血清非靶向代谢组学分析方法的比较

Tiago A H Fonseca, M. Oliveira, Rúben Araújo, Luís Bento, Cristiana P Von Rekowski, G. Justino, C. Calado
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引用次数: 0

摘要

代谢组学已经成为发现新的医学诊断和预后生物标志物的有力工具。然而,存在许多挑战,例如用于表征系统代谢组的方法。在本工作中,对两种分析平台进行了比较,以获得危重患者的血清代谢组。通过超高效液相色谱-串联质谱(UPLC-MS/MS)的非靶向血清代谢组分析,可以鉴定出一组在死亡和出院患者之间具有统计学差异的代谢物。这组代谢物也能够建立一个非常好的预测模型,基于线性判别分析(LDA),灵敏度和特异性分别为80%和100%。傅里叶变换红外光谱(FTIR)也被用于高通量、简单快速的血清代谢组分析。尽管该技术不能识别代谢物,但它可以识别与每个患者组相关的分子指纹,同时基于主成分分析- lda建立良好的预测模型,灵敏度和特异性分别为100%和90%。因此,这两种分析技术具有互补的特点,应进一步探索代谢组学表征和应用,以发现用于医学诊断和预后的生物标志物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of Analytical Methods Of Serum Untargeted Metabolomics
Metabolomics has emerged as a powerful tool in the discovery of new biomarkers for medical diagnosis and prognosis. However, there are numerous challenges, such as the methods used to characterize the system metabolome. In the present work, the comparison of two analytical platforms to acquire the serum metabolome of critically ill patients was conducted. The untargeted serum metabolome analysis by ultraperformance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) enabled to identify a set of metabolites statistically different between deceased and discharged patients. This set of metabolites also enabled to develop a very good predictive model, based on linear discriminant analysis (LDA) with a sensitivity and specificity of 80% and 100%, respectively. Fourier Transform Infrared (FTIR) spectroscopy was also applied in a high-throughput, simple and rapid mode to analyze the serum metabolome. Despite this technique not enabling the identification of metabolites, it allowed to identify molecular fingerprints associated to each patient group, while leading to a good predictive model, based on principal component analysis-LDA, with a sensitivity and specificity of 100% and 90%, respectively. Therefore, both analytical techniques presented complementary characteristics, that should be further explored for metabolome characterization and application as for biomarkers discovery for medical diagnosis and prognosis.
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