Metabolite marker discovery for the detection of bladder cancer by comparative metabolomics
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Chi-Hung Shao1,*, Chien-Lun Chen2,3,*, Jia-You Lin4, Chao-Jung Chen5,6,7, Shu-Hsuan Fu8, Yi-Ting Chen8,9, Yu-Sun Chang8, Jau-Song Yu8,9,10, Ke-Hung Tsui2, Chiun-Gung Juo8 and Kun-Pin Wu1
1Institute of Biomedical Informatics, National Yang Ming University, Taipei 11221, Taiwan
2Department of Urology, Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan
3College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
4Department of Biomedical Engineering, National Yang Ming University, Taipei 11221, Taiwan
5Proteomics Core Laboratory, China Medical University Hospital, Taichung 40402, Taiwan
6Department of Medical Research, China Medical University Hospital, Taichung 40402, Taiwan
7Graduate Institute of Integrated Medicine, China Medical University, Taichung 40402, Taiwan
8Molecular Medicine Research Center, Chang Gung University, Taoyuan 33302, Taiwan
9Department of Biomedical Sciences, Chang Gung University, Taoyuan 33302, Taiwan
10Department of Cell and Molecular Biology, Chang Gung University, Taoyuan 33302, Taiwan
*These authors have contributed equally to this work
Kun-Pin Wu, email: email@example.com
Chiun-Gung Juo, email: firstname.lastname@example.org
Keywords: bladder cancer, metabolomics, metabolite marker selection, decision tree, machine learning
Received: June 29, 2016 Accepted: February 28, 2017 Published: March 21, 2017
Bladder cancer is one of the most common urinary tract carcinomas in the world. Urine metabolomics is a promising approach for bladder cancer detection and marker discovery since urine is in direct contact with bladder epithelia cells; metabolites released from bladder cancer cells may be enriched in urine samples. In this study, we applied ultra-performance liquid chromatography time-of-flight mass spectrometry to profile metabolite profiles of 87 samples from bladder cancer patients and 65 samples from hernia patients. An OPLS-DA classification revealed that bladder cancer samples can be discriminated from hernia samples based on the profiles. A marker discovery pipeline selected six putative markers from the metabolomic profiles. An LLE clustering demonstrated the discriminative power of the chosen marker candidates. Two of the six markers were identified as imidazoleacetic acid whose relation to bladder cancer has certain degree of supporting evidence. A machine learning model, decision trees, was built based on the metabolomic profiles and the six marker candidates. The decision tree obtained an accuracy of 76.60%, a sensitivity of 71.88%, and a specificity of 86.67% from an independent test.
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