OBJECTIVETo develope a tree analysis pattern of mass spectral urine profiles to discriminate bladder transitional cell carcinoma (TCC) from non-cancer lesions using surface-enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF-MS) technology.

METHODSUrine samples from 61 bladder transitional cell carcinoma (TCCs) patients, 53 healthy volunteers and 42 patients with other urogenital diseases were analyzed using IMAC-Cu-3 ProteinChip. Proteomic spectra were generated by SELDI-TOF- MS. 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 which identified a fine-protein mass pattern that discriminated cancers from non-cancers effectively. A blinded test set including 38 cases was used to determine the sensitivity and specificity of the classification system.

RESULTSThe algorithm identified a cluster pattern that, in the training set, segregated cancer from non-cancer with a sensitivity of 84.8% and specificity of 91.7%. The discriminatory pattern was correctly identified. A sensitivity of 93.3% and a specificity of 87% for the blinded test were obtained when compared the TCC versus non-cancers.

CONCLUSIONSELDI-TOF-MS technology is a rapid, convenient and high-throughput analyzing method. The urine tree analysis proteomic pattern as a screening tool is effective for differential diagnosis of bladder cancer. More detailed studies are needed to further evaluate the clinical value of this pattern.