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背景与目的早期诊断是提高肺癌生存率的关键,传统的肺癌诊断技术仍存在一定局限性。鉴于近年来以质谱为核心技术的肿瘤蛋白组学在癌症诊断方面的初步研究,本研究探索性应用基质辅助激光解析电离飞行时间质谱(matrix assisted laser desorption ionization-time of ight-mass spectrometry,MALDI-TOF-MS)分析非小细胞肺癌(non-small cell lung cancer,NSCLC)患者和健康人群的血清差异多肽,以建立NSCLC的血清分类模型。方法将年龄和性别匹配的133例NSCLC患者和132例健康者血清标本按照3:1的比例随机分为两组:训练组由100例NSCLC患者和100例健康者血清标本组成,用以建立分类模型;测试组由33例NSCLC患者和32例健康者血清标本组成,用以验证模型。采用铜离子鳌合纳米磁珠提取血清多肽、MALDI-TOF-MS技术检测得到质谱图。ClinProToolsTM统计软件分析训练组NSCLC患者与健康者之间的多肽图谱,从中筛选出一组差异多肽并建立分类模型,最后用测试组对模型进行盲样验证。结果在训练组中观察到血清质荷比(m/z)在1,000Da-10,000Da范围内有131个差异多肽信号峰,在此范围内共得到14个有统计学意义的差异多肽峰(P<0.000,001;AUC0.9),其中NSCLC患者与健康者相比,表达上调的多肽有2个,表达下调的有12个,由统计软件筛选出3个多肽峰(7,478.59Da、2,271.44Da、4,468.38Da)建立分类模型,然后对测试组进行验证,其盲样验证敏感性100%,特异性96.9%,准确率98.5%。结论本组研究显示NSCLC患者与健康人群的血清多肽存在差异,应用MALDI-TOF-MS技术可建立NSCLC的血清多肽分类模型且小规模验证具有较好的敏感性和特异性,希望大规模验证模型,并与传统诊断方法对照或结合,进而尝试建立一种新的NSCLC早期诊断模式。
Background and Objective Early diagnosis is the key to improve the survival rate of lung cancer. The traditional diagnostic techniques of lung cancer still have some limitations. In view of the preliminary research on cancer proteomics with mass spectrometry as the core technology in recent years, this study explored the application of matrix assisted laser desorption ionization-time of light-mass spectrometry (MALDI- TOF-MS) was used to analyze serum differential polypeptides in patients with non-small cell lung cancer (NSCLC) and healthy people to establish a serum classification model of NSCLC. Methods Seventy-three patients with NSCLC and 132 healthy controls were divided into two groups randomly according to the ratio of 3: 1. The training group consisted of 100 NSCLC patients and 100 healthy volunteers to establish the classification Model; the test group consisted of 33 NSCLC patients and 32 healthy volunteers to verify the model. Serum peptides were extracted with copper ion chelate nanospheres and detected by MALDI-TOF-MS. ClinProToolsTM statistical software analysis of training group NSCLC patients and healthy people between the peptide map, screening out a group of different peptides and the establishment of classification model, and finally use the test group to blindly verify the model. Results In the training group, there were 131 differential polypeptide signal peaks in the mass-to-charge ratio (m / z) in the range of 1,000 Da to 10,000 Da. A total of 14 statistically significant differential polypeptide peaks (P <0.000,001; AUC0.9). There were 2 polypeptides with up-regulated expression in 12 NSCLC patients and 12 down-regulated genes compared with healthy controls. Three polypeptide peaks (7,478.59Da, 2,271.44Da, 4,468.38Da), and then validated the test group. The sensitivity of blind test was 100%, the specificity was 96.9% and the accuracy was 98.5%. Conclusion This study shows that there are differences in serum peptides between NSCLC patients and healthy people. Using MALDI-TOF-MS to establish NSCLC serum polypeptide classification model and small-scale verification has good sensitivity and specificity. We hope that the large-scale verification model , And compared with the traditional diagnostic methods or combination, and then try to establish a new model of early diagnosis of NSCLC.