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Journal of ZheJiang University(Medical Science)  2012, Vol. 41 Issue (3): 289-297    DOI:
    
Application of serum protein fingerprint in diagnosis of pancreatic cancer
ZHU Yang-wen1,WANG Yue-dong1,YE Zai-yuan1,HU Xun2,Yu Jie-kai2
1.Department of General Surgery,Zhejiang Provincial People’s Hospital,Hangzhou 310014,China; 2.Cancer Institute,Second Affiliated Hospital,Zhejiang University School of Medicine,Hangzhou 310009,China
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Abstract  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.


Key wordsPancreatic neoplasms/diagnosis; Proteomics; Spectrometry      mass      Matrix-assisted laser desorption-ionization; Tumor markers      biological; Sensitivity and specificity; Blood proteins/analysis     
Published: 25 May 2012
Cite this article:

. Application of serum protein fingerprint in diagnosis of pancreatic cancer. Journal of ZheJiang University(Medical Science), 2012, 41(3): 289-297.

URL:

http://www.zjujournals.com/med/     OR     http://www.zjujournals.com/med/Y2012/V41/I3/289


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。

关键词: 胰腺肿瘤/诊断; 蛋白质组学; 光谱法,  质量,  基质辅助激光解吸电离; 肿瘤标记,  生物学; 敏感性与特异性; 血蛋白质类/分析 
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