多囊卵巢综合征患者在不同疾病状态下蛋白质谱的变化

Preliminary study of protein expression profiling of PCOS on different state

目的了解多囊卵巢综合征（PCOS）患者不同疾病状态下血清蛋白质谱变化。方法采用表面增强激光解离飞行时间质谱（SELDI—TOF MS）弱阳离子交换蛋白芯片对PCOS患者胰岛素抵抗（IR）、非胰岛素抵抗（non-IR）患者和正常对照者（每组各30例）的血清蛋白质谱进行检测。结果筛查出PCOS IR组与正常组相比有27个差异蛋白质峰，PCOS non—IR组与正常组相比有17个差异蛋白质峰，PCOS IR和non—IR组相比有19个差异蛋白质峰。进一步运用支持向量机（SVM）在差异蛋白质中筛选标志性蛋白质建立了PCOS IR、PCOS non-IR和IR诊断模型，3组模型的敏感性、特异性、阳性及阴性预测值均达80％以上。结论PCOS患者在IR和non-IR两种疾病状态下血清蛋白质表达谱发生显著的变化；运用SELDI—TOF MS技术结合SVM可以快速而有效地建立PCOS诊断模型。

Objective To screen the serum protein expression profiles in patients having polycystic ovary syndrome （PCOS） with or without insulin resistance （IR） and search for discriminatory proteins. Method Fasting serum samples of 30 PCOS patients with IR, 30 PCOS patients without IR, and 30 control individuals from Reproductive Center of Peking University Third Hospital were studied. Results There were 27 differential protein peaks between PCOS IR patients and controls, 17 between PCOS non-IR patients and controls, and 19 between PCOS IR patients and non-IR patients. Marker proteins from differentially expressed proteins were screened out using support vector machine （ SVM）, and were used to establish three diagnostic models for PCOS IR, PCOS non-IR, and IR, respectively. Conclusionss There were significantly different serum proteomic patterns in different types of PCOS. Using ProteinChip combined with SVM, computer diagnostic models for PCOS with and without IR were set up quickly and efficiently. These discriminatory proteins may help us understand the proteomic changes in serum and find out potential biomarkers of PCOS and IR.