Ruan Xujun, Wang Yan, Zhou Lirong, Zheng Qiuling, Hao Haiping, He Dandan
Key Laboratory of Drug Metabolism and Pharmacokinetics, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, China.
Department of Pharmaceutical Analysis, College of Pharmacy, China Pharmaceutical University, Nanjing, China.
Front Pharmacol. 2022 May 30;13:894099. doi: 10.3389/fphar.2022.894099. eCollection 2022.
Discovery of disease biomarker based on untargeted metabolomics is informative for pathological mechanism studies and facilitates disease early diagnosis. Numerous of metabolomic strategies emerge due to different sample properties or experimental purposes, thus, methodological evaluation before sample analysis is essential and necessary. In this study, sample preparation, data processing procedure and metabolite identification strategy were assessed aiming at the discovery of biomarker of breast cancer. First, metabolite extraction by different solvents, as well as the necessity of vacuum-dried and re-dissolution, was investigated. The extraction efficiency was assessed based on the number of eligible components (components with MS/MS data acquired), which was more reasonable for metabolite identification. In addition, a simplified data processing procedure was proposed involving the OPLS-DA, primary screening for eligible components, and secondary screening with constraints including VIP, fold change and value. Such procedure ensured that only differential candidates were subjected to data interpretation, which greatly reduced the data volume for database search and improved analysis efficiency. Furthermore, metabolite identification and annotation confidence were enhanced by comprehensive consideration of mass and MS/MS errors, isotope similarity, fragmentation match, and biological source confirmation. On this basis, the optimized strategy was applied for the analysis of serum samples of breast cancer, according to which the discovery of differential metabolites highly encouraged the independent biomarkers/indicators used for disease diagnosis and chemotherapy evaluation clinically. Therefore, the optimized strategy simplified the process of differential metabolite exploration, which laid a foundation for biomarker discovery and studies of disease mechanism.
基于非靶向代谢组学发现疾病生物标志物，对于病理机制研究具有重要意义，并有助于疾病的早期诊断。由于样本性质或实验目的不同，出现了众多代谢组学策略，因此，在样本分析前进行方法学评估至关重要且必不可少。在本研究中，针对乳腺癌生物标志物的发现，对样本制备、数据处理流程和代谢物鉴定策略进行了评估。首先，研究了不同溶剂的代谢物提取方法，以及真空干燥和重新溶解的必要性。基于合格成分（获取了MS/MS数据的成分）数量评估提取效率，这对于代谢物鉴定更为合理。此外，提出了一种简化的数据处理流程，包括OPLS-DA、合格成分的初步筛选以及基于VIP、倍数变化和P值等约束条件的二次筛选。该流程确保只有差异候选物才进行数据解读，极大地减少了数据库搜索的数据量，提高了分析效率。此外，通过综合考虑质量和MS/MS误差、同位素相似性、碎片匹配以及生物来源确认，增强了代谢物鉴定和注释的可信度。在此基础上，将优化后的策略应用于乳腺癌血清样本分析，据此发现的差异代谢物有力地推动了临床上用于疾病诊断和化疗评估的独立生物标志物/指标的发展。因此，优化后的策略简化了差异代谢物探索过程，为生物标志物发现和疾病机制研究奠定了基础。
