血清蛋白质质谱与支持向量机模型在胰腺癌检测上的应用

目的:为提高胰腺癌的早期检测率筛选新的标志物,应用蛋白芯片结合表面增强激光解吸电离飞行时间质谱(SELDI-TOF-MS)技术建立胰腺癌的血清蛋白质质谱模型。方法:用弱阳离子交换芯片(CM10)结合SELDI-TOF-MS技术检测了73例血清样本,其中31例胰腺癌,22例胰腺炎,20例健康人。用支持向量机方法建立胰腺癌和健康人以及胰腺癌和胰腺炎的辨别模型。结果:胰腺癌和健康人辨别模型用了3个蛋白质峰,辨别的敏感性和特异性均为100%,而胰腺癌和胰腺炎辨别模型用了5个蛋白质峰,辨别的特异性和敏感性分别为95.5%和93.5%。结论:SELDI-TOF-MS技术结合生物信息学方法检测胰腺癌具有较高的敏感性和特异性。

Application of serum protein fingerprint in diagnosis of pancreatic cancer

Objective: To establish serum protein fingerprint model for early diagnosis of pancreatic cancer with surface enhanced laser desorption/ionization time of flight-mass spectrometry(SELDI-TOF-MS) and bioinformatics techniques.Methods: A total of 73 samples were analyzed in this study,including 31 cases of pancreatic cancers,22 cases of pancreatitis and 20 healthy individuals.Samples were first analyzed by SELDI-TOF-MS and two patterns of differentiation model were constructed with support vector machine arithmetic method.Results: The pattern 1 model differentiating pancreatic cancer patients from healthy individuals had a specificity and a sensitivity of both 100.0%.The pattern 2 model differentiating pancreatic cancer from pancreatitis had a specificity of 95.5% and a sensitivity of 93.5%.Conclusions: SELDI-TOF-MS technique combined with bioinformatics can facilitate to identify biomarkers for pancreatic cancer.