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基于气相色谱-质谱技术的代谢组学方法发现复发性流产标记物的研究 被引量:2

Research on the Metabolic Markers of Recurrent Spontaneous Abortion Using the Metabonomics Method Based on GC-MS Technique
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摘要 目的:基于气相色谱-质谱联用(GC-MS)技术研究复发性流产(RSA)的发病机制及可能的代谢标记物,为干预和治疗提供理论依据。方法:选择20例健康早孕孕妇(正常对照组)和20例RSA早孕孕妇(RSA组)为研究对象,用GC-MS方法对两组的尿液进行代谢轮廓分析,比较两组尿液代谢组分的差异。结果:两组的总离子流图显示:有一些谱峰信号在RSA组和正常对照组之间存在着明显的差异。采用PCA和PLS-DA对两组尿样代谢轮廓比较发现:两组的聚类基本完全分开,但大部分在三维空间的同一区域。采用PLS-DA方法建模:VIP值>1的差异尿液代谢物共69个,匹配度>80%的差异尿液代谢物共25个。结论:RSA孕妇和健康早孕孕妇的尿液代谢轮廓有明显的差异,这可能是RSA的发病机制所在;25个差异物质可能是RSA的代谢标记物。 Objective: To research the pathogenesis and possible metabolic markers of recurrent spontaneous abortion( RSA) from the perspectives of metabonomics based on the GC-MS technique.Methods: Twenty cases of normal pregnant women in early pregnancy and 20 cases of pregnant women with RSA in early pregnancy.Analyze urinary metabolic profiles using GC-MS method and compare the differences between the two groups. Results: The 2visualized TIC showed that there were some obvious peak signal variations between the RSA pregnant women and healthy pregnant women in early pregnancy.The non-monitoring PCA and PLS-DA was used for analyzing the urine sample metabolic profiles of the two groups,it can be seen that the clustering of the RSA pregnant women and healthy women in early pregnancy was almost completely separated,but were mostly in the same area of 3D space.The model constructed by PLS-DA method indicated that between the two groups,a total of 69 different urinary metabolites whose VIP value 〉1 and 25 differential urinary metabolites whose matching degree 80% were identified. Conclusions: The urinary metabolic profiles of RSA pregnant women and healthy women in early pregnancy are significantly different,and this may be the nosogenesis of RSA.The 25 discrepant urinary metabolites are potential metabolic markers of RSA.
出处 《实用妇产科杂志》 CAS CSCD 北大核心 2017年第1期53-56,共4页 Journal of Practical Obstetrics and Gynecology
关键词 复发性流产 气相色谱-质谱联用 代谢组学 代谢轮廓 Recurrent spontaneous abortion Gas chromatography-mass spectrometry Metabonomic Metabolic profiling
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