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表面增强激光解吸电离飞行时间质谱蛋白质芯片筛查肺癌血清相关蛋白质及其临床意义 被引量:2

Detection of serum proteomic patterns of patients with lung cancer by surface enhanced laser desorption/ionization-time of flight-mass spectrometry and its clinical significance
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摘要 目的 探讨用表面增强激光解吸电离飞行时间质谱技术(SELDI-TOF-MS)筛查肺癌血清特异性蛋白质的临床意义.方法 应用CM-10蛋白质芯片对肺鳞状细胞癌38例,肺腺癌30例,健康对照组50例的血清样品进行蛋白质指纹图谱测定,其中34例肺癌患者进行术前和术后对比检测,用BioMarker Wizard及BioMarker pattern system分析软件对所得的数据进行处理并建立诊断模型.结果 共检测到167个蛋白质峰,筛选出2个肺癌特异蛋白质峰[质荷比(M/Z)分别为6 010、5 330]建立了诊断模型.模型的原始判别敏感度为100%,特异度为100%.交叉验证敏感度为98%,特异度为96%.肺癌术前和术后有9个差异蛋白质(P<0.05),转移和无转移肺癌患者血清有8个差异蛋白质(P<0.05).结论 SELDI技术在肺癌的诊断中具有较高的敏感性和特异性,可能成为一种有效的筛查手段. Objective To investigate the serum proteomic patterns in lung cancer by surface enhanced laser desorption/ionization-time of flight-mass spectrometry (SELDI-TOF-MS) techniques and build diagnostic models in order to evaluate their clinical significance by biomarkers screening in lung cancer.Methods SELDI-TOF-MS and CM-10 protein chip were used to detect the serum proteomic patterns of 38 lung squamous cell carcinoma and 30 lung adenocarcinomas,including the comparation in 34 geminate patient serums before and after surgery.Nagative control setted as a group of 50 normal tissues.BioMarker Wizard and BioMarker pattern system software were used in combination to analyze the data and to develop diagnostic models.Results Two protein peaks from a total of 167 were chosen as a biomarker pattern in the training set.Two protein peaks pattern (mass/charge ratio [m/z] 6 010,5 330) was observed in the model that could be used as to distinguish lung cancer from non-cancerous diseases.It yielded a sensitivity of 98 % and a specificity of 96 %.There were 9 different protein peaks (P < 0.05) between pre-surgery and post-surgery.There were 8 different peaks (P < 0.05) between metastasis and non-metastasis.Conclusion SELDI techniques can be employed as diagnosis in lung cancer patients with relatively high sensitivity and specificity,and also could be used as an effective tool for the screening of lung cancer.
出处 《肿瘤研究与临床》 CAS 2014年第9期601-604,607,共5页 Cancer Research and Clinic
基金 山西省科技攻关项目(20090311060)
关键词 肺肿瘤 肿瘤标志 光谱法 质量 基质辅助激光解吸电离 Lung neoplasms Tumor biomakers Spectrometry, mass, matrix-assisted laser de sorption-ionization
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