SELDI-TOF-MS筛选慢性阻塞性肺疾病血清标志蛋白的初步研究  

Application of SELDI-TOF-MS Technology Analyzing Serum Protein Profiling in Diagnosis of Chronic Obstructive Pulmonary Disease

在线阅读下载全文

作  者:张霞[1] 李琦[1] 吴小意 陈文妃 张宗德[1] 张杰[2] 

机构地区:[1]首都医科大学附属北京胸科医院北京市结核病胸部肿瘤研究所,101149 [2]首都医科大学附属北京天坛医院呼吸科,100050 [3]北京德易生物医学技术有限公司,100035

出  处:《结核病与胸部肿瘤》2015年第2期88-92,共5页Tuberculosis and Thoracic Tumor

基  金:国家科技重大专项课题(2013ZX10003002)

摘  要:目的利用表面增强激光解吸电离飞行时间质谱技术(SELDI—TOF—MS)筛选慢性阻塞性肺疾病(COPD)血清特异标志物。方法应用SELDI—TOF—MS技术检测30例COPD稳定期患者和30例健康对照者血清蛋白指纹图谱,采用Biomarker pattern软件进行分析,建立COPD的诊断模型。结果COPD患者血清蛋白图谱与对照组相比,在相对分子质量2000-15000范围内共检测到75个蛋白峰,发现19个有统计学差异的蛋白峰(P〈0.05)。通过对COPD组与对照组间的数据作进一步分析,经BPS软件分析,建立质荷比(M/Z)3167、4645的差异蛋白组成的诊断模型,其诊断敏感度为96.67%,特异度为96.67%。结论SELDI-TOF-MS技术是一种快速、简单易行、用量少和高通量的分析方法。能直接筛选出COPD血清中特异表达标志物,用特异表达标志物建立的诊断模型能有效区分COPD患者与健康对照者,有望成为COPD诊断的辅助指标。Objective To screen the serum protein biomarkers of chronic obstructive pulmonary disease (COPD) by using surface-enhanced laser adsorption ionization time-of-flight mass spectrometry (SELDI-TOF-MS) technology. Methods SELDI-TOF-MS was used to analyze serum samples including 30 cases of stable COPD patients and 30 cases of healthy controls. Biomarker Pattern Software was used to establish a diagnostic pattern. Results Compared with normal controls, 75 protein peaks were detected at the molecular range of 2 000-15 000 Da and nineteen protein biomarkers were identified. Analyzed by BPS, two protein peaks (M/Z 3 167,4 645) were chosen to build the model for COPD detection. The diagnostic model separated the COPD from the control samples with sensitivity of 96.67 % and specificity of 96.67 %. Conclusions SELDI-TOF-MS is a quick, easy and practical, high throughput analytic method. It can detect biological markers of COPD, and it shows the diagnostic model established by distinguished proteomic peaks can discriminate COPD patients from healthy controls. It will provide a highly accurate approach for the diagnosis of COPD.

关 键 词:慢性阻塞性肺疾病 表面增强激光解析电离飞行时间质谱 蛋白质组学 

分 类 号:R73[医药卫生—肿瘤]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象