Investigation of the urinary metabolic variations and the application in bladder cancer biomarker discovery |
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| Authors: | Xiang Liu Lu He Wenli Zhang Yajie Wang Wei Sun Zhigang Ji |
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| Affiliation: | 1. Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, China;2. Beijing Tiantan Hospital, , Capital Medical University, Beijing, China;3. Core Laboratory for Clinical Medical Research, Beijing Tiantan Hospital, Capital Medical University, Beijing, China;4. Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Science, Beijing, China |
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| 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. |
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| Keywords: | urine metabolomics individual variation bladder cancer biomarker |
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