| Beyond the Abstract - Detection of Bladder Cancer in Human Urine by Metabolomic Profiling Using High Performance Liquid Chromatography/Mass Spectrometry |
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| Tuesday, 10 June 2008 | ||
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BERKELEY, CA (UroToday.com) - One of the main goals in the clinical research of patients with bladder cancer is non-invasive detection of the tumors. Attempts to use mutated gene or gene products for this purpose failed mainly due to the genetic heterogeneity of bladder cancer. Metabolomic approaches are based on the assumption that pathologic conditions change metabolic pathways and are characterized by different metabolic profile compared with that of unaffected individuals. In the current study using sophisticated statistical methods we were able to identify and characterize urine metabolite profiles obtained from patients with bladder cancer over controls. The next step in our research will be the identification of key metabolites that are typical to patients with superficial bladder cancer. This may help us to improve and simplify our diagnosis of patients with bladder tumor. Furtherer more it will help to better understand their role in the pathophysiology of the disease and possible as a future target for therapeutic intervention. Metabolomics, or alternately metabonomics, an emerging field of biochemical research, is a complementary technique to genomics, transcriptomics, and proteomics. Identification and quantitative measurements of all the metabolites expressed in urine, serum, plasma and tissue are essential for the study of biological processes in normal and disease states. High performance liquid chromatography was used to resolve the metabolites in urine that were detected by mass spectrometry according to their mass-to-charge ratio. Mass spectrometry was selected for this study because of its sensitivity. The complexity and concentration dynamic range of the metabolites in the metabolome makes the detection and quantitation of every metabolite extremely challenging. That is why we resorted to profiling the control and bladder cancer mass spectrometric data sets using statistical methods, that are commercially available, to discriminate between the two different data sets. Written by
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