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.