Research Papers:

Metabolite marker discovery for the detection of bladder cancer by comparative metabolomics

Chi-Hung Shao, Chien-Lun Chen, Jia-You Lin, Chao-Jung Chen, Shu-Hsuan Fu, Yi-Ting Chen, Yu-Sun Chang, Jau-Song Yu, Ke-Hung Tsui, Chiun-Gung Juo and Kun-Pin Wu _

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Oncotarget. 2017; 8:38802-38810. https://doi.org/10.18632/oncotarget.16393

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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

Correspondence to:

Kun-Pin Wu, email: [email protected]

Chiun-Gung Juo, email: [email protected]

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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