用蛋白质芯片技术筛选非小细胞肺癌患者血清中标志蛋白

目的 探讨用蛋白质芯片技术检测血清非小细胞肺癌（NSCLC）标志蛋白筛查肺癌患者的可行性。方法 用蛋白质芯片表面增强激光解吸电离飞行时间质谱仪（SELDI-TOF-MS）技术检测123例肺癌患者和40名正常人血清蛋白质质谱。用数字表法随机抽取94份标本（53例NSCLC，21例小细胞肺癌和20名正常人）作为训练组进行系统训练，将筛选出来的相对分子质量为11493、6429、8245、5336及2536的5个蛋白峰作为一个标志物组合模式，建立分类树模型（即系统训练过程）；用69份未知血清标本（49例NSCLC，20名正常人）作为盲筛组验证该模型。结果 系统显示，在训练组该模式检测NCLC的敏感性和特异性分别为95．9％（71／74）、90．0％（18／20），盲筛组分别为83．7％（41／49）及80．0％（16／20）。结论 蛋白质芯片SELDI-TOF-MS技术能较准确的区分NSCLC患者与健康对照者，该技术为NSCLC的筛查提供了新的有效工具。

Application of serum surface-enhanced laser desorption/ionization proteomic patterns in distinguishing non-small cell lung cancer patients from healthy people

Objective To explore the application of serum surface-enhanced laser desorption/ionization(SELDI) marker patterns in distinguishing non-small cell lung cancer patients from healthy people by protein chip technology.Methods One hundred and sixty-three serum samples(123 patients with lung cancer and 40 healthy persons),were randomly divided into a training set 94 cases,53 non-small cell lung cancer(NSCLC),21 small cell lung cancer and 20 healthy persons] and a blinded test set(69 cases),were included for analysis by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry(SELDI-TOF-MS).Five protein peaks at 11 493,6 429,8 245,5 336 and 2 536 were automatically chosen for the system training and the development of a decision classification tree model(marker pattern).The accuracy of the model was tested with the blinded test set(an independent set of masked serum samples from 49 patients with NSCLC and 20 healthy persons).Results The model differentiated the patients with NSCLC from the healthy people with a sensitivity of 95.9%(71/74) and a specificity of 90.0%(18/20) in the training set and a sensitivity of 83.7%,and a specificity of 80.0% in the blinded set respectively.Conclusion SELDI-TOF-MS technique can correctly distinguish NSCLC patients from healthy people,and it has the potential for the development of a screening test for the detection of NSCLC.