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, Kun Pin Wu

Research output: Contribution to journalArticlepeer-review

33 Citations (Scopus)

Abstract

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 OPLSDA 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.

Original languageEnglish
Pages (from-to)38802-38810
Number of pages9
JournalOncotarget
Volume8
Issue number24
DOIs
Publication statusPublished - 2017
Externally publishedYes

Keywords

  • Bladder cancer
  • Decision tree
  • Machine learning
  • Metabolite marker selection
  • Metabolomics

ASJC Scopus subject areas

  • Oncology

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