谷歌浏览器插件
订阅小程序
在清言上使用

Multifunctional isotopic standards based steroidomics strategy: Exploration of cancer screening model

JOURNAL OF CHROMATOGRAPHY A(2020)

引用 9|浏览37
暂无评分
摘要
Quantitative determination of endogenous compounds in biological samples has still been challenged by the absence of authentic blank matrix. Alternative strategy of surrogate matrix for preparing reference samples are prevalent due to its low cost and high availability. However, the evaluation system of surrogate matrix is not perfect. Herein, a novel multifunctional isotopic standards based steroidomics strategy was developed. Isotope-labeled standards were used not only as internal standards but also for the evaluation the feasibility of surrogate matrix, which improved the accuracy of assessment and could provide a new prospect for the quantitative analysis of endogenous compounds. Based on this approach, a detailed optimization from LC separation, MS detection to extraction conditions for global steroids in the steroidogenesis was firstly carried out. Characteristics and regularities of steroids in LC-MS were summarized to make references for further targeted or untargeted steroidomics study. Then eighteen steroids were quantified with high accuracy and high sensitivity in plasma from four types of cancer patients and healthy subjects using 1% BSA in PBS as surrogate matrix. And multi-steroids indexes with the best discriminating ability for lung, colorectal, breast and gastric cancer in different genders were identified successfully with Student's t-test, PLS-DA and logistic regression- ROC curve analysis. Finally, efficient cancer screening workflow was established by integrating the amine submetabolomics and lipidomics data of our previous studies. Taken together, the integrated steroidomics strategy could shed a light on the guidance for further steroidome as well as other endogenous compounds analysis and may provide a powerful tool for cancer diagnosis. (C) 2019 Published by Elsevier B.V.
更多
查看译文
关键词
Steroidomics,Multifunctional isotopic standards,Surrogate matrix,Cancer biomarkers
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要