Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles.
Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles.
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DOI：
10.1007/s11306-009-0178-y
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发表时间：
2010-03
期刊：
影响因子：
3.6
通讯作者：
Tomita, Masaru
中科院分区：
文献类型：
--
作者：
Sugimoto, Masahiro;Wong, David T.;Hirayama, Akiyoshi;Soga, Tomoyoshi;Tomita, Masaru
关键词：
唾液是一种易于获取和提供信息的生物流体，使其成为早期检测各种疾病的理想选择，包括心血管，肾脏和自身免疫性疾病，病毒和细菌感染，以及重要的癌症。基于唾液的诊断，特别是那些基于代谢组学技术，正在出现，并提供了一个有前途的临床策略，表征唾液分析物和特定疾病之间的关联。在这里，我们进行了全面的代谢物分析唾液样本从215个人（69口腔，18胰腺癌和30乳腺癌患者，11牙周病患者和87健康对照）使用毛细管电泳飞行时间质谱（CE-TOF-MS）。我们确定了57种主要代谢物，可用于准确预测受每种疾病影响的概率。尽管在已知的患者特征和定量代谢物之间发现了小但显著的相关性，但与牙周病患者和对照受试者相比，在所有三种癌症中检测到的大多数代谢物的浓度相对较高。这表明癌症特异性特征嵌入在唾液代谢物中。多元逻辑回归模型产生了高的受试者工作特征曲线（AUC）下的面积，以区分健康对照与每种疾病。口腔癌、乳腺癌、胰腺癌和牙周病的AUC分别为0.865、0.973、0.993和0.969。模型的准确性也很高，交叉验证AUC分别为0.810、0.881、0.994和0.954。这57种代谢物及其组合的定量信息使我们能够预测疾病的易感性。这些代谢物是用于医学筛查的有前景的生物标志物。本文的在线版本（doi：10.1007/s11306-009-0178-y）包含补充材料，可供授权用户使用。
Saliva is a readily accessible and informative biofluid, making it ideal for the early detection of a wide range of diseases including cardiovascular, renal, and autoimmune diseases, viral and bacterial infections and, importantly, cancers. Saliva-based diagnostics, particularly those based on metabolomics technology, are emerging and offer a promising clinical strategy, characterizing the association between salivary analytes and a particular disease. Here, we conducted a comprehensive metabolite analysis of saliva samples obtained from 215 individuals (69 oral, 18 pancreatic and 30 breast cancer patients, 11 periodontal disease patients and 87 healthy controls) using capillary electrophoresis time-of-flight mass spectrometry (CE-TOF-MS). We identified 57 principal metabolites that can be used to accurately predict the probability of being affected by each individual disease. Although small but significant correlations were found between the known patient characteristics and the quantified metabolites, the profiles manifested relatively higher concentrations of most of the metabolites detected in all three cancers in comparison with those in people with periodontal disease and control subjects. This suggests that cancer-specific signatures are embedded in saliva metabolites. Multiple logistic regression models yielded high area under the receiver-operating characteristic curves (AUCs) to discriminate healthy controls from each disease. The AUCs were 0.865 for oral cancer, 0.973 for breast cancer, 0.993 for pancreatic cancer, and 0.969 for periodontal diseases. The accuracy of the models was also high, with cross-validation AUCs of 0.810, 0.881, 0.994, and 0.954, respectively. Quantitative information for these 57 metabolites and their combinations enable us to predict disease susceptibility. These metabolites are promising biomarkers for medical screening. The online version of this article (doi:10.1007/s11306-009-0178-y) contains supplementary material, which is available to authorized users.
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