Using tree analysis pattern and SELDI-TOF-MS to discriminate transitional cell carcinoma of the bladder cancer from noncancer patients.

OBJECTIVE: To determine whether SELDI protein profiling of urine coupled with a tree analysis pattern could differentiate TCC from noncancer patients.
METHODS: The ProteinChip Arrays were performed on a ProteinChip PBS II reader of the ProteinChip Biomarker System. The study was divided into two phases: a preliminary phase with construction of tree analysis pattern, and a testing phase with test urine samples. Generation of the tree analysis pattern was performed by a training data set consisting of 104 samples. The validity of the tree analysis pattern was then challenged with a test set of 68 samples.
RESULTS: Average of 187 mass peaks was detected in the urine samples, and five of these peaks were used to construct the tree analysis pattern. The classification pattern correctly predicted 91.67-94.64% of the samples for both of the two groups in the training set, for an overall correct classification of about 93%. The pattern correctly predicted 72.0% (49 of 68) of the test samples, with 71.4% (25 of 35) of the TCC samples, 72.7% (24 of 33) of the noncancer samples.
CONCLUSIONS: The high sensitivity and specificity obtained by the urine protein profiling approach demonstrate that SELDI-TOF-MS combined with a tree analysis pattern can both facilitate discriminate TCC bladder cancer with noncancer and provide an innovative clinical diagnostic platform improve the detection of TCC bladder cancer patients.