Investigation of the urinary metabolic variations and the application in bladder cancer biomarker discovery. Issue 2 (2nd March 2018)

• Record Type: Journal Article
• Authors: Liu, Xiaoyan; Cheng, Xiangming; Liu, Xiang; He, Lu; Zhang, Wenli; Wang, Yajie; Sun, Wei; Ji, Zhigang
• Is Part Of: International journal of cancer. Volume 143:Issue 2(2018)
• Page Start: 408
• Page End: 418
• Publication Date: 2018-03-02
• Subjects: urine metabolomics -- individual variation -- bladder cancer -- biomarker; Cancer -- Periodicals; Cancer -- Prevention -- Periodicals; 616.994
• DOI: 10.1002/ijc.31323
• Languages: English
• ISSNs: 0020-7136

Abstract

Abstract : Urine metabolomics have been used to identify biomarkers for clinical diseases. However, inter‐individual variations and effect factors need to be further evaluated. In our study, we explored the urine metabolome in a cohort of 203 health adults, 6 patients with benign bladder lesions, and 53 patients with bladder cancer (BCa) using liquid chromatography coupled with high resolution mass spectrometry. Inter‐individual analysis of both healthy controls and BCa patients showed that the urine metabolome was relatively stable. Further analysis indicated that sex and age affect inter‐individual variations in urine metabolome. Metabolic pathways such as tryptophan metabolism, the citrate cycle, and pantothenate and CoA biosynthesis were found to be related to sex and age. To eliminate age and sex interference, additional BCa urine metabolomic biomarkers were explored using age and sex‐matched urine samples (Test group: 44 health adults vs . 33 patients with BCa). Metabolic profiling of urine could significantly differentiate the cases with cancer from the controls and high‐grade from low‐grade BCa. A metabolite panel consisting of trans‐2‐dodecenoylcarnitine, serinyl‐valine, feruloyl‐2‐hydroxyputrescine, and 3‐hydroxynonanoyl carnitine were discovered to have good predictive ability for BCa with an area under the curve (AUC) of 0.956 (cross validation: AUC = 0.924). A panel of indolylacryloylglycine, N2 ‐galacturonyl‐L‐lysine, and aspartyl‐glutamate was used to establish a robust model for high‐ and low‐grade BCa distinction with AUC of 0.937 (cross validation: AUC = 0.891). External sample (26 control vs . 20 BCa) validation verified the acceptable accuracy of these models for BCa detection. Our study showed that urinary metabolomics is a useful strategy for differential analysis and biomarker discovery. Abstract : What's new? While urine metabolomics have been used to identify disease biomarkers, inter‐individual variations and the effects of physiological factors remain unclear. In this comprehensive characterization of the urine metabolome of 203 healthy subjects and 53 bladder cancer patients, inter‐individual analysis indicated that the urine metabolome was relatively stable and could be used for biomarker discovery. Age and sex were two important factors associated with urine metabolome variation. A pilot characterization by age‐ and sex‐matched urine samples could distinguish bladder cancer cases from controls and high‐grade from low‐grade bladder cancer. Urinary metabolomics is a useful strategy for differential analysis and biomarker discovery.