SELDI-TOF-MS技术在胰腺癌诊断中的应用

目的 应用表面增强激光解析/离子化飞行时间质谱技术(SELDI-TOF-MS)从胰腺癌患者血清中筛选标志蛋白,找出最佳的标志蛋白组合模式作为临床诊断指标.方法 收集29例胰腺癌患者血清标本和57例年龄、性别相匹配的非癌人群血清标本作为对照.采用SELDI技术检测其蛋白质指纹图谱表达,所得到的结果采用Biomarker Wizard及Biomarker Patterns system软件分析,筛选最终可能用于胰腺癌诊断的蛋白标志物并优化组合建立胰腺癌诊断模型.结果 发现胰腺癌患者和对照组血清蛋白质指纹图谱之间有26个差异表达特异性蛋白,分析系统筛选出一组包含4个标志蛋白(5705、4935、5318和3243 Da)建立起一个胰腺癌的诊断模型,对胰腺癌诊断的敏感性为100%,特异性97.4%.盲法验证此模型敏感性88.9%、特异性89.5%.结论 SELDI-TOF-MS技术的特异性及敏感性远远高于目前所采用的某一单独标志物的血清学诊断,其结果对进一步研究胰腺癌的蛋白质组学改变及其临床诊断应用可能具有重要意义.

Establishment of serum protein pattern model for greening pancreatic cancers by SELDI-TOF-MS technique

Objective To detect the serum specific proteins in pancreatic cancer patients and establish diagnostic model by surface enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF-MS)technique.Methods Twenty-nine serum samples from patients of pancreatic cancer were collected before surgery and an additional 57 serum samples from age and sex matched individuals without cancer were used as controls.SELDI-TOF-MS technique and WCX magnetic beads were used to detect the protein fingerprint expression of all the serum samples and the resulting profiles between pancreatic cancer patients and controls were analyzed with biomarker wizard system,established the model using biomarker patterns system software.A double-blind test was used to determine the sensitivity and specificity of the classification model.Results A panel of four biomarkers(relative molecular weight are 5705,4935,5318 and 3243 Da)were selected to set up a decision trees as the classification model for screening pancreatic cancer effectively.The result yielded a sensitivity of 100%.specificity of 97.4%.The doubleblind test challenged the model with a sensitivity of 88.9%and a specificity of 89.5%.Conclusions SELDI-TOF-MS offers a unique platform for the proteomic detection of serum in pancreatic cancer patients.It also offers a noninvasive method to further study the proteomic changes in the development and progression of pancreatic cancer.