汉、藏族肺结核患者血清差异蛋白质的质谱分析  

Mass spectrometry for serum differential proteins from Han and Tibetan patients with pulmonary tuberculosis

作　　者：刘琦[1] 刘新宇[2] 张仁卿[3] 吴桂辉[3] 岳冀 李晓萍 文富强[6] 

机构地区：[1]贵阳医学院附属医院消化内科,贵阳550004 [2]四川大学华西医院生物治疗国家重点实验室,成都610041 [3]成都市传染病医院内科,成都610061 [4]成都市结核病防治研究院内科,成都610016 [5]西藏自治区驻成都办事处医院内科,成都610041 [6]四川大学华西医院呼吸科,成都610041

出　　处：《第三军医大学学报》2010年第18期1986-1990,共5页Journal of Third Military Medical University

基　　金：国家自然科学基金(30425007)~~

摘　　要：目的用表面增强激光解析电离-飞行时间质谱(SELDI-TOF MS)技术比较汉、藏族肺结核患者之间及其与相应的非结核对照者之间的血清差异蛋白,以寻找各自的质谱诊断模型并比较两个民族肺结核的蛋白质差异。方法收集2008年6月至2009年3月四川大学华西医院呼吸科结核病区、西藏自治区驻成都办事处医院内科、成都市传染病医院和成都市结核病防治研究院内科住院患者及门诊健康体检者共154份血清样本,其中87例活动性肺结核患者(汉族38例,藏族49例),67例非结核对照(汉族36例,藏族31例)。SELDI-TOF MS质谱分析分为训练组和测试组:训练组包含53例肺结核(汉族23例,藏族30例)和42例非结核对照(汉族20例,藏族22例),所获得的血清质谱用决策树的学习算法来进行分析,得到的诊断模型再用另一个独立的测试组来进行准确性验证,测试组包含59份血清样本:34例肺结核(汉族15例,藏族19例),25例非结核对照(汉族16例,藏族9例)。结果训练组中得到的诊断模型质荷比(m/z)为3193.61、4592.11可以诊断出20/23例汉族肺结核和17/20例汉族对照,准确度86.04%(敏感性为86.95%,特异性为85.00%),模型m/z为4821.45能够诊断出28/30例藏族肺结核和19/22例藏族对照,准确度90.38%(敏感性93.33%,特异性86.36%),而模型m/z为4091.98、3398.27、7970.44、4965.51、7970.44则能够将藏族肺结核从汉族患者中区分出来(准确度92.45%)。在相应的验证组中,3个诊断模型的准确度分别为70.96%、71.42%和67.92%。同时发现藏、汉族肺结核之间有16个差异蛋白峰。结论SELDI-TOF MS对汉、藏族肺结核的诊断均具有较高价值,两个民族的肺结核患者存在一定的蛋白质表达差异。Objective To compare the serum differential proteins of Han and Tibetan patients with pulmonary tuberculosis,and those of their corresponding gender-and age-matched non-tuberculosis controls with surface enhanced laser desorption/ionization time-of-flight mass spectrometry（SELDI-TOF MS）in order to establish a diagnositic model for tuberculosis differential proteins in these 2 nationalities.Methods A total of 87 sera samples from patients with pulmonary tuberculosis,67 matched non-tuberculosis controls from Han and Tibetan people,54 health individuals,10 acquaired pneumonia and 3 chronic obstructive pulmonary disease were analyzed with SELDI-TOF MS.A training set of spectra derived from analysis of sera from 53 patients with pulmonary tuberculosis（Han 23,Tibetan 30）and 42 non-tuberculosis controls（Han 20,Tibetan 22）were analyzed with a machine learning algorithm called as decision tree boosting.The discovered decision trees were then used to identify an independent testing set of 59 masked samples：34 pulmonary tuberculosis（Han people 15,Tibetan 19）,and 25 controls（Han people 16,Tibetan 9）.Results The decision tree model of m/z 3193.61 and 4592.11 detected 20 of 23 Han people pulmonary tuberculosis（sensitivity 86.59%）and 17 of 20 Han people controls（specificity 85.00%）,and the model of m/z 4821.45 detected 28 of 30 Tibetan pulmonary tuberculosis（sensitivity 93.33%）and 19 of 22 Tibetan controls（specificity 86.36%）in the training set.However,the decision tree model of m/z 4091.98,3398.27,7970.44,4965.51 and 7970.44 gave an accuracy of 92.45% for identifying Tibetan patients from Han people patients.Validation of independent blinded testing set detected 70.96%,71.42%,and 67.92% accuracy,respectively.There were 16 differential protein/peptide peaks between Han and Tibetan patients with pulmonary tuberculosis.Conclusion The above decision model suggests a potential application of SELDI proteomic pattern analysis as a rapid and accurate method to diagnose individuals with pulmonary t

关 键 词：光谱法 质量 基质辅助激光解吸电离 肺结核 蛋白质组学 蛋白质阵列分析 民族差异性 

分 类 号：R521[医药卫生—内科学] R446.112[医药卫生—临床医学]