Diagnosis of bladder cancer and prediction of survival by urinary metabolomics

• Published in: Oncotarget Vol. 5; no. 6; pp. 1635 - 1645
• Main Authors: Jin, Xing, Yun, Seok Joong, Jeong, Pildu, Kim, Isaac Yi, Kim, Wun-Jae, Park, Sunghyouk
• Format: Journal Article
• Language: English
• Published: United States Impact Journals LLC 30.03.2014
• Subjects: Aged; Biomarkers, Tumor - urine; Case-Control Studies; Chromatography, High Pressure Liquid; Female; Follow-Up Studies; Humans; Lymphatic Metastasis; Male; Mass Spectrometry; Metabolomics; Middle Aged; Muscle Neoplasms - diagnosis; Muscle Neoplasms - mortality; Muscle Neoplasms - urine; Neoplasm Grading; Neoplasm Invasiveness; Prognosis; Research Paper; ROC Curve; Survival Rate; Urinary Bladder Neoplasms - diagnosis; Urinary Bladder Neoplasms - mortality; Urinary Bladder Neoplasms - urine
• ISSN: 1949-2553; 1949-2553
• DOI: 10.18632/oncotarget.1744

Bladder cancer (BC) is a common cancer but diagnostic modalities, such as cystoscopy and urinary cytology, have limitations. Here, 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 diseases). 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 betaoxidation. The association of these metabolites with cancer was corroborated by microarray results showing that carnitine transferase and pyruvate dehydrogenase complex expressions are significantly altered in cancer groups. In terms of clinical applicability, the differentiation model diagnosed BC with a sensitivity and specificity of 91.3% and 92.5%, respectively, and comparable results were obtained by receiver operating characteristic analysis (AUC = 0.937). Multivariate regression also suggested that the metabolomic profile correlates with cancer-specific survival time. 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.