胰腺癌血清蛋白质波谱模型的构建及诊断价值研究

Construction of protein profiling models for diagnosis of pancreatic carcinoma

目的 建立胰腺癌诊断模型,寻找与胰腺癌分期相关的蛋白峰并鉴定出新型的肿瘤标志物.方法 应用表面增强激光解吸离子化飞行时间质谱（SELDI-TOF-MS）技术,用SAX2蛋白芯片,检测胰腺癌患者、胰腺良性疾病患者和正常对照者的血清蛋白指纹谱,统计建立决策树诊断模型和Logistic回归模型并评估诊断价值,蛋白芯片免疫法鉴定差异蛋白峰,ELISA法测定其在血清中的浓度.结果 胰腺癌组和正常对照组指纹谱比较.26个蛋白峰差异有统计学意义（P＜0.01）,胰腺癌组和胰腺良性疾病组指纹谱比较,16个蛋白峰差异有统计学意义（P＜0.05）.建立的决策树诊断模型对胰腺癌的敏感性为83.3%,特异性为100.0%,经ROC曲线评估,该模型优于糖链抗原（CA）19-9.有6个差异蛋白峰在不同分期咦腺癌中差异有统计学意义（P＜0.01）,建立Logistic回归模型诊断早期胰腺癌.敏感性为81.6%,特异性为80.6%.鉴定出差异蛋白峰质荷比（M/Z）28068为C14or166,其诊断胰腺癌的敏感性＞82%,特异性＞88%.结论 应用SELDI-TOF-MS技术建立的诊断模型对胰腺癌的诊断有较大价值,明显优于CA19-9.鉴定出的差异蛋白C14orf166有望在胰腺癌诊断中发挥作用.

Objective To establish diagnostic models for pancreatic carcinoma（PC） and to find out the biomarkers related to PC. Methods Serum samples obtained from subjects including PC patients, pancreatic benign disease patients and normal controls were examined with strong anionic exchange chromatography （SAX2） chips for protein profiling using surface enhanced laser desorption/ ionization-time of flight-mass spectrometry （SELDI-TOF-MS）. The decision tree models and logistic regression models for evaluating the value of serum biomarkers were assessed. SELDI immunoassay and ELISA were used to identify the biomarker and its level in serum respectively. Results Twenty- six mass peaks were different between PC patients and normal controls （P〈0.01） and 16 mass peaks were different between patients with PC and with pancreatic benign disease （P〈0.05）. The decision tree model had a sensitivity of 83.3% and a specificity of 100.0% in differentiation of PC, which was better than that of CA19-9 by ROC curve. There were significant differences in 6 mass peaks among different stages of PC （P 〈0.01）. Logistic regression model showed a sensitivity of 81. 6% and a specificity of 80.6% in diagnosis of early PC. The M/Z 28068 protein was identified as C14orf166 with a sensitivity of more than 82% and a specificity of more than 88% in diagnosis of PC. Conclusions The diagnostic models based on SELDI-TOF-MS were superior to CA19-9 in diagnosis of PC. The identified biomarker C14orf166 is expected to play a role in the diagnosis of PC.