School of Pharmacy , Shenyang Pharmaceutical University , 103 Wenhua Road , Shenyang 110016 , China.
Metabolomics Core Facility of RHLCCC , Northwestern University Feinberg School of Medicine , Chicago , Illinois 60611 , United States.
Anal Chem. 2019 Mar 5;91(5):3389-3396. doi: 10.1021/acs.analchem.8b04715. Epub 2019 Feb 12.
Lipid quantification is the ultimate goal in lipidomics studies challenged by the availability of standard compounds. A novel strategy for targeted lipidomics based on LC-MS/MS parameters prediction and multivariate statistical analysis was developed for the quantitation of lysophosphatidylcholines (LPCs) in this study. Multiple linear regression models were established with the acyl chain length and number of double bonds after the prediction correlation coefficients ( R) were evaluated. Then related analytical parameters including collision energy, declustering potential, retention time, and response factor were successfully predicted for any given LPC. With this "model-prediction" strategy, sensitivity, accuracy, and coverage of targeted lipidomics were improved significantly, and 60 LPCs were determined simultaneously in plasma for the first time. An integrated evaluation method for multi-indexes, logistic regression-ROC analysis was also proposed after biomarkers were identified by Student's t test, univariate ROC curve, and PLS-DA. Then the developed workflow was successfully used to discover and evaluate multi-LPCs indexes (a set of LPCs biomarkers with the best discriminating ability) for differentiating lung, breast, colorectal, and gastric cancer from controls, and among different types of cancer. Finally, the multi-LPCs index for lung cancer was compared with the plasma before and after treatment to test its utility. The novel targeted lipidomics methodology for LPCs was expected to provide a new insight into quantitative lipidomics and further clinical application.
脂质定量是脂质组学研究的最终目标，但受到标准化合物可用性的挑战。本研究提出了一种基于 LC-MS/MS 参数预测和多变量统计分析的靶向脂质组学新策略，用于定量分析溶血磷脂酰胆碱 (LPC)。通过评估预测相关系数 (R) 来建立具有酰基链长度和双键数的多元线性回归模型。然后，成功预测了任何给定 LPC 的碰撞能、解簇电位、保留时间和响应因子等相关分析参数。通过这种“模型预测”策略，靶向脂质组学的灵敏度、准确性和覆盖范围得到了显著提高，首次在血浆中同时测定了 60 种 LPC。在通过学生 t 检验、单变量 ROC 曲线和 PLS-DA 鉴定生物标志物后，还提出了一种用于多指标的综合评估方法，即逻辑回归-ROC 分析。然后，成功地将开发的工作流程用于发现和评估多-LPC 指数（一组具有最佳区分能力的 LPC 生物标志物），以区分肺癌、乳腺癌、结直肠癌和胃癌与对照组，以及不同类型的癌症之间。最后，将肺癌的多-LPC 指数与治疗前后的血浆进行比较，以测试其效用。该靶向脂质组学新方法有望为定量脂质组学和进一步的临床应用提供新的见解。
