Abstract
You have accessJournal of UrologyBladder Cancer: Detection & Screening1 Apr 2014MP22-06 A URINARY METABOLOMIC APPROACH CAN DIFFERENTIATE PATIENTS WITH BLADDER CANCER FROM NORMAL OR HEMATURIC CONTROLS, AND PREDICT CANCER-SPECIFIC SURVIVAL Wun-Jae Kim, Phildu Jeong, Xing Jin, Young-Won Kim, In-Chang Cho, Won Tae Kim, Yong-June Kim, Sunghyouk Park, Sang-Cheol Lee, and Seok-Joong Yun Wun-Jae KimWun-Jae Kim More articles by this author , Phildu JeongPhildu Jeong More articles by this author , Xing JinXing Jin More articles by this author , Young-Won KimYoung-Won Kim More articles by this author , In-Chang ChoIn-Chang Cho More articles by this author , Won Tae KimWon Tae Kim More articles by this author , Yong-June KimYong-June Kim More articles by this author , Sunghyouk ParkSunghyouk Park More articles by this author , Sang-Cheol LeeSang-Cheol Lee More articles by this author , and Seok-Joong YunSeok-Joong Yun More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2014.02.854AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Metabolomics is the relatively new scientific study of the biochemical processes that involve metabolites. BC seems to be ideal for urinary metabolomics-based diagnosis, as urine can directly contact the cancer lesion in the bladder. Moreover, urine collection can be made conveniently and its metabolomics study is non-invasive. METHODS In the present study, high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (HPLC-QTOFMS) was used to profile urine metabolites of 138 patients with BC and 121 control subjects (69 healthy people and 52 patients with hematuria due to non-malignant disease), a patient population much larger than those assessed in other BC metabolomics studies. RESULTS Multivariate statistical analysis revealed that the cancer group could be clearly distinguished from the control groups on the basis of their metabolomic profiles, even when the hematuric control group was included. Patients with muscle-invasive BC could also be distinguished from patients with non-muscle-invasive BC on the basis of their metabolomic profiles. Successive analyses identified 12 differential metabolites that contributed to the distinction between the BC and control groups, and many of them turned out to be involved in glycolysis and beta-oxidation. The association of these metabolites with cancer was corroborated by microarray results showing that carnitine transferase and pyruvate dehydrogenase complex expressions is significantly altered in cancer groups. In terms of clinical applicability, the multivariate differentiation model diagnosed BC with a sensitivity and specificity of 91.3% and 92.5%, respectably, and comparable results were obtained by receiver operating characteristic analysis (AUC = 0.937). Multivariate regression also suggested that the metabolic profile correlates with cancer-specific survival time. CONCLUSIONS The excellent performance and simplicity of this metabolomics-based approach suggests that it has the potential to augment or even replace the current modalities for BC diagnosis © 2014FiguresReferencesRelatedDetails Volume 191Issue 4SApril 2014Page: e236 Advertisement Copyright & Permissions© 2014MetricsAuthor Information Wun-Jae Kim More articles by this author Phildu Jeong More articles by this author Xing Jin More articles by this author Young-Won Kim More articles by this author In-Chang Cho More articles by this author Won Tae Kim More articles by this author Yong-June Kim More articles by this author Sunghyouk Park More articles by this author Sang-Cheol Lee More articles by this author Seok-Joong Yun More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...
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