血清蛋白质指纹图谱诊断模型在早期胰腺癌中的应用研究  

The Application of Serum Protein Fingerprinting Diagnostic Pattern to Early Pancreatic Cancer

作　　者：刘建[1] 余捷凯[2] 邹璎[1] 毛捷鸿[1] 彭佳萍[2] 刘颖斌[3] 林汉庭[2] 郑树[2] 

机构地区：[1]浙江中医药大学附属第二医院,浙江杭州310005 [2]浙江大学附属第二医院,浙江杭州310009 [3]上海长海医院,上海200433

出　　处：《肿瘤学杂志》2011年第10期776-778,共3页Journal of Chinese Oncology

基　　金：浙江省科技厅项目(2008C33067)

摘　　要：[目的]利用表面增强激光解吸电离飞行时间质谱(SELDI-TOF-MS)技术和生物信息学方法筛选胰腺癌的血清肿瘤标志物。[方法]用弱阳离子交换芯片(CM10)结合并用SELDI-TOF-MS检测54例胰腺癌样本(22例早期胰腺癌,32例晚期胰腺癌)。用支持向量机(SVM)方法建立辨别模型。[结果]利用筛选出的5个蛋白峰(m/z6667.68、8572.38、2958.76、6441.59、5913.36Da)用于建立区分早、晚期胰腺癌的SVM模型,模型的灵敏度和特异性分别为90.9%和78.1%。[结论]SELDI-TOF-MS技术结合生物信息学方法可找到辨别早期胰腺癌和晚期胰腺癌的标志物,并建立区分模型。[Purpose] To screen serum tumor biomarkers by surface enhanced laser desorption/ionization time of flight-mass spectrometry （SELDI-TOF-MS） technology and bioinformatics tools for pancreatic cancer. [Methods] A total of 54 samples were analyzed, including 22 cases with early pancreatic cancer, and 32 cases with advanced pancreatic cancer. The samples were first united with CM10 protein chips and were analyzed by SELDI-TOF-MS . The pattern was established by support vector machine arithmetic（SVM）.[Results] Five fingerprinting peaks, m/z 6667.68 Da,8572.38 Da, 2958.76 Da,6441.59 Da, and 5913.36Da was screened out. SVM models was established by utilizing these biomarkers,to differentiate early pancreatic cancer from advanced pancreatic cancer with a specificity of 78.1% and sensitivity of 90.9%. [Conclusion] SELDI-TOF-MS technique combined with bioinformatics can facilitate the discovery of better biomarkers for early pancreatic cancer and provide a useful tool for molecular diagnosis of early pancreatic cancer.

关 键 词：胰腺肿瘤 表面加强激光解吸电离飞行时间质谱 支持向量机 蛋白质质谱 

分 类 号：R735.9[医药卫生—肿瘤]