Detection of bladder cancer in human urine by metabolomic profiling using high performance liquid chromatography/mass spectrometry
Detection of bladder cancer in human urine by metabolomic profiling using high performance liquid chromatography/mass spectrometry
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DOI：
10.1016/j.juro.2008.01.084
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发表时间：
2008-06-01
影响因子：
6.6
通讯作者：
Mullerad, Michael
中科院分区：
文献类型：
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作者：
Issaq, Haleem J.;Nativ, Ofer;Mullerad, Michael
目的：目前使用膀胱镜筛查和检测膀胱癌是侵入性和扩张。各种基于尿的生物标志物已用于此目的，但成功有限。代谢组学，即代谢组学，是定量测量的代谢反应的病理生理刺激。该分析提供了可以是各种良性和恶性病症的特征的代谢物模式。我们评估了高效液相色谱联用质谱代谢组学的方法来区分尿液样本从健康人和膀胱癌patients.Materials和方法：尿液标本收集自48名健康人和41例移行细胞癌患者，并保存在-80 ℃。使用Agilent 1100系列高效液相色谱系统（Agilent Technologies，Santa Clara，加州）与混合三重-四重飞行时间QSTAR（R）XL质谱仪联机分析样品。在分析时，将样品解冻并离心。提交各样品的总离子色谱图进行统计分析。本研究采用了主成分分析和正交偏最小二乘判别分析两种统计方法进行数据解释。使用正离子质谱正交偏最小二乘判别分析正确预测了48个健康尿液样本中的48个和41个膀胱癌尿液样本中的41个，而主成分分析，这是一种无监督的轮廓统计方法，证实了这些结果，并正确预测46的48个健康和40的41膀胱癌尿液samples.Conclusions：在一个相对较少的科目的概念研究证明的结果表明，代谢组学使用高效液相色谱-质谱有可能成为一个非侵入性的早期检测膀胱癌的测试。
Purpose: The current use of cystoscopy for screening and detecting bladder cancer is invasive and expansive. Various urine based biomarkers have been used for this purpose with limited success. Metabolomics, ie metabonomics, is the quantitative measurement of the metabolic response to pathophysiological stimuli. This analysis provides a metabolite pattern that can be characteristic of various benign and malignant conditions. We evaluated high performance liquid chromatography coupled online with a mass spectrometer metabolomic approach to differentiate urine samples from healthy individuals and patients with bladder cancer.Materials and Methods: Urine specimens were collected from 48 healthy individuals and 41 patients with transitional cell carcinoma, and stored at -80C. Samples were analyzed using an Agilent 1100 Series high performance liquid chromatography system (Agilent Technologies, Santa Clara, California) coupled online with a hybrid triple-quad time-of-flight QSTAR(R) XL mass spectrometer. At the time of analysis samples were thawed and centrifuged. The resulting total ion chromatograms of each sample were submitted for statistical analysis. For data interpretation in this study 2 statistical methods were used, that is principal component analysis and orthogonal partial least square-discriminate analysis.Results: Using positive ionization mass spectrometry orthogonal partial least square-discriminate analysis correctly predicted 48 of 48 healthy and 41 of 41 bladder cancer urine samples, while principal component analysis, which is an unsupervised profiling statistical method, confirmed these results and correctly predicted 46 of 48 healthy and 40 of 41 bladder cancer urine samples.Conclusions: The results of this proof of concept study in a relatively small number of subjects indicate that metabolomics using high performance liquid chromatography-mass spectrometry has the potential to become a noninvasive early detection test for bladder cancer.