title: [Tree analysis pattern of mass spectral urine profiles in differential diagnosis of bladder transitional cell carcinoma].
abstract: OBJECTIVE
  To 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.
  
  
  METHODS
  Urine 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.
  
  
  RESULTS
  The 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.
  
  
  CONCLUSIONS
  SELDI-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.
authors.affiliation: Department of Urology, Shanghai 6th People's Hospital, Shanghai Jiatong University, Shanghai 200233, China. wudenglong@163.com
authors.forename: Deng-long
authors.initials: DL
authors.lastname: Wu
authors.forename: Yuan-fang
authors.initials: YF
authors.lastname: Zhang
authors.forename: Ming
authors.initials: M
authors.lastname: Guan
authors.forename: Wei-wei
authors.initials: WW
authors.lastname: Liu
authors.forename: Yue-min
authors.initials: YM
authors.lastname: Xu
authors.forename: San-bao
authors.initials: SB
authors.lastname: Jin
authors.forename: Jiong
authors.initials: J
authors.lastname: Zhang
authors.forename: Chong-rui
authors.initials: CR
authors.lastname: Jin
authors.forename: Yuan
authors.initials: Y
authors.lastname: Lü
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002295:Carcinoma, Transitional Cell / Q000175:diagnosis / Q000652:urine*; D003556:Cystitis / Q000175:diagnosis / Q000652:urine; D003663:Decision Trees; D003937:Diagnosis, Differential; D006801:Humans; D008297:Male; D008875:Middle Aged; D011470:Prostatic Hyperplasia / Q000175:diagnosis / Q000652:urine; D040081:Protein Array Analysis; D040901:Proteomics / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods; D001749:Urinary Bladder Neoplasms / Q000175:diagnosis / Q000652:urine*