血清蛋白指纹图与大肠癌预后诊断  

Serum protein fingerprinting for diagnosis and prognosis evaluation of colorectal cancer

作　　者：王专[1] 李小琼[2] 王开正[2] 邓明明[3] 徐亮[4] 

机构地区：[1]泸州医学院附属医院脊柱外科,四川省泸州市646000 [2]泸州医学院附属医院检验科,四川省泸州市646000 [3]泸州医学院附属医院消化内科,四川省泸州市646000 [4]泸州医学院附属医院普外科,四川省泸州市646000

出　　处：《世界华人消化杂志》2010年第35期3745-3751,共7页World Chinese Journal of Digestology

摘　　要：目的:研究从不同病程的大肠癌患者血清蛋白指纹图谱中筛选出的特征蛋白质组,以期对大肠癌患者进行预后判断以及帮助制定个体化的医疗措施.方法:用高通量高灵敏的表面增强激光解析电离飞行时间质谱技术(SELDI-TOF-MS)检测大肠癌组(45例)、术后预后良好组(术后无复发和无转移14例)、术后预后不良组(术后复发或转移13例)、肠道良性疾病组(24例)和健康人群组(155例)的血清蛋白指纹图谱,利用Biomarker Wizard软件进行差异蛋白质筛选,人工神经网络技术建立大肠癌的诊断模型和预后模型,并检验模型的诊断效率.结果:筛选出7个表达有明显差异的标志蛋白质(P<0.01),其相对分子质量为4955Da、5325Da、5890Da、6615Da、7739Da、8109Da、8575Da.利用这7个标志蛋白质建立人工神经网络大肠癌的诊断模型,再利用相对分子质量为4955Da、5325Da、5890Da、6615Da、7739Da的5个差异蛋白建立人工神经网络大肠癌的预后模型.其中诊断模型对大肠癌的诊断灵敏度和特异度分别为82.22%和80.45%,阴性预测值94.74%,阳性预测值51.39%,准确度为80.80%.预后模型对大肠癌术后复发或转移的诊断符合率为62.96%.结论:从SELDI-TOF-MS血清蛋白指纹图谱中找到了大肠癌不同病程时期的标志蛋白质组,并建立了大肠癌的分类树模型.AIM：To identify differentially expressed proteins for diagnosis and prognosis evaluation of colorectal cancer by serum protein fingerprint in colorectal cancer patients. METHODS：Serum protein fingerprinting was performed by surface-enhanced laser desorption ionization time-of-flight mass spectrometry （SELDI-TOF-MS） in 45 colorectal cancer patients,14 colorectal cancer having a good prognosis （no postoperative recurrence and metastasis）,13 colorectal cancer patients having a poor prognosis （having recurrence or metastasis）,24 patients with benign gastrointestinal disease,and 155 healthy controls. The Biomarker Wizard software was used to identify differential proteins. Two respective artificial neural network （ANN） models were developed for diagnosis and prognosis evaluation of colorectal cancer. RESULTS：Seven proteins that displayed significant differential expression were identified （all P 0.01）,and their molecular weight was 4955 Da,5325 Da,5890 Da,6615 Da,7739 Da,8109 Da,and 8575 Da,respectively. Using these seven protein markers,we developed an artificial neural network model for diagnosis of colorectal cancer. Furthermore,five proteins that had a molecular weight of 4955 Da,5325 Da,5890 Da,6615 Da,and 7739 Da were used to develop an artificial neural network model for evaluation of the prognosis of colorectal cancer. The sensitivity,specificity,negative predictive value,positive predictive value,and accuracy of the diagnostic model were 82.22%,80.45%,94.74%,51.39% and 80.80%,respectively. The coincidence rate of the prognostic model for evaluation of recurrence and metastasis was 62.96%. CONCLUSION：SELDI-TOF-MS serum protein fingerprinting allows identification of differentially expressed proteins in colorectal cancer to develop models for diagnosis and prognosis evaluation of the disease.

关 键 词：大肠癌 表面增强激光解析电离飞行时间质谱技术 人工神经网络 预后判断 蛋白组学 

分 类 号：R735.34[医药卫生—肿瘤]