due to comprehensive PSA screening, the incidence for prostate cancer (PCa) is rising. Therefore, there is an urgent need for improved PCa diagnostics and prognostic tools to differentiate between insignificant and aggressive, fast growing tumors.
With the proteome-based method presented here, we were able to distinguish PCa from BPH, chronic prostatitis and healthy controls with 83 % sensitivity and 67 % specificity. Furthermore, the methods discerned advanced PCa from local, organ-confined PCa in a group of patients with gleason score 7 (80 % sensitivity, 82 % specificity).
Our proteomic approach is based on the analysis of low molecular weight polypeptides, identified as the endpoint of the naturally occuring liquefaction cascade in seminal plasma. For the first time using seminal plasma as a source, we analysed a complex network of interacting proteases and specific inhibitors, reflecting tumor biology specificity. Our diagnostic and prognostic tool is robust and easy to handle, and therefore it is well suitable for the laboratory and medical practice.
由于全面的前列腺特异性抗原（PSA）筛查，前列腺癌（PCa）的发病率正在上升。因此，迫切需要改进PCa的诊断和预后工具，以区分无意义的和侵袭性的、快速生长的肿瘤。
采用本文介绍的基于蛋白质组的方法，我们能够以83%的灵敏度和67%的特异性将PCa与良性前列腺增生（BPH）、慢性前列腺炎及健康对照区分开来。此外，该方法在一组Gleason评分为7分的患者中，能够从局限性、器官局限性PCa中辨别出晚期PCa（灵敏度80%，特异性82%）。
我们的蛋白质组学方法基于对低分子量多肽的分析，这些多肽被确定为精浆中自然发生的液化级联反应的终产物。首次以精浆为来源，我们分析了一个由相互作用的蛋白酶和特异性抑制剂组成的复杂网络，反映了肿瘤生物学特异性。我们的诊断和预后工具稳健且易于操作，因此非常适合实验室和医学实践。
