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Proteomic profiling of urine for the detection of colon cancer

DOI: 10.1186/1477-5956-6-19

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Abstract:

We collected urine from 67 patients with colorectal cancer and 72 non-cancer control subjects, diluted to a constant protein concentration and generated MALDI and SELDI spectra. The intensities of 19 peaks differed significantly between cancer and non-cancer patients by both t-tests and after adjusting for confounders using multiple linear regressions. Logistic regression classifiers based on peak intensities identified colorectal cancer with up to 78% sensitivity at 87% specificity. We identified and independently quantified 3 of the discriminatory peaks using synthetic stable isotope peptides (an 1885 Da fragment of fibrinogen and hepcidin-20) or ELISA (β2-microglobulin).Changes in the urine proteome may aid in the early detection of colorectal cancer.Colorectal cancer is the third most common cancer in the developed world and the second most common cause of cancer-related death. The prognosis is clearly related to the stage at which the disease is detected and this observation has led to screening programmes using the faecal occult blood test that have resulted in a significant reduction in mortality [1]. Other stool-based approaches, such as DNA testing [2] are showing promise but blood tests, such as carcinoembryonic antigen (CEA) have been disappointing due to their low sensitivity in patients with early disease, the target population in screening programmes. The application of other serum biomarkers such as MMP-9, complement C3a des-arg and α-defensins [3-5] or proteomic approaches that seek characteristic diagnostic signatures [6] have met with limited success but have shown that, in principle, they are capable of generating sensitivities and specificities that are superior to CEA.Surface Enhanced Laser Desorption/Ionisation time-of-flight mass spectrometry (SELDI) has been widely applied to serum and plasma in attempts to discover changes in the proteome diagnostic for human cancers (reviewed in [7-9]). This methodology uses on-chip retentate chromatography

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