Issaq Haleem J, Nativ Ofer, Waybright Timothy, Luke Brian, Veenstra Timothy D, Issaq Elias J, Kravstov Alexander, Mullerad Michael
Laboratory of Proteomics and Analytical Technologies, SAIC Frederick, Inc., NCI-Frederick, Frederick, Maryland 21702, USA.
J Urol. 2008 Jun;179(6):2422-6. doi: 10.1016/j.juro.2008.01.084. Epub 2008 Apr 23.
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.
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 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.
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.
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.
目前用于膀胱癌筛查和检测的膀胱镜检查具有侵入性且费用高昂。各种基于尿液的生物标志物已用于此目的,但成效有限。代谢组学,即代谢物组学,是对病理生理刺激的代谢反应进行定量测量。该分析提供了一种可表征各种良性和恶性病症的代谢物模式。我们评估了在线联用质谱仪的高效液相色谱代谢组学方法,以区分健康个体和膀胱癌患者的尿液样本。
收集了48名健康个体和41名移行细胞癌患者的尿液标本,并储存在-80°C。使用安捷伦1100系列高效液相色谱系统(安捷伦科技公司,加利福尼亚州圣克拉拉)在线联用混合型三重四极杆飞行时间QSTAR XL质谱仪对样本进行分析。分析时将样本解冻并离心。将每个样本得到的总离子色谱图提交进行统计分析。本研究中使用了两种统计方法进行数据解读,即主成分分析和正交偏最小二乘判别分析。
使用正离子质谱法的正交偏最小二乘判别分析正确预测了48份健康尿液样本中的48份以及41份膀胱癌尿液样本中的41份,而作为一种无监督的谱图统计方法的主成分分析证实了这些结果,并正确预测了48份健康尿液样本中的46份以及41份膀胱癌尿液样本中的40份。
这项在相对少量受试者中进行的概念验证研究结果表明,使用高效液相色谱-质谱联用的代谢组学有潜力成为一种用于膀胱癌的非侵入性早期检测测试。