Using tree analysis pattern and SELDI-TOF-MS to discriminate transitional cell carcinoma of the bladder cancer from noncancer patients
Using tree analysis pattern and SELDI-TOF-MS to discriminate transitional cell carcinoma of the bladder cancer from noncancer patients
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DOI：
10.1016/j.eururo.2004.10.006
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发表时间：
2005-04-01
期刊：
影响因子：
23.4
通讯作者：
Lu, Y
中科院分区：
文献类型：
--
作者：
Liu, WW;Guan, M;Lu, Y
目的：为了确定是否SELDI蛋白分析的尿液加上树分析模式可以区分TCC从noncancer patients.Methods：蛋白芯片阵列进行蛋白芯片PBS II阅读器的蛋白芯片生物标记系统。研究分为两个阶段：初步阶段与树分析模式的构建，和测试阶段与测试尿液样本。通过由104个样本组成的训练数据集进行树分析模式的生成。结果：尿液样品中平均检测到187个质量峰，其中5个峰被用于构建树状分析模式。分类模式正确预测了训练集中两组样本的91.67-94.64%，总体正确分类率约为93%。该模式正确预测72.0%（49/68）的测试样本，71.4% TCC样本中的25例（72.7%）（33例中的24例）。结论：通过尿蛋白谱分析方法获得的高灵敏度和特异性表明SELDI-TOF-MS是一种有效的尿蛋白分析方法。MS结合树状图分析模式既可帮助区分TCC膀胱癌与非癌，又可提供创新的临床诊断平台，提高TCC膀胱癌患者的检出率。(c)2004 Elsevier B. V.保留所有权利。
Objective: To determine whether SELDI protein profiling of urine coupled with a tree analysis pattern could differentiate TCC from noncancer patients.Methods: The ProteinChip Arrays were performed on a ProteinChip PBS II reader of the ProteinChip Biomarker System. The study was divided into two phases: a preliminary phase with construction of tree analysis pattern, and a testing phase with test urine samples. Generation of the tree analysis pattern was performed by a training data set consisting of 104 samples. The validity of the tree analysis pattern was then challenged with a test set of 68 samples.Results: Average of 187 mass peaks was detected in the urine samples, and five of these peaks were used to construct the tree analysis pattern. The classification pattern correctly predicted 91.67-94.64% of the samples for both of the two groups in the training set, for an overall correct classification of about 93%. The pattern correctly predicted 72.0% (49 of 68) of the test samples, with 71.4% (25 of 35) of the TCC samples, 72.7% (24 of 33) of the noncancer samples.Conclusions: The high sensitivity and specificity obtained by the urine protein profiling approach demonstrate that SELDI-TOF-MS combined with a tree analysis pattern can both facilitate discriminate TCC bladder cancer with noncancer and provide an innovative clinical diagnostic platform improve the detection of TCC bladder cancer patients. (c) 2004 Elsevier B.V. All rights reserved.