Tree analysis of mass spectral urine profiles discriminates transitional cell carcinoma of the bladder from noncancer patient.
Clin Biochem
; 37(9): 772-9, 2004 Sep.
Article
em En
| MEDLINE
| ID: mdl-15329315
ABSTRACT
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.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Neoplasias da Bexiga Urinária
/
Árvores de Decisões
/
Carcinoma de Células de Transição
/
Espectrometria de Massas por Ionização e Dessorção a Laser Assistida por Matriz
Tipo de estudo:
Prognostic_studies
Limite:
Adult
/
Aged
/
Aged80
/
Humans
/
Middle aged
Idioma:
En
Ano de publicação:
2004
Tipo de documento:
Article