Abstract

PURPOSE:

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.

MATERIALS AND METHODS:

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.

RESULTS:

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.

CONCLUSIONS:

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.

Copyright © 2011 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.