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Updated: Aug 19, 2026
3-D Cell Culture System for Studying Invasion and Evaluating Therapeutics in Bladder Cancer
Published on: September 13, 2018
Using tree analysis pattern and SELDI-TOF-MS to discriminate transitional cell carcinoma of the bladder cancer from
Weiwei Liu1, Ming Guan, Denglong Wu
1Center of Laboratory Medicine, Huashan Hospital, Fudan University, Shanghai 200040, P.R. China.
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.
