SELDI-TOF-MS蛋白芯片技术在胃癌早期诊断中的应用

Application of SELDI-TOF-MS Protein Chip Technology in Diagnosis of Early Gastric Carcinoma

目的 建立胃癌早期患者的血清差异表达蛋白诊断模型,构建用于胃癌早期诊断的敏感和特异的新方法.方法 采用PBSⅡ/C 型蛋白质指纹图谱仪及金芯片检测45例早期胃癌患者及80例健康对照人群血清蛋白质图谱,用Biomarker Wizard 3.1 软件分析所得数据,筛选胃癌差异表达蛋白.通过人工神经网络数学模型（ANN）选择最佳差异表达组合建立并验证胃癌的诊断模型.内标物质加入血清样本,校准和消除操作条件波动对分析结果的影响,提高实验的准确度.最后,应用SPSS13.0软件绘制胃癌诊断模型的受试者工作特征曲线（ROC curve）,评价诊断模型的准确度.结果 在质荷比为2 000～20 000范围内共检测到有显著差异的蛋白质峰（t检验,P〈0.01）28个.经ANN反复训练筛选其中5个差异蛋白（质荷比分别为2 545.3±3.6,2 942.7±4.3,3 135.5±2.8,4 130.6±2.1,8 691.4±1.3）组成胃癌人工神经网络诊断模型,对胃癌的诊断灵敏度为95.0%,特异度97.5%,阳性预测值为95.0%,阴性预测值97.5%,诊断准确度为96.67%.绘制ROC曲线,曲线下面积为0.972,表明该诊断模型准确性较高.结论 该研究建立的诊断模型对胃癌的诊断准确度较高.SELDI-TOF-MS蛋白芯片技术在胃癌的早期诊断和血清肿瘤标志物的筛选方面具有一定价值,值得进一步深入研究.

Objective To build different expressed protein diagnostic model of patients with early gastric carcinoma and find new method with superiority in sensitivity and specificity for detection of early gastric carcinoma. Methods The PBS II/C protein spectrometry analyzer and golden protein chip were performed to detect mass spectrogram of 45 cases patients with early gastric carcinoma and 80 cases healthy persons for sera protein signature analysis. Then the different expressed markers were screened from the maps by Biomarker Wizard 3.1 software. Artificial Neural Network （ANN） was used to build the best different expressed protein group and test diagnostic model of gastric carcinoma. Inner standard was used into samples to eliminate the error by fluctuation of operation for high veracity. Finally ROC curve was used to evaluate its diagnostic value. Results Total of 28 different expressed protein peaks were detected between the groups of gastric carcinoma and contrast group on 2000-20 000 zone （t test, P（0.01）. Five specific protein peaks （M/Z as 2 545.3±3.6,2 942.7±4. 3,3 135.5±2.8,4 130. 6±2.1,8 691.4±1.3） were chosen to develop the artificial neural network diagnostic model. The model was yielded a sensitivity of 95.0% and a specificity of 97. 5% and a positive predictive value of 95.0% ,a negative predictive value of 97.5% ,a accuracy of 96.67%. Area under ROC curve was 0. 972, which yield a high accuracy. Conclusion The model built by this study has high accuracy on diagnosis of gastric carcinoma. SELDI-TOF-MS protein chip technology is valuable to the early diagnosis of gastric carcinoma,choice of tumor markers in serum and need further study.