TY - JOUR
T1 - “Modeling−Prediction” Strategy for Deep Profiling of Lysophosphatidic Acids by Liquid Chromatography−Mass Spectrometry
T2 - Exploration Biomarkers of Breast Cancer
AU - Zhang, Qian
AU - Yang, Xiao
AU - Wang, Qian
AU - Zhang, Yiwen
AU - Gao, Peng
AU - Li, Zuojing
AU - Liu, Ran
AU - Xu, Huarong
AU - Bi, Kaishun
AU - Li, Qing
N1 - Funding Information:
This work was supported by the National Natural Science Foundation of China (Grant Nos. 81973464/H2803 and 81703463/H3010), Support Program for Young and Middle-Aged Technological Innovation Talents (RC190505) and Liaoning Distinguished Professor Project for Qing Li (2017)
PY - 2020/12/20
Y1 - 2020/12/20
N2 - 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.
AB - 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.
KW - Breast cancer biomarker
KW - Deep profiling
KW - Lysophosphatidic acid
KW - “Modeling−prediction” strategy
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U2 - 10.1016/j.chroma.2020.461634
DO - 10.1016/j.chroma.2020.461634
M3 - Article
C2 - 33176220
AN - SCOPUS:85095701331
VL - 1634
JO - Journal of Chromatography A
JF - Journal of Chromatography A
SN - 0021-9673
M1 - 461634
ER -