摘要
目的应用表面加强激光解吸离子化飞行时间质谱（SELDI—TOF—MS）技术分析胃癌血清蛋白质指纹图谱，筛选胃癌特异性血清蛋白组合，建立胃癌诊断模型。方法利用强阴离子交换芯片（Q10）分别对山东省临朐县胃癌高发区36例胃癌患者和46例浅表性胃炎患者的血清蛋白指纹图谱进行检测，采用ZUCIPDAS蛋白芯片数据分析系统，建立高发区胃癌血清蛋白指纹图谱诊断模型，并利用盲筛法验证模型的正确性。结果通过对26例胃癌患者及37例浅表性胃炎患者血清蛋白指纹图谱进行检测，筛选出差异具有统计学意义的6个蛋白质峰，质荷比分别为8587、6945、8243、3899、7035和9943，并建立胃癌蛋白指纹图谱诊断模型，该模型诊断胃癌的灵敏度达88．5％，特异度达97．3％。在完成建模后，选择19例患者血清进行盲筛，其中胃癌患者10例，浅表性胃炎患者9例。通过盲筛验证，该模型诊断胃癌的灵敏度为80.0％，特异度为88．9％。结论初步建立了胃癌高发区胃癌血清蛋白指纹图谱诊断模型，该模型诊断胃癌的灵敏度和特异度均达到80％以上，该方法为胃癌的诊断提供了新的思路。
Objective To explore the specific and sensitive biomarkers for gastric cancer detection, a surface-enhanced laser desorption and ionization protein chip mass spectrometry （SELDI-TOF-MS） was used to generate protein profiles of serum in gastric cancer at a high-risk area. Methods A total of 36 gastric cancer cases and 46 subjects with superficial gastritis were selected from Linqu county, Shandong province, a high-risk area of gastric cancer. Serum samples were collected and Q10 protein chips were used to detect the serum proteomic patterns, and the sensitivity and specificity were assessed. Results For the comparison of the gastric cancer group （ 26 out of 36 gastric cancer cases ） versus superficial gastritis group （37 out of 46 subjects） , the 6 most discriminating peaks （ m/z 8587,6945,8243,3899,7035, and 9943 ） were identified by the ProteinChip Data Analysis System （ZUCIPDAS）. The sensitivity and specificity of this pattern were 88.5% and 97. 3%, respectively. A total of 19 subjects （10 gastric cancer cases and 9 superficial gastritis subjects） was selected to test the accuracy of this pattern by using blind method, and the sensitivity and specificity were 80. 0% and 88. 9%, respectively. Conclusion Our findings suggest that SELDI profiling of serum might be a potential for gastric cancer detection and screening in high-risk population.
出处
《中华预防医学杂志》
CAS
CSCD
北大核心
2008年第9期677-680,共4页
Chinese Journal of Preventive Medicine
基金
国家自然科学基金（30572135）
北京市自然科学基金（7062016）
关键词
质谱
胃肿瘤
DNA指纹法
蛋白质阵列分析
Mass spectrometry
Stomach neoplasms
DNA fingerprinting
Protein array analysis
