Urinary polyamine biomarker panels with machine-learning differentiated colorectal cancers, benign disease, and healthy controls

Tetsushi Nakajima, Kenji Katsumata, Hiroshi Kuwabara, Ryoko Soya, Masanobu Enomoto, Tetsuo Ishizaki, Akihiko Tsuchida, Masayo Mori, Kana Hiwatari, Tomoyoshi Soga, Masaru Tomita, Masahiro Sugimoto

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Colorectal cancer (CRC) is one of the most daunting diseases due to its increasing worldwide prevalence, which requires imperative development of minimally or non-invasive screening tests. Urinary polyamines have been reported as potential markers to detect CRC, and an accurate pattern recognition to differentiate CRC with early stage cases from healthy controls are needed. Here, we utilized liquid chromatography triple quadrupole mass spectrometry to profile seven kinds of polyamines, such as spermine and spermidine with their acetylated forms. Urinary samples from 201 CRCs and 31 non-CRCs revealed the N1,N12-diacetylspermine showing the highest area under the receiver operating characteristic curve (AUC), 0.794 (the 95% confidence interval (CI): 0.704–0.885, p < 0.0001), to differentiate CRC from the benign and healthy controls. Overall, 59 samples were analyzed to evaluate the reproducibility of quantified concentrations, acquired by collecting three times on three days each from each healthy control. We confirmed the stability of the observed quantified values. A machine learning method using combinations of polyamines showed a higher AUC value of 0.961 (95% CI: 0.937–0.984, p < 0.0001). Computational validations confirmed the generalization ability of the models. Taken together, polyamines and a machine-learning method showed potential as a screening tool of CRC.

Original languageEnglish
Article number756
JournalInternational journal of molecular sciences
Volume19
Issue number3
DOIs
Publication statusPublished - 2018 Mar 7

Keywords

  • Colorectal cancer
  • Liquid chromatography-mass spectrometry
  • Machine learning
  • Polyamine
  • Urine

ASJC Scopus subject areas

  • Catalysis
  • Molecular Biology
  • Spectroscopy
  • Computer Science Applications
  • Physical and Theoretical Chemistry
  • Organic Chemistry
  • Inorganic Chemistry

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  • Cite this

    Nakajima, T., Katsumata, K., Kuwabara, H., Soya, R., Enomoto, M., Ishizaki, T., Tsuchida, A., Mori, M., Hiwatari, K., Soga, T., Tomita, M., & Sugimoto, M. (2018). Urinary polyamine biomarker panels with machine-learning differentiated colorectal cancers, benign disease, and healthy controls. International journal of molecular sciences, 19(3), [756]. https://doi.org/10.3390/ijms19030756