title: Urine metabolomics analysis for kidney cancer detection and biomarker discovery.
abstract: Renal cell carcinoma (RCC) accounts for 11,000 deaths per year in the United States. When detected early, generally serendipitously by imaging conducted for other reasons, long term survival is generally excellent. When detected with symptoms, prognosis is poor. Under these circumstances, a screening biomarker has the potential for substantial public health benefit. The purpose of this study was to evaluate the utility of urine metabolomics analysis for metabolomic profiling, identification of biomarkers, and ultimately for devising a urine screening test for RCC. Fifty urine samples were obtained from RCC and control patients from two institutions, and in a separate study, urine samples were taken from 13 normal individuals. Hydrophilic interaction chromatography-mass spectrometry was performed to identify small molecule metabolites present in each sample. Cluster analysis, principal components analysis, linear discriminant analysis, differential analysis, and variance component analysis were used to analyze the data. Previous work is extended to confirm the effectiveness of urine metabolomics analysis using a larger and more diverse patient cohort. It is now shown that the utility of this technique is dependent on the site of urine collection and that there exist substantial sources of variation of the urinary metabolomic profile, although group variation is sufficient to yield viable biomarkers. Surprisingly there is a small degree of variation in the urinary metabolomic profile in normal patients due to time since the last meal, and there is little difference in the urinary metabolomic profile in a cohort of pre- and postnephrectomy (partial or radical) renal cell carcinoma patients, suggesting that metabolic changes associated with RCC persist after removal of the primary tumor. After further investigations relating to the discovery and identity of individual biomarkers and attenuation of residual sources of variation, our work shows that urine metabolomics analysis has potential to lead to a diagnostic assay for RCC.
authors.affiliation: Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, California 95616, USA.
authors.forename: Kyoungmi
authors.initials: K
authors.lastname: Kim
authors.forename: Pavel
authors.initials: P
authors.lastname: Aronov
authors.forename: Stanislav O
authors.initials: SO
authors.lastname: Zakharkin
authors.forename: Danielle
authors.initials: D
authors.lastname: Anderson
authors.forename: Bertrand
authors.initials: B
authors.lastname: Perroud
authors.forename: Ian M
authors.initials: IM
authors.lastname: Thompson
authors.forename: Robert H
authors.initials: RH
authors.lastname: Weiss
mesh_terms: D000328:Adult; D000368:Aged; D014408:Biomarkers, Tumor / Q000652:urine*; D002292:Carcinoma, Renal Cell / Q000175:diagnosis* / Q000378:metabolism / Q000652:urine*; D016000:Cluster Analysis; D016002:Discriminant Analysis; D005247:Feeding Behavior; D005260:Female; D006801:Humans; D007680:Kidney Neoplasms / Q000175:diagnosis* / Q000378:metabolism / Q000652:urine*; D013058:Mass Spectrometry; D055442:Metabolome; D055432:Metabolomics; D008875:Middle Aged; D025341:Principal Component Analysis; D013997:Time Factors
chemical_list: D014408:Biomarkers, Tumor