Objective: Since accurate biomarkers for the early diagnosis or individual prognosis of the bladder carcinoma are still not available, we used the ProteinChip technology, to search for discriminating protein expressions associated with this cancer and its subtypes.
Methods: A training set consisting of 30 archival urine samples from bladder carcinoma patients and 30 urinary samples from healthy volunteers, was analyzed via ProteinChip technology and computer based data mining. Mass clusters of differentially expressed proteins were verified by a second set (test set) comprising 21 bladder carcinoma urine samples and 21 non-tumor urinary samples. Expression differences between carcinoma subtype sample groups of the initial training set were assessed by a trend test.
Results: Bladder carcinoma was segregated from control with a sensitivity and specificity of 80% and 90 to 97% in the trainings set, as well as 52 to 57% and 57 to 62% in the test set, respectively. Segregation of pooled tumor stages pT2-pT3 from stages pT1 and pTa was possible at the 53.3 kDa cluster of the CM10-chip array data derived rule base.
Conclusion: ProteinChip technology together with adapted computer based data mining tools are useful for the rapid establishment of potential protein biomarkers.