Urinary Polyamine Biomarker Panels with Machine-Learning Differentiated Colorectal Cancers, Benign Disease, and Healthy Controls.
Urinary Polyamine Biomarker Panels with Machine-Learning Differentiated Colorectal Cancers, Benign Disease, and Healthy Controls.
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DOI：
10.3390/ijms19030756
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发表时间：
2018-03-07
影响因子：
5.6
通讯作者：
Sugimoto M
中科院分区：
文献类型：
--
作者：
Nakajima T;Katsumata K;Kuwabara H;Soya R;Enomoto M;Ishizaki T;Tsuchida A;Mori M;Hiwatari K;Soga T;Tomita M;Sugimoto M
结直肠癌（CRC）是最令人生畏的疾病之一，由于其在全球范围内的发病率不断增加，这迫切需要开发微创或非侵入性筛查测试。尿多胺已被报道为检测CRC的潜在标志物，并且需要准确的模式识别来区分CRC与早期病例和健康对照。在这里，我们利用液相色谱三重四极杆质谱分析七种多胺，如精胺和亚精胺与它们的乙酰化形式。来自201个CRC和31个非CRC的尿液样本显示N1，N12-二乙酰精胺显示最高的受试者工作特征曲线下面积（AUC），0.794（95%置信区间（CI）：0.704-0.885，p < 0.0001），以区分CRC与良性和健康对照。总体而言，分析了59份样本，以评价定量浓度的重现性，通过从每个健康对照中采集三次，每次三天。我们确认了观察到的定量值的稳定性。使用多胺组合的机器学习方法显示出更高的AUC值0.961（95%CI：0.937-0.984，p < 0.0001）。计算验证证实了模型的泛化能力。总之，多胺和机器学习方法显示出作为CRC筛查工具的潜力。
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.
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影响因子：
4.6
作者：
Uemura H;Ghaibeh AA;Katsuura-Kamano S;Yamaguchi M;Bahari T;Ishizu M;Moriguchi H;Arisawa K
通讯作者：
Arisawa K
影响因子：
5.9
作者：
Brown DG;Rao S;Weir TL;O'Malia J;Bazan M;Brown RJ;Ryan EP
通讯作者：
Ryan EP
影响因子：
254.7
作者：
Siegel, Rebecca;Ma, Jiemin;Jemal, Ahmedin
通讯作者：
Jemal, Ahmedin
影响因子：
2.4
作者：
Katanoda, Kota;Kamo, Ken-Ichi;Nishimoto, Hiroshi
通讯作者：
Nishimoto, Hiroshi
影响因子：
1.5
作者：
Lalosevic, Milica Stojkovic;Stankovic, Sanja;Krivokapic, Zoran
通讯作者：
Krivokapic, Zoran