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Int. J. Mol. Sci. 2018, 19(3), 756; https://doi.org/10.3390/ijms19030756

Urinary Polyamine Biomarker Panels with Machine-Learning Differentiated Colorectal Cancers, Benign Disease, and Healthy Controls

1
Department of Gastrointestinal and Pediatric Surgery, Tokyo Medical University, 6-7-1 Nishishinjuku, Shinjuku, Tokyo 160-0023, Japan
2
Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan
3
Research and Development Center for Minimally Invasive Therapies Health Promotion and Preemptive Medicine, Tokyo Medical University, Shinjuku, Tokyo 160-8402, Japan
*
Author to whom correspondence should be addressed.
Received: 28 January 2018 / Revised: 27 February 2018 / Accepted: 2 March 2018 / Published: 7 March 2018
(This article belongs to the Section Molecular Pathology, Diagnostics, and Therapeutics)
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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. View Full-Text
Keywords: colorectal cancer; polyamine; urine; liquid chromatography-mass spectrometry; machine learning colorectal cancer; polyamine; urine; liquid chromatography-mass spectrometry; machine learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Nakajima, T.; Katsumata, K.; Kuwabara, H.; Soya, R.; Enomoto, M.; Ishizaki, T.; Tsuchida, A.; Mori, M.; Hiwatari, K.; Soga, T.; Tomita, M.; Sugimoto, M. Urinary Polyamine Biomarker Panels with Machine-Learning Differentiated Colorectal Cancers, Benign Disease, and Healthy Controls. Int. J. Mol. Sci. 2018, 19, 756.

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