Lysophosphatidic acids (LPAs) are important bioactive phospholipids consisting of various species involved in a wide array of physiological and pathological processes. However, LPAs were rarely identified in untargeted lipidomics studies because of the incompatibility with analytical methods. Moreover, in targeted studies, the coverages of LPAs remained unsatisfactorily low due to the limitation of reference standards. Herein, a "modeling-prediction" workflow for deep profiling of LPAs by liquid chromatography-mass spectrometry was developed. Multiple linear regression models of qualitative and quantitative parameters were established according to features of fatty acyl tails of the commercial standards to predict the corresponding parameters for unknown LPAs. Then 72 multiple reaction monitoring (MRM) transitions were monitored simultaneously and species of LPA 14:0, LPA 16:1, LPA 18:3, LPA 20:3 and LPA 20:5 were firstly characterized and quantified in plasma. Finally, the workflow was applied to explore the changes of LPAs in plasma of breast cancer patients compared with healthy volunteers. Multi-LPAs indexes with strong diagnostic ability for breast cancer were identified successfully using Student's t- test, orthogona partial least-squares discrimination analysis (OPLS-DA) and logistic regression- receiver operating characteristic (ROC) curve analysis. The proposed workflow with high sensitivity, high accuracy, high coverage and reliable identification would be a powerful complement to untargeted lipidomics and shed a light on the analysis of other lipids.
Keywords: Breast cancer biomarker; Deep profiling; Lysophosphatidic acid; “Modeling−prediction” strategy.
Copyright © 2020. Published by Elsevier B.V.