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目的:探讨用表面增强激光解吸电离飞行时间质谱(SELDI-TOF-MS)技术筛查肺癌血清特异性蛋白质的临床意义。方法:应用SELDI-TOF-MS对35例正常对照组、43例治疗前肺癌病人的血清样品进行蛋白质指纹图谱测定,用BioMarker Wizard 3．01及BioMarker Parrern System 5．01分析软件对测得的数据进行处理及建立诊断模型。结果:共检测到251个蛋白质峰,筛选出差异蛋白质峰11个,以质荷比(m/z)分别为M2799_26,M3227_41,M5739_70和M8164_30的4个蛋白质峰为依据组合构建分类决策树模型,分出5个终节点。决策树模型的原始判别总准确率为91．0%(71/78),敏感性为88．4%(38/43),特异性为94．3%(33/35);交叉验证总准确率为85．9%(67/78),敏感性为88．4%(38/43),特异性为82．9%(29/35)。结论:SELDI-TOF-MS在肺癌血清特异性蛋白质的筛选及诊断模型的建立有一定的临床意义。
Objective: To investigate the clinical significance of SELDI-TOF-MS in the screening of serum-specific proteins in lung cancer using surface-enhanced laser desorption / ionization time-of-flight mass spectrometry. Methods: Serum samples of 35 normal controls and 43 pre-treatment lung cancer patients were analyzed by protein fingerprinting using SELDI-TOF-MS. Biomarker Wizard 3.01 and BioMarker Parrern System 5.01 software were used to analyze the measured data Process and establish diagnostic models. Results: A total of 251 protein peaks were detected and 11 differential protein peaks were screened. Based on the four protein peaks of M2799_26, M3227_41, M5739_70 and M8164_30 with mass / charge ratio (m / z) Divide 5 end nodes. The overall accuracy of the decision tree model was 91.0% (71/78), the sensitivity was 88.4% (38/43) and the specificity was 94.3% (33/35). The overall accuracy of cross-validation The rate was 85.9% (67/78), the sensitivity was 88.4% (38/43) and the specificity was 82.9% (29/35). Conclusion: SELDI-TOF-MS has some clinical significance in the screening of serum-specific protein and establishing the diagnosis model of lung cancer.