血清蛋白质指纹图谱预测模型在胰腺癌诊断中的应用研究  

A predictive model for pancreatic cancer using serum protein footprint map

作　　者：龙江[1] 狄扬[1] 金忱[1] 虞先浚[1] 刘维薇[2] 陈宇明[2] 傅德良[1] 倪泉兴[1] 

机构地区：[1]复旦大学附属华山医院普外科复旦大学胰腺病研究所,上海200040 [2]复旦大学附属华山医院检验医学中心

出　　处：《中华医学杂志》2008年第22期1533-1536,共4页National Medical Journal of China

基　　金：国家自然科学基金资助项目（30571820）;上海市科委登山计划基金资助项目（06JC14013）

摘　　要：目的血清蛋白质指纹图谱预测模型在胰腺癌诊断中的建立及应用。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，分析胰腺癌患者和健康对照者的血清蛋白质指纹图谱，从中筛选出与胰腺癌相关的血清差异蛋白质，通过生物信息学方法建立胰腺癌诊断预测模型，并通过盲法检测验证该模型的可靠性。比较该诊断预测模型与胰腺癌传统血清肿瘤标志物CA19-9对于胰腺癌诊断的灵敏度和特异度，评价其临床应用价值。结果采用金属亲和捕获芯片（IMAC3），在胰腺癌患者血清中发现了12个差异表达蛋白质，其中有6个蛋白质诊断胰腺癌的价值高于CA19-9；应用决策树原理，建立了由4个节点、5个终结点组成的胰腺癌血清蛋白质指纹图谱诊断预测模型；经盲法检测，该模型在区分胰腺癌与健康对照者时的灵敏度是90．7％（39／43），特异度是89．6％（43／48），均优于传统的肿瘤标志物CA19-9，诊断预测模型与CA19-9的系列试验可以将特异度提高至97．9％（47／48），而平行试验可以将灵敏度提高至95．3％（41／43）。结论血清蛋白质指纹图谱预测模型为胰腺癌的诊断提供了一条快速、简便、准确的新途径，并有助于筛选新的肿瘤标志物。Objective To establish a predictive model for pancreatic cancer by using serum protein footprint map. Methods Surface-enhanced laser desorption/ionization （SELDI） ProteinChip technology was applied to screen abnormally expressed proteins in 92 pancreatic cancer patients, 15 patients with benign pancreatic diseases, 18 patients with other diseases of digestive tract, and 96 healthy volunteers. 48 patients with pancreatic diseases and 48 healthy volunteers selected randomly constituted the training group, and 43 pancreatic cancer patients,48 healthy volunteers, 15 patients with pancreatic diseases, and 18 patients with malignant diseases of digestive tract selected randomly constituted the testing group. A predictive model was founded by bioinformation methods using the data of the training group. The data of the testing group was imported to this model to verify by blind method. The sensitivity and specificity of both diagnosis models and CA19-9, were analyzed to evaluate the clinical application value of the model in diagnosis of pancreatic cancer. Results Using IMAC3-Cu chip, 12 peaks of differentially expressed proteins were discovered. Six of those had significant value in diagnosis of pancreatic cancer. With the principle of decision tree, a model was founded to diagnose pancreatic cancer, which was composed of 4 decisive nodes and 5 terminal nodes. With the blind test, the sensitivity of the model was 90.7% （39/43） and the specificity was 89.6% （43/ 48） in differentiating cancer from normal status. The modal was better than CA19-9. Serial tests could raise the specificity to 97.9% （47/48）, while parallel test could raise the sensitivity to 95.3% （ 41/43 ）. Conclusion The predictive model established by serum protein footprint map is a quick, easy, convenient and accurate method to diagnosis pancreatic cancer and screen new tumor markers.

关 键 词：胰腺肿瘤 蛋白质组 血清学试验 

分 类 号：R686[医药卫生—骨科学]