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

研究成果: Article査読

23 被引用数 (Scopus)

抄録

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.

本文言語English
論文番号756
ジャーナルInternational journal of molecular sciences
19
3
DOI
出版ステータスPublished - 2018 3月 7

ASJC Scopus subject areas

  • 触媒
  • 分子生物学
  • 分光学
  • コンピュータ サイエンスの応用
  • 物理化学および理論化学
  • 有機化学
  • 無機化学

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