Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men.
Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men.
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发表时间：
2002-07
期刊：
影响因子：
11.2
通讯作者：
B. Adam;Yinsheng Qu;John W. Davis;M. Ward;M. Clements;L. Cazares;O. Semmes;P. Schellhammer
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文献类型：
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作者：
B. Adam;Yinsheng Qu;John W. Davis;M. Ward;M. Clements;L. Cazares;O. Semmes;P. Schellhammer
前列腺特异性抗原检测在提高对前列腺癌（PCA）的认识以及更好地管理患者方面一直是一个主要因素，但其缺乏特异性限制了它在诊断中的应用，并且不利于前列腺癌的早期检测。我们研究的目的是利用能够同时解析和分析多种蛋白质的蛋白质谱技术，确定更好的用于早期检测前列腺癌的生物标志物。评估多种蛋白质对于建立区分癌症与非癌症的特征性蛋白质组模式，以及识别癌症的所有基因亚型及其生物活性至关重要。在这项研究中，我们使用了一种蛋白质生物芯片表面增强激光解吸/电离质谱方法，并结合一种人工智能学习算法，来区分前列腺癌患者和非癌症人群。我们利用167名前列腺癌患者、77名良性前列腺增生患者以及82名年龄匹配的未患病健康男性的血清表面增强激光解吸/电离质谱蛋白质谱来训练和开发一种决策树分类算法，该算法使用一种九种蛋白质的质量模式，能够正确分类96%的样本。在分析前通过分层随机抽样从训练集中分离出一个盲测集，用于确定分类系统的敏感性和特异性。在比较前列腺癌组与非癌症组（良性前列腺增生患者/健康男性）时，得到该研究人群的敏感性为83%、特异性为97%、阳性预测值为96%，普通人群的阳性预测值为91%。这种高通量蛋白质组分类系统将为前列腺癌的早期检测/诊断提供一种高度准确和创新的方法。
The prostate-specific antigen test has been a major factor in increasing awareness and better patient management of prostate cancer (PCA), but its lack of specificity limits its use in diagnosis and makes for poor early detection of PCA. The objective of our studies is to identify better biomarkers for early detection of PCA using protein profiling technologies that can simultaneously resolve and analyze multiple proteins. Evaluating multiple proteins will be essential to establishing signature proteomic patterns that distinguish cancer from noncancer as well as identify all genetic subtypes of the cancer and their biological activity. In this study, we used a protein biochip surface enhanced laser desorption/ionization mass spectrometry approach coupled with an artificial intelligence learning algorithm to differentiate PCA from noncancer cohorts. Surface enhanced laser desorption/ionization mass spectrometry protein profiles of serum from 167 PCA patients, 77 patients with benign prostate hyperplasia, and 82 age-matched unaffected healthy men were used to train and develop a decision tree classification algorithm that used a nine-protein mass pattern that correctly classified 96% of the samples. A blinded test set, separated from the training set by a stratified random sampling before the analysis, was used to determine the sensitivity and specificity of the classification system. A sensitivity of 83%, a specificity of 97%, and a positive predictive value of 96% for the study population and 91% for the general population were obtained when comparing the PCA versus noncancer (benign prostate hyperplasia/healthy men) groups. This high-throughput proteomic classification system will provide a highly accurate and innovative approach for the early detection/diagnosis of PCA.