Adam Bao-Ling, Qu Yinsheng, Davis John W, Ward Michael D, Clements Mary Ann, Cazares Lisa H, Semmes O John, Schellhammer Paul F, Yasui Yutaka, Feng Ziding, Wright George L
Department of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, Virginia 23501, USA.
Cancer Res. 2002 Jul 1;62(13):3609-14.
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.
前列腺特异性抗原检测一直是提高前列腺癌（PCA）认知度和改善患者管理的主要因素，但其缺乏特异性限制了其在诊断中的应用，不利于PCA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，识别用于PCA早期检测的更好生物标志物。评估多种蛋白质对于建立区分癌症与非癌症的标志性蛋白质组模式以及识别癌症的所有基因亚型及其生物学活性至关重要。在本研究中，我们使用蛋白质生物芯片表面增强激光解吸/电离质谱方法结合人工智能学习算法，将PCA与非癌症队列区分开来。来自167例PCA患者、77例良性前列腺增生患者和82例年龄匹配的未受影响健康男性的血清表面增强激光解吸/电离质谱蛋白质谱，用于训练和开发一种决策树分类算法，该算法使用一种九蛋白质量模式，能正确分类96%的样本。在分析前通过分层随机抽样与训练集分开的一个盲法测试集，用于确定分类系统的敏感性和特异性。在比较PCA与非癌症（良性前列腺增生/健康男性）组时，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PCA的早期检测/诊断提供一种高度准确和创新的方法。
