Tree analysis of mass spectral urine profiles discriminates transitional cell carcinoma of the bladder from noncancer patient.
BACKGROUND
Recent advances in proteomic profiling technologies, such as surface-enhanced laser desorption/ionization mass spectrometry (SELDI), have allowed preliminary profiling and identification of tumor markers in biological fluids in several cancer types and establishment of clinically useful diagnostic computational models. We developed a bioinformatics tool and used it to identify proteomic patterns in urine that distinguish transitional cell carcinoma (TCC) from noncancer.


METHODS
Proteomic spectra were generated by mass spectroscopy (surface-enhanced laser desorption and ionization). A preliminary "training" set of spectra derived from analysis of urine from 46 TCC patients, 32 patients with benign urogenital diseases (BUD), and 40 age-matched unaffected healthy men were used to train and develop a decision tree classification algorithm that identified a fine-protein mass pattern that discriminated cancer from noncancer effectively. A blinded test set, including 38 new cases, was used to determine the sensitivity and specificity of the classification system.


RESULTS
The algorithm identified a cluster pattern that, in the training set, segregated cancer from noncancer with sensitivity of 84.8% and specificity of 91.7%. The discriminatory pattern correctly identified. A sensitivity of 93.3% and a specificity of 87.0% for the blinded test were obtained when comparing the TCC vs. noncancer.


CONCLUSIONS
These findings justify a prospective population-based assessment of proteomic pattern technology as a screening tool for bladder cancer in high-risk and general populations.
Zhang
Yuan-Fang
YF
Department of Urology, Huashan Hospital, Fudan University, Shanghai 200040, PR China.
Wu
Deng-Long
DL
Guan
Ming
M
Liu
Wei-Wei
WW
Wu
Zhong
Z
Chen
Yu-Ming
YM
Zhang
Wan-Zhong
WZ
Lu
Yuan
Y
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002295:Carcinoma, Transitional Cell / Q000652:urine*; D003663:Decision Trees; D006801:Humans; D008875:Middle Aged; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*; D001749:Urinary Bladder Neoplasms / Q000652:urine*