SELDI技术筛选胃癌相关血清蛋白质指纹图谱的临床研究

目的 应用SELDI-TOF-MS技术筛选胃癌相关血清蛋白质多肽谱指纹并建立辅助诊断胃癌模型.方法 采用CMIO弱阳离子芯片,结合表面增强激光解吸电离飞行时间质谱(SELDI-TOF-MS)技术检测50例首次就诊且病理诊断为胃癌患者和16例健康人血清样本并记录其蛋白质多肽谱指纹,利用Biomarker Wizard软件比较两组间的血清蛋白质指纹图谱.Biomark-er Patterns软件进行分析,建立胃癌的特异指纹模型.结果 胃癌患者血清蛋白质多肽谱与健康人相比,有34个蛋白质差异有统计学意义,其中m/z为6016,6744,2822,4474,7892和3242的六个蛋白质组成的诊断模型能达到鉴别胃癌患者和健康人的最佳诊断效果,在测试模式下,灵敏度和特异度分别为96.0%和93.8%,准确度为95.5%.结论 SELDI-TOF-MS技术用于胃癌患者血清蛋白质多肽谱分析可筛选出有意义的差异表达蛋白组多肽谱指纹,可用此方法建立胃癌的辅助诊断模型.

The study on the serum proteomic fingerprints in gastric carcinoma patients by SELDI technique

Objective To detect serum protein spectrum in gastric cancer, screen the serum proteins related to gastric carcinoma and build auxiliary diagnosis model with SELDI-TOF-MS, Methods The serum proteomic fingerprints were detected by SELDI-TOF-MS and CM10 proteinchip. The sera came from 50 first visit gastric cancer patients who were diagnosed by pathological method and 16 healthy peo- ple. The data were analyzed by Biomarker Wizard Software and the proteomic fingerprint of gastric carcinoma model was developed by BPS (biomarker pattern software). Results Compared with the healthy people group, there were 34 significant different protein peaks in the gas- tric carcinoma group. And the diagnosis model composed with 6 proteins (M/Z values were 6016, 6744, 2822, 4474, 7892 and 3242) could classify the 2 groups correctly. In the test group, the sensitivity and specificity were 96% and 93.75% respectively, the accuracy was 95.45%. Conclusion SELDI-TOF-MS method show features such as microcontent, fast and high- resolution etc. It could be utilized to screen significant proteins and develop an auxiliary diagnosis model in gastric carcinoma, which may be used to diagnose this disease.