摘要
目的比较肺癌患者、肺良性病患者及正常人血清蛋白质组的差异,以期发现可用于肺癌诊断的生物学指标。方法采用表面增强激光解吸离子化飞行时间质谱(SELDI-TOF-MS)蛋白芯片技术,检测89例肺癌患者、64例肺良性病患者及68例健康人血清,筛选出有分类意义的差异蛋白并建立分类树模型。再从同期住院或健康体检人群中随机抽取肺癌患者、肺良性病患者及健康人各30例组成测试组,对诊断分类树模型进行盲法验证。结果比较三组血清,共发现39种差异蛋白质(P<0.05),由其中质荷比(M/Z)为4485、5252、5807、5908、5969、6113、6625、8946、8998、9137、9183、9298、9498、13878、15128、15867、16081的17种蛋白建立的诊断分类树模型,对肺癌、肺良性病、健康人的分类准确率为98.2%(217/221),敏感度为98.9%(88/89),特异度为97.7%(129/132),双盲验证的敏感度和特异度分别为90.0%(27/30)和93.3%(56/60)。结论利用SELDI-TOF-MS技术建立的肺癌诊断分类树模型具有较高的敏感度和特异度,可用于肺癌的快速诊断。
Objective To explore and determine the biologic markers for the diagnosis of lung cancer by comparison of proteomics among patients with lung cancer,those with benign lung tumor and healthy people.Methods The serum proteomics patterns of 89 cases of lung cancer,64 cases of benign lung tumor and 68 healthy subjects were read by surface enhanced laser desorption/ionization-time of flight-mass spectrometry （SELDI-TOF-MS） to screen significant differential proteins,and to develop a classification tree model for the diagnosis of lung cancer.Thirty cases of lung cancer,30 cases of benign lung tumor and 30 healthy subjects were randomly selected at the same period and assigned as test groups for double-blind verification of the model.Results Thirty-nine differential proteins were identified from the three groups,and the classification tree model formed by 17 proteins （M/Z：4485,5252,5807,5908,5969,6113,6625,8946,8998,9137,9183,9298,9498,13878,15128,15867 and 16081） could be used to identify lung cancer,benign lung tumor and healthy subjects with an accuracy of 98.2% （217/221）,sensitivity of 98.9% （88/89） and specificity of 97.7% （129/132）,respectively.The double-blind test challenged the model with a sensitivity of 90.0% （27/30） and specificity of 93.3% （56/60）.Conclusion The classification tree model constructed by SELDI-TOF-MS possesses high sensitivity and specificity,and it may be used for rapid diagnosis of lung cancer.
出处
《解放军医学杂志》
CAS
CSCD
北大核心
2010年第4期439-441,共3页
Medical Journal of Chinese People's Liberation Army
基金
全军医学科研"十一五"基金(08Z006)
关键词
肺肿瘤
光谱法
质量
表面增强激光解吸离子化飞行时间
蛋白质组学
生物学标记
lung neoplasms
spectrometry
mass
surface-enhanced laser desorption/ionization time-of-flight
proteomics
biological markers
