傳統的膀胱癌診斷主要依賴膀胱鏡檢查以及尿液細胞學檢查，但是膀胱鏡檢查是屬於侵入性的，而細胞學檢查的靈敏度很低，因此在膀胱癌的診斷上還需要新的檢驗方法。本篇研究主要是利用蛋白質體的分析方法，去尋找有可能作為膀胱癌診斷的生物指標。尿液樣本來自26個膀胱癌病人，為了避免在質譜分析的時候大量的蛋白質會抑制少量蛋白質的訊號，因此將樣本依據是否有蛋白尿分成兩組，並分別混合。接著再利用acetonitrile沈澱法和分子篩去除尿液中的基質，使用高效能液相層析電噴灑離子化質譜儀 (High performance liquid chromatography coupled with electrosprary ionization tandem mass spectrometry) 分析尿液蛋白質體。總共有146個蛋白質被鑑定到，使用生物資訊工具去篩選蛋白質名單，找到四個跟癌症相關的尿液蛋白質，polo-like kinase 2 (PLK2), prosaposin (PSAP), lysosomal-associated membrane protein 2 (LAMP2) 以及osteopontin (OPN)，有可能做為膀胱癌的腫瘤標誌。這四個候選者經過西方點墨法(Western blotting)確認在14個正常沒有泌尿道疾病和10個膀胱癌病人中蛋白質表現量的差異。在此結果中，四個蛋白質經過統計分析都沒有差異，然而若能增加樣本數目，在分析上可能可達到統計上的差異。

The traditional detections of bladder cancers are cytoscopy and urine cytology, but cytoscopy is invasive and urine cytology has low sensitivity. Thus, a new method for detecting bladder cancer is necessary. The objective of this study is to discover the potential biomarkers for bladder cancer diagnosis by proteomic approaches. The urine samples were collected from 26 bladder cancer patients. To avoid the interference from high abundant proteins in MS analysis, we separated urine samples with proteinuria from those without and pooled each group. Urine matrix was removed by acetonitrile precipitation followed by molecular weight cut-off. High performance liquid chromatography coupled with electrosprary ionization tandem mass spectrometry (HPLC-ESI-MS/MS) was used to analyze the urinary proteome. A total of 146 urinary proteins were identified. The protein list was screened by bioinformatic tools and four cancer-related urinary proteins, polo-like kinase 2 (PLK2), prosaposin (PSAP), lysosomal-associated membrane protein 2 (LAMP2) and osteopontin (OPN) were found to be potential tumor markers for bladder cancer. These four candidates have been validated by Western blotting using the urine samples from 14 normal subjects and 10 bladder cancer patients. After validation, the four proteins have no differences in statistical analyses. However, if we can increase the number subjects, the four proteins may be significant in statistical analysis.

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