Clinical Laboratory of Coal General Hospital, Beijing, PR China.
Int J Mol Med. 2012 Nov;30(5):1061-8. doi: 10.3892/ijmm.2012.1113. Epub 2012 Aug 30.
Proteomic methods have been widely used in disease marker discovery research. The aim of this study was to discover potential biomarkers for pancreatic cancer (PCa) using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Crude serum samples from 132 patients with PCa and 67 healthy controls (HCs) were analyzed in duplicate using SELDI. Support vector machine (SVM) analysis of the spectra was used to generate a predictive algorithm based on proteins that were maximally differentially expressed between patients with PCa and the HCs in the training cohort. This algorithm was tested using leave-one-out cross-validation in the test cohort. From the 4 significant peaks in the training cohort, a classifier for separating patients with PCa from HCs was developed. The classifier was challenged with all samples achieving 96.67% sensitivity and 100% specificity in the training cohort and 93.1% sensitivity and 78.57% specificity in the test cohort. Additionally, the classifier correctly classified 12/12 stage Ia and 13/16 stage IIa PCa cases. The combination of the SELDI panel and CA19-9 was superior to CA19-9 alone in distinguishing individuals with PCa from the healthy subject group. These results suggest that high-throughput proteomic profiling has the capacity to provide new biomarkers for the early detection and diagnosis of PCa.
蛋白质组学方法已广泛应用于疾病标志物发现研究。本研究旨在使用表面增强激光解吸/电离飞行时间质谱（SELDI-TOF-MS）发现胰腺癌（PCa）的潜在生物标志物。使用 SELDI 对 132 例 PCa 患者和 67 例健康对照者（HCs）的粗血清样本进行了重复分析。对谱图进行支持向量机（SVM）分析，以基于在训练队列中 PCa 患者和 HCs 之间差异最大表达的蛋白质生成预测算法。该算法在测试队列中使用留一法交叉验证进行了测试。从训练队列中的 4 个显著峰中，开发了一种用于区分 PCa 患者和 HCs 的分类器。该分类器对所有样本进行了测试，在训练队列中达到了 96.67%的敏感性和 100%的特异性，在测试队列中达到了 93.1%的敏感性和 78.57%的特异性。此外，该分类器正确分类了 12/12 期 Ia 和 13/16 期 IIa PCa 病例。SELDI 面板与 CA19-9 的组合在区分 PCa 患者与健康受试者组方面优于 CA19-9 单独使用。这些结果表明，高通量蛋白质组学分析具有提供 PCa 早期检测和诊断新生物标志物的能力。
