Urinary biomarker profiling in transitional cell carcinoma
| Autor: | Nicholas P. Munro, Margaret A. Knowles, Rosamonde E. Banks, Douglas Thompson, Patricia Harnden, Paul Clarke, Mark A. Rogers, Anthea J. Stanley, Jennifer H. Barrett, Ian Eardley, David A Cairns |
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| Rok vydání: | 2006 |
| Předmět: |
Adult
Male Oncology alpha-Defensins Cancer Research medicine.medical_specialty Pathology Urinary system Hemoglobinuria urologic and male genital diseases Internal medicine Biomarkers Tumor medicine Carcinoma Humans Multiplex Anion Exchange Resins Aged Tumor marker Aged 80 and over Carcinoma Transitional Cell Bladder cancer business.industry Confounding Middle Aged Chromatography Ion Exchange medicine.disease female genital diseases and pregnancy complications Transitional cell carcinoma Urinary Bladder Neoplasms Biomarker (medicine) Female business |
| Zdroj: | International Journal of Cancer. 119:2642-2650 |
| ISSN: | 1097-0215 0020-7136 |
| DOI: | 10.1002/ijc.22238 |
| Popis: | Urinary biomarkers or profiles that allow noninvasive detection of recurrent transitional cell carcinoma (TCC) of the bladder are urgently needed. We obtained duplicate proteomic (SELDI) profiles from 227 subjects (118 TCC, 77 healthy controls and 32 controls with benign urological conditions) and used linear mixed effects models to identify peaks that are differentially expressed between TCC and controls and within TCC subgroups. A Random Forest classifier was trained on 130 profiles to develop an algorithm to predict the presence of TCC in a randomly selected initial test set (n = 54) and an independent validation set (n = 43) several months later. Twenty two peaks were differentially expressed between all TCC and controls (p < 10(-7)). However potential confounding effects of age, sex and analytical run were identified. In an age-matched sub-set, 23 peaks were differentially expressed between TCC and combined benign and healthy controls at the 0.005 significance level. Using the Random Forest classifier, TCC was predicted with 71.7% sensitivity and 62.5% specificity in the initial set and with 78.3% sensitivity and 65.0% specificity in the validation set after 6 months, compared with controls. Several peaks of importance were also identified in the linear mixed effects model. We conclude that SELDI profiling of urine samples can identify patients with TCC with comparable sensitivities and specificities to current tumor marker tests. This is the first time that reproducibility has been demonstrated on an independent test set analyzed several months later. Identification of the relevant peaks may facilitate multiplex marker assay development for detection of recurrent disease. |
| Databáze: | OpenAIRE |
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