Kuang Zhizhou, Huang Ruochun, Yang Zhimin, Lv Zhiqiang, Chen Xinyan, Xu Fuping, Yi Yu-Hua, Wu Jian, Huang Ruo-Pan
RayBiotech Inc, Guangzhou, China.
RayBiotech Inc, Parkway Lane, Norcross, GA, USA.
Oncotarget. 2018 Aug 24;9(66):32624-32641. doi: 10.18632/oncotarget.25976.
Screening biomarkers in serum samples for different diseases has always been of great interest because it presents an early, reliable, and, most importantly, noninvasive means of diagnosis and prognosis. Reverse phase protein arrays (RPPAs) are a high-throughput platform that can measure single or limited sets of proteins from thousands of patients' samples in parallel. They have been widely used for detection of signaling molecules involved in diseases, especially cancers, and related regulation pathways in cell lysates. However, this approach has been difficult to adapt to serum samples. Previously, we developed a sensitive method called the enhanced protein array to quantitatively measure serum protein levels from large numbers of patient samples. Here, we further refine the technology on several fronts: 1. simplifying the experimental procedure; 2. optimizing multiple parameters to make the assay more robust, including the support matrix, signal reporting method, background control, and antibody validation; and 3. establishing a method for more accurate quantification. Using this technology, we quantitatively measured the expression levels of 10 proteins: alpha-fetoprotein (AFP), beta 2 microglobulin (B2M), Carcinoma Antigen 15-3(CA15-3), Carcinoembryonic antigen (CEA), golgi protein 73 (GP73), Growth differentiation factor 15 (GDF15), Human Epididymis Protein 4 (HE4), Insulin Like Growth Factor Binding Protein 2 (IGFBP2), osteopontin (OPN) and Beta-type platelet-derived growth factor receptor (PDGFRB) from serum samples of 132 hepatocellular carcinoma (HCC) patients and 78 healthy volunteers. We found that 6 protein expression levels are significantly increased in HCC patients. Statistical and bioinformatical analysis has revealed decent accuracy rates of individual proteins, ranging from 0.617 (B2M) to 0.908 (AFP) as diagnostic biomarkers to distinguish HCC from healthy controls. The combination of these 6 proteins as a specific HCC signature yielded a higher accuracy of 0.923 using linear discriminant analysis (LDA), logistic regression (LR), random forest (RF) and support vector machine (SVM) predictive model analyses. Our work reveals promise for using reverse phase protein arrays for biomarker discovery and validation in serum samples.
筛选不同疾病血清样本中的生物标志物一直备受关注，因为它提供了一种早期、可靠且最重要的是非侵入性的诊断和预后手段。反相蛋白质阵列（RPPA）是一种高通量平台，可并行测量数千名患者样本中的单个或有限组蛋白质。它们已被广泛用于检测细胞裂解物中参与疾病（尤其是癌症）的信号分子及相关调控途径。然而，这种方法一直难以应用于血清样本。此前，我们开发了一种名为增强型蛋白质阵列的灵敏方法，用于定量测量大量患者样本中的血清蛋白水平。在此，我们在几个方面进一步完善了该技术：1. 简化实验程序；2. 优化多个参数以使检测更稳健，包括支持基质、信号报告方法、背景对照和抗体验证；3. 建立更准确的定量方法。利用该技术，我们定量测量了132例肝细胞癌（HCC）患者和78名健康志愿者血清样本中10种蛋白质的表达水平：甲胎蛋白（AFP）、β2微球蛋白（B2M）、癌抗原15-3（CA15-3）、癌胚抗原（CEA）、高尔基体蛋白73（GP73）、生长分化因子15（GDF15）、人附睾蛋白4（HE4）、胰岛素样生长因子结合蛋白2（IGFBP2）、骨桥蛋白（OPN）和β型血小板衍生生长因子受体（PDGFRB）。我们发现HCC患者中有6种蛋白质表达水平显著升高。统计和生物信息学分析显示，作为区分HCC与健康对照的诊断生物标志物，单个蛋白质的准确率不错，范围从0.617（B2M）到0.908（AFP）。使用线性判别分析（LDA）、逻辑回归（LR）、随机森林（RF）和支持向量机（SVM）预测模型分析，这6种蛋白质组合作为特定的HCC标志物，准确率更高，达到0.923。我们的工作表明，反相蛋白质阵列在血清样本中发现和验证生物标志物方面具有前景。
