Salivary metabolomics with machine learning for colorectal cancer detection
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- Hiroshi Kuwabara
- Department of Gastrointestinal and Pediatric Surgery Tokyo Medical University Shinjuku Japan
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- Kenji Katsumata
- Department of Gastrointestinal and Pediatric Surgery Tokyo Medical University Shinjuku Japan
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- Atsuhiro Iwabuchi
- Center for Health Surveillance and Preventive Medicine Tokyo Medical University Hospital Shinjuku Japan
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- Ryutaro Udo
- Department of Gastrointestinal and Pediatric Surgery Tokyo Medical University Shinjuku Japan
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- Tomoya Tago
- Department of Gastrointestinal and Pediatric Surgery Tokyo Medical University Shinjuku Japan
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- Kenta Kasahara
- Department of Gastrointestinal and Pediatric Surgery Tokyo Medical University Shinjuku Japan
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- Junichi Mazaki
- Department of Gastrointestinal and Pediatric Surgery Tokyo Medical University Shinjuku Japan
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- Masanobu Enomoto
- Department of Gastrointestinal and Pediatric Surgery Tokyo Medical University Shinjuku Japan
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- Tetsuo Ishizaki
- Department of Gastrointestinal and Pediatric Surgery Tokyo Medical University Shinjuku Japan
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- Ryoko Soya
- Department of Gastrointestinal and Pediatric Surgery Tokyo Medical University Shinjuku Japan
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- Miku Kaneko
- Institute for Advanced Biosciences Keio University Tsuruoka Japan
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- Sana Ota
- Institute for Advanced Biosciences Keio University Tsuruoka Japan
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- Ayame Enomoto
- Institute for Advanced Biosciences Keio University Tsuruoka Japan
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- Tomoyoshi Soga
- Institute for Advanced Biosciences Keio University Tsuruoka Japan
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- Masaru Tomita
- Institute for Advanced Biosciences Keio University Tsuruoka Japan
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- Makoto Sunamura
- Digestive Surgery and Transplantation Surgery Tokyo Medical University Hachioji Medical Center Tokyo Japan
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- Akihiko Tsuchida
- Department of Gastrointestinal and Pediatric Surgery Tokyo Medical University Shinjuku Japan
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- Masahiro Sugimoto
- Institute for Advanced Biosciences Keio University Tsuruoka Japan
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- Yuichi Nagakawa
- Department of Gastrointestinal and Pediatric Surgery Tokyo Medical University Shinjuku Japan
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説明
<jats:title>Abstract</jats:title><jats:p>As the worldwide prevalence of colorectal cancer (CRC) increases, it is vital to reduce its morbidity and mortality through early detection. Saliva‐based tests are an ideal noninvasive tool for CRC detection. Here, we explored and validated salivary biomarkers to distinguish patients with CRC from those with adenoma (AD) and healthy controls (HC). Saliva samples were collected from patients with CRC, AD, and HC. Untargeted salivary hydrophilic metabolite profiling was conducted using capillary electrophoresis–mass spectrometry and liquid chromatography–mass spectrometry. An alternative decision tree (ADTree)‐based machine learning (ML) method was used to assess the discrimination abilities of the quantified metabolites. A total of 2602 unstimulated saliva samples were collected from subjects with CRC (<jats:italic>n</jats:italic> = 235), AD (<jats:italic>n</jats:italic> = 50), and HC (<jats:italic>n</jats:italic> = 2317). Data were randomly divided into training (<jats:italic>n</jats:italic> = 1301) and validation datasets (<jats:italic>n</jats:italic> = 1301). The clustering analysis showed a clear consistency of aberrant metabolites between the two groups. The ADTree model was optimized through cross‐validation (CV) using the training dataset, and the developed model was validated using the validation dataset. The model discriminating CRC + AD from HC showed area under the receiver‐operating characteristic curves (AUC) of 0.860 (95% confidence interval [CI]: 0.828‐0.891) for CV and 0.870 (95% CI: 0.837‐0.903) for the validation dataset. The other model discriminating CRC from AD + HC showed an AUC of 0.879 (95% CI: 0.851‐0.907) and 0.870 (95% CI: 0.838‐0.902), respectively. Salivary metabolomics combined with ML demonstrated high accuracy and versatility in detecting CRC.</jats:p>
収録刊行物
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- Cancer Science
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Cancer Science 113 (9), 3234-3243, 2022-07-08
Wiley
