Department of Urology and Core-Unit Chip Application, University Hospital and Biocontrol Jena GmbH, Jena, Germany.
J Urol. 2011 Aug;186(2):713-20. doi: 10.1016/j.juro.2011.03.124. Epub 2011 Jun 17.
The prognosis in patients with metastasized bladder cancer is still poor. Clinical and histopathological parameters have limited ability to predict the risk of tumor progression. Thus, we identified specific protein patterns associated with tumor progression to differentiate specimens with and without metastasis.
We analyzed 46 metastasized and 42 nonmetastasized muscle invasive bladder cancers by ProteinChip® technology surface enhanced laser desorption/ionization time of flight mass spectrometry. Cell lysis was done after laser capture microdissection from cryostat sections to achieve high tumor cell purity. Surface enhanced laser desorption/ionization time of flight mass spectrometry was completed with 2 matrices (Q10 and CM10). Bioinformatic analysis was performed by XLMiner® clustering using the Fuzzy c-means method. Differentially expressed proteins were identified and verified by 2-dimensional gel electrophoresis, tryptic in gel digest, peptide mapping, immunodepletion assay and Western blot analysis.
By combining data on 2 chip surfaces (Q10 and CM10) results showed 86% sensitivity and 89% specificity in the training set, and 63% sensitivity and 88% specificity in the validation set. The relevant protein peaks 10.83, 14.68, 16.15 and 27.85 Da were identified as S100A8, MAP-1LC3, MUC-1S1 and GST-M1, respectively.
We defined specific protein patterns with ProteinChip technology using bioinformatic evaluation software, which allowed differentiation between nonmetastasized and metastasized bladder tumor samples with high sensitivity and specificity. We identified 4 differentially expressed proteins. Thus, it seems possible to identify patients at high metastasized risk even at a clinically localized stage, leading to individual therapy decisions.
转移性膀胱癌患者的预后仍然较差。临床和组织病理学参数预测肿瘤进展风险的能力有限。因此，我们确定了与肿瘤进展相关的特定蛋白质图谱，以区分有和无转移的标本。
我们通过 ProteinChip®技术表面增强激光解吸/电离飞行时间质谱分析了 46 例转移性和 42 例非转移性肌层浸润性膀胱癌。通过激光捕获显微切割从冷冻切片中进行细胞裂解，以获得高纯度的肿瘤细胞。采用 2 种基质（Q10 和 CM10）完成表面增强激光解吸/电离飞行时间质谱分析。生物信息学分析通过 XLMiner®聚类使用 Fuzzy c-means 方法进行。通过二维凝胶电泳、胶内酶切消化、肽图谱、免疫耗竭试验和 Western blot 分析鉴定和验证差异表达蛋白。
通过结合 2 个芯片表面的数据（Q10 和 CM10），在训练集中的敏感性为 86%，特异性为 89%，在验证集中的敏感性为 63%，特异性为 88%。相关蛋白峰 10.83、14.68、16.15 和 27.85 Da 分别被鉴定为 S100A8、MAP-1LC3、MUC-1S1 和 GST-M1。
我们使用生物信息学评估软件使用 ProteinChip 技术定义了特定的蛋白质图谱，该图谱允许以高灵敏度和特异性区分非转移性和转移性膀胱癌样本。我们鉴定了 4 种差异表达蛋白。因此，即使在临床局限性阶段，似乎也有可能识别出高转移风险的患者，从而做出个体化治疗决策。
