飞行时间质谱技术在食管鳞癌血清学诊断中的应用

目的：用蛋白质质谱分析法寻找食管鳞状细胞癌相对特异的生物标志物。方法：用金属亲和表面（IMAC3）芯片和表面增强激光解析／电离飞行时间质谱仪（SELDI-TOF-MS）检测44例食管鳞癌患者、42例正常人血清的蛋白质质谱。应用Bio-marker Pattem软件建立决策树分类模型，经交叉验证得到该分类模型对测试组病变人群的诊断率。结果：用食管鳞癌患者与正常人质荷比为M9479．43的一种蛋白质建立的决策树分类模型，在学习模式下44例食管鳞癌患者中有43例被正确诊断,42例正常人有40例被诊断正常，诊断准确率为96．5％（83／86），敏感性和特异性分别为97．7％（43／44）、95.2％（40／42）；在检测模式下44例食管鳞癌患者中有42例被正确诊断，42例正常人中有40例被正确分组，敏感性和特异性分别为95.4％（42／44）、95．2％（40／42）。结论：该方法可快速、准确检测食管鳞癌，敏感性、特异性高。

Application of SELDI-TOF MS spectrometry in serological diagnosis for esophageal squamous cell carcinoma

Objective To search for a comparatively specific biomarker of esophageal squamous cell carcinoma(ESCC)by applying protein mass-spectrometry analysis.Methods Proteomic spectra of 44 patients with ESCC and 42 healthy people were generated by IMAC3(CipherGen Inc.)chip and surface-enhanced laser desorption/inionation-time of flight-mass spectra(SELDI-TOF-MS).The decision tree classification algorithm derived from a set of spectra was built by bioinformatics software Biomarker Pattern.The diagnosis rate as well as sensitivity and specificity for the test group was analyzed by cross validation with the decision tree classification model.Results A protein spot with ratio of mass to charge(M/Z)of M9479.43 was selected to build the decision tree classification model for identification of the patients with ESCC.In learning mode,43 patients and 40 controls were correctly identified with accuracy of 96.5%(83/86),and the sensitivity and specificity were 97.7%(43/44)and 95.2%(40/42),respectively.In test mode,42 patients and 40 control people were correctly identified,and the sensitivity and specificity were 95.4 %(42/44)and 95.2%(40/42)respectively.Conclusion ESCC can be rapidly and correctly diagnosed by SELDI-TOF-MS with high sensitivity and specificity.