Identification of metabolic biomarkers to diagnose epithelial ovarian cancer using a UPLC/QTOF/MS platform
Identification of metabolic biomarkers to diagnose epithelial ovarian cancer using a UPLC/QTOF/MS platform
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使用 UPLC/QTOF/MS 平台鉴定诊断上皮性卵巢癌的代谢生物标志物
DOI：
10.3109/0284186x.2011.648338
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发表时间：
2012-04-01
期刊：
影响因子：
3.1
通讯作者：
Li, Kang
中科院分区：
文献类型：
--
作者：
Fan, Lijun;Zhang, Wang;Li, Kang
背景资料。目前可用的检测方法不足以区分上皮性卵巢癌(EOC)患者和正常人。代谢组学是对生物系统中代谢过程的研究，已成为测量组织或体液中小分子代谢物的关键技术。材料和方法。为探讨代谢组学在EoC相关生物标志物筛选中的应用，采用超高效液相色谱-四极杆飞行时间质谱仪(UPLC/QTOF/MS)对173例初诊EOC患者和93例正常人的血浆标本进行分析。采用两步法选择EOC相关生物标志物。第一步是通过偏最小二乘判别分析和数据库搜索，筛选出区分42例癌症患者和58例正常对照的潜在生物标志物；第二步是在包含38个EOC和35个对照的数据集中验证这些生物标志物的区分性能。结果。筛选出8个候选生物标志物。联合使用这些生物标志物，受试者工作特征曲线的面积为0.941，灵敏度为0.921，特异度为0.886。讨论。我们的研究结果表明，卵巢癌患者和正常对照组之间的代谢谱存在显著差异。已鉴定的8种与EoC相关的代谢物可作为新的诊断生物标志物。
Background. Currently available tests are insufficient to distinguish patients with epithelial ovarian cancer (EOC) from normal individuals. Metabolomics, a study of metabolic processes in biologic systems, has emerged as a key technology in the measurements of small molecular metabolites in tissues or biofluids. Material and methods. To investigate the application of metabolomics on selecting EOC-associated biomarkers, 173 plasma specimens (80 newly diagnosed EOC patients and 93 normal individuals) were analyzed using ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC/QTOF/MS). A two-step strategy was performed to select EOC-associated biomarkers. The first step was to select potential biomarkers in distinguishing 42 cancer patients from 58 normal controls through partial least-squares discriminant analysis (PLS-DA) and database searching, and the second step was to validate the discrimination performance of these biomarkers in a dataset contained 38 EOCs and 35 controls. Results. Eight candidate biomarkers were selected. The combination of these biomarkers resulted in the area of receiver operating characteristic curve (AUC) of 0.941, a sensitivity of 0.921, and a specificity of 0.886 at the best cut-off point for detecting EOC. Discussion. Our findings suggested that sharp differences in metabolic profiles exist between EOC patients and normal controls. The identified eight metabolites associated with EOC may be served as novel biomarkers for diagnosis.