Protein Profiling in Urine for the Diagnosis of Bladder Cancer

[摘要] At present, the most reliable means of diagnosis and surveillance of bladder cancer are cystoscopic examination and bladder biopsy for histologic confirmation. The invasive and labor-intensive nature of this procedure underscores the need to develop better, less costly, and nonsurgical diagnostic tools (1)(2). Use of surface-enhanced laser desorption/ionization (SELDI) time-of-flight mass spectrometry has been successful in facilitating protein profiling of complex biological mixtures. This technology uses chemical affinity platforms to capture protein molecules from various biological sources. Retained proteins are subsequently analyzed by mass spectrometry [reviewed in Ref. ((3))]. Recent reports provide evidence that analysis of SELDI data by “learning” algorithms can lead to the identification of serum protein “fingerprints” for prostate, ovarian, and breast cancers (4)(5)(6)(7)(8)(9) and urinary fingerprints for kidney cancer (10). We recently reported the application of the SELDI system for detection of potential bladder cancer-associated biomarkers in urine (11). In this earlier study, we showed that combination of five transitional cell carcinoma (TCC)-associated protein peaks by simple statistical methods provided 87% sensitivity and 66% specificity in disease detection. The objectives of the current study were ( a ) to evaluate a commercial available data-mining classification algorithm for the analysis of the SELDI mass spectral data, and ( b ) assessing the clinical utility of this assay in detecting bladder cancer from a geographically and clinically mixed population.Fresh spot-voided urine specimens from 230 individuals were included in the study. Specimens were collected from patients …

[学科分类] 过敏症与临床免疫学