PURPOSE: The prognosis in @entity1 with metastasized @entity150 is still poor. Clinical and histopathological parameters have limited ability to predict the risk of @entity5 progression. Thus, we identified specific protein patterns associated with @entity5 progression to differentiate specimens with and without @entity3 . MATERIALS AND METHODS: We analyzed 46 metastasized and 42 nonmetastasized muscle invasive @entity150 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 @entity5 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 @entity2757 , MAP-1LC3, MUC-1S1 and @entity9164 , respectively. CONCLUSIONS: We defined specific protein patterns with ProteinChip technology using bioinformatic evaluation software, which allowed differentiation between nonmetastasized and metastasized @entity150 samples with high sensitivity and specificity. We identified 4 differentially expressed proteins. Thus, it seems possible to identify @entity1 at high metastasized risk even at a clinically localized stage, leading to individual therapy decisions.

Specific protein patterns characterize metastatic potential of advanced XXXX .

@entity1 :: ('9606', 'Species') :: ['patients'] @entity5 :: ('MESH:D009369', 'Disease') :: ['tumor'] @entity150 :: ('MESH:D001749', 'Disease') :: ['bladder tumor', 'bladder cancers', 'bladder cancer'] @entity3 :: ('MESH:D009362', 'Disease') :: ['metastasis'] @entity2757 :: ('6279', 'Gene') :: ['S100A8'] @entity9164 :: ('2944', 'Gene') :: ['GST-M1']
@entity150 :: (MESH:D001749,Disease) :: ['bladder cancer']
