应用SELDI-TOF-MS技术建立胰腺癌筛选血清蛋白指纹图谱模型

目的：建立胰腺癌筛选血清蛋白质指纹图谱模型。方法：应用表面增强激光解析离子化飞行时间质谱（SELDI-TOF-MS）技术，采用弱阳离子交换芯片（CM10）首先对33例胰腺癌患者和31例性别年龄匹配的健康对照血清蛋白表达谱的差异进行了分析，建立决策树分类模型。结果：在质荷比（M/Z）2000～20000范围内，共检测到185个有效蛋白峰，其中12个峰差异有显著性（P〈0.01）。自动选用M/Z为11674.7和7771.2的2个差异蛋白峰，建立胰腺癌决策树分类模型，其灵敏度为96.97%（32/33），特异度为96.67%（30/31）。结论：应用SELDI-TOF-MS技术可以筛选出胰腺癌相关的血清蛋白标志，建立的决策树模型可能对胰腺癌的诊断具有重要的临床价值。

Establishment of a Classification Diagnostic Model From Characteristic Serum Proteomic Patterns with Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry for Screning Pancreatic Cancer

Objective To establish a classification diagnostic model from characteristic serum proteomic patterns for screening pancreatic cancer. Methods With CM10 protein chip,surface-enhanced laser desorption ionization-time of flight-mass spectrometry (SELDI-TOF-MS) was used to analyze the difference of serum protein profiles in 33 patients with pancreatic cancer and 31 controls and a classification diagnostic model for of pancreatic cancer was established. Results 185 effective protein peaks were detected at the m...