Zhao Shu-Yun, Qiao Jie, Li Mei-Zhi, Zhang Xiao-Wei, Yu Jie-Kai, Li Rong
Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 100083, China.
Zhonghua Yi Xue Za Zhi. 2008 Jan 1;88(1):7-11.
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.
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.
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.
There were significantly different serum proteomic patterns in different types of PCOS. Using Protein Chip 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.
筛选有或无胰岛素抵抗（IR）的多囊卵巢综合征（PCOS）患者的血清蛋白表达谱，并寻找具有鉴别意义的蛋白质。
研究了北京大学第三医院生殖中心30例有IR的PCOS患者、30例无IR的PCOS患者及30例对照个体的空腹血清样本。
PCOS IR患者与对照之间有27个差异蛋白峰，PCOS非IR患者与对照之间有17个，PCOS IR患者与非IR患者之间有19个。利用支持向量机（SVM）从差异表达蛋白中筛选出标志物蛋白，并分别用于建立PCOS IR、PCOS非IR和IR的三种诊断模型。
不同类型的PCOS血清蛋白质组模式存在显著差异。利用蛋白质芯片结合SVM，快速有效地建立了有或无IR的PCOS的计算机诊断模型。这些具有鉴别意义的蛋白质可能有助于我们了解血清中的蛋白质组变化，并找出PCOS和IR的潜在生物标志物。
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