子宫内膜异位症患者血浆蛋白质指纹图谱诊断分类树模型的初步建立

Establishment of endometriosis diagnostic model using plasma protein profiling

目的应用表面增强激光解吸电离飞行时间质谱（SELDI—TOF—MS）技术结合生物信息学方法筛选子宫内膜异位症（内异症）患者的血浆生物学标志物，并初步建立内异症患者血浆蛋白质指纹图谱诊断分类树模型。方法收集2007年1—10月北京协和医院妇产科收治的因内异症而行腹腔镜手术的内异症患者36例（内异症组）及因卵巢良性肿瘤和不孕行腹腔镜手术的非内异症患者35例（对照组）。采用SELDI—TOF—MS技术及其配套蛋白质芯片检测两组患者的血浆蛋白质指纹图谱，比较两组蛋白质峰的差异。采用分类与回归树（CART）软件建立内异症诊断分类树模型，并对该模型诊断内异症的敏感度和特异度进行验证。结果与对照组比较，内异症组患者血浆中有14个异常表达的蛋白质峰（P〈0．01）。采用相对分子质量分别为3956000、11710000和6986000的3个蛋自质峰构成的内异症诊断模型，其敏感度为92％，特异度为83％；验证后其敏感度为88％，特异度为80％。结论SELDI—TOF—MS技术对于筛选内异症的生物标志物提供了一条新的途径，其临床应用价值值得进一步研究。

Objective To establish the diagnostic model for endometriosis by screening the plasma biomarkers of endometriosis using surface enhanced laser desorption/ionization time of flight mass spectrometry（SELDI-TOF-MS） coupled with bioinformatics. Methods Plasma samples from 36 patients with endometriosis （endometriosis group） matched with 35 patients with infertility or benign ovarian tumors （control group） before laparoscopy were collected at Peking Union Medical College Hospital from January to October 2007. Plasma protein profiling were detected using SELDI-TOF-MS and protein chip and peak intensities were compared between the two groups. Biomarker Discovery Software was used for data analysis and model was build by classification and regression tree software （CART） , sensitivity and specificity of the diagnostic model were verified. Results There were 14 protein peaks abnormally expressed in endometriosis group compared with those of control group （P 〈 0. 01 ）. The diagnostic model composed of three protein peaks with the molecular weight of 3 956 000, 11 710 000 and 6 986 000 showed a sensitivity of 92% and specificity of 83% . In the blind test the model showed a sensitivity of 88% and specificity of 80% . Conclusions SELDI-TOF-MS is a new approach for screening markers of endometriosis. Its clinical value deserves further investigation.