恶性肿瘤化疗后血糖变化的蛋白质组指纹模型  

Development of Proteomic Models for Serum Glucose Changes of Malignant Tumor Patients after Chemotherapy

作　　者：魏淑青[1] 姚智敏[2] 

机构地区：[1]山西省肿瘤医院,山西太原030013 [2]中国医科大学,辽宁沈阳110001

出　　处：《现代生物医学进展》2009年第11期2115-2119,共5页Progress in Modern Biomedicine

基　　金：国家民政部;中国老年学学会十一五专项课题(项目编号:民人教科字(2007)18-2-32)

摘　　要：目的:应用表面增强激光解析离子飞行时间质谱(Surface-enhanced laser desorption ionization time of flight mass spectrometry,SELDI-TOF-MS)技术筛选与恶性肿瘤化疗后血糖变化情况相关的血清蛋白质组指纹并建立模型。方法:应用CM10弱阳离子芯片结合SELDI-TOF-MS技术检测197例恶性肿瘤患者化疗后血清样本的蛋白质谱,2年后随访,按血糖标准分为血糖正常组(171例)、糖耐量异常组(16例)和糖尿病组(10例),利用Biomarker Wizard软件比较各组间的血清蛋白质指纹图谱,Biomarker Pattern软件建立模型。结果:M/Z为4276和4662的两个蛋白质组成的诊断模型可将糖尿病组与糖耐量异常组准确分组,灵敏度、特异度和准确度分别为70%、81.25%和76.92%;M/Z为2818、7535和2633的三个蛋白质组成的诊断模型可将糖尿病组与血糖正常组准确分组,灵敏度、特异度和准确度分别为80%、79.53%和82.32%;M/Z为2818、7744、3187、2564、4175、5165和3374的七个蛋白质组成的诊断模型可将糖耐量异常组与血糖正常组准确分组,灵敏度、特异度和准确度分别为87.5%、87.72%和88.77%。结论:SELDI-TOF-MS技术筛选出恶性肿瘤化疗后三组血糖情况的蛋白质指纹,M/Z为4175、4276、4086、3158、3374、3316、2044、3441、4662和4290可作为预测化疗后糖尿病的指标,M/Z为2818、3374、3352、4276、2932、8817、4070、3187、7535和15525可作为预测化疗后糖耐量异常的指标,M/Z为6021、3187、2818、2932、3273、4070、7916、8817、8057和4387可作为预测化疗后可能不会发生糖尿病的指标,这为化疗副反应的防治提供了科学依据。Objective： By SELDI-TOF-MS, the serum proteomic fingerprints related to serum glucose changes of malignant tumor patients after chemotherapy was selected and constructed for a proteomic model. Method： By surface-enhanced laser desorption / ionization time-of-flight mass spectrometry （SELDI-TOF-MS）, the serum of 197 malignant tumor patients with chemotherapy were tested, and the proteomic fingerprints were received. After 2 years follow-up, all the patients were divided into 3 groups： the euglycemia group （171 individuals）, the carbohydrate tolerance abnormality group （16 individuals）, and the diabetes metlitus group （10 individuals）. The pro- teomic fingerprints were analyzed by Biomarker Wizard Software and the idio-proteomic fingerprint of models were constructed by biomarker pattern software （BPS）. Results： The model composed with 2 proteins （M/Z values were 4276 and 4662） could classify the carbohydrate tolerance abnormality group, and the diabetes mellitus group correctly. In the test model, the sensitivity, specificity and accuracy were 70%, 81.25% and 76.92%. The diagnosis model composed with 3 proteins （M/Z values were 2818,7535 and 2633） could classify the diabetes mellitus group and the euglycemia group correctly. In the test model, the sensitivity, specificity and accuracy were 80 %, 79.53% and 82.32%. The diagnosis model composed with 7 proteins （M/Z values were 2818, 7744, 3187, 2564, 4175, 5165 and 3374） could classify the diabetes mellitus group and the carbohydrate tolerance abnormality group correctly. In the test model, the sensitivity, specificity and accuracy were 87.5%, 87.72% and 88.77%. Conclusions： SELDI-TOF-MS could be utilized to analyze protein profiling in screening serum glucose changing-related biomarkers and developing patterns. The proteomic fingerprints whose M/Z were 4175, 4276, 4086, 3158, 3374, 3316, 2044, 3441, 4662 and 4290 can be regarded as the prediction marker of diabetes mellitus after application of chemotherapy; the proteomic f

关 键 词：恶性肿瘤 化疗 血糖 

分 类 号：R730.5[医药卫生—肿瘤]