Evaluation of Untargeted Metabolomic Strategy for the Discovery of Biomarker of Breast Cancer

Abstract

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 p 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.

Assessment of Untargeted Metabolomics by Hydrophilic Interaction Liquid Chromatography−Mass Spectrometry to Define Breast Cancer Liquid Biopsy-Based Biomarkers in Plasma Samples

“…Specifically, metabolomic analyses of BC have widely demonstrated that metabolic dysregulation in cancer might be detected in a simple and cost-effective manner which may yield potential biomarkers of the disease behavior after further validation. As an example, untargeted metabolomics have previously shown that altered molecular pathways related to BC initiation involve the biosynthesis of unsaturated fatty acids, aminoacyl-tRNA biosynthesis, carnitine metabolisms, phenylalanine, tyrosine and tryptophan biosynthesis, as well as nicotinate and nicotinamide metabolisms [16,29,30]. Moreover, this strategy could also be used in the search for predictive and prognostic biomarkers when combined with temporal statistical methods that may reinforce its high value as a tool for deciphering cancer behavior [14].…”

Untargeted stable isotope-resolved metabolomics to assess the effect of PI3Kβ inhibition on metabolic pathway activities in a PTEN null breast cancer cell line

“…Untargeted metabolomic profiling has become a valuable tool for the identification of potential metabolic biomarkers in cancer (Struck-Lewicka et al, 2020;Kowalczyk et al, 2021) or the assessment of the response to anti-tumor therapy (Han et al, 2021;Ruan et al, 2022). In order to investigate dynamic pathway alterations in metabolic networks, untargeted stable isotope resolved metabolomics (SIRM) can be applied as a complementary tool (Huang et al, 2014) to evaluate the cellular fate of precursor metabolites and thereby enabling deeper insight into dysregulation of cancer metabolism and individual drug response phenotypes.…”

Predictive analysis of breast cancer response to neoadjuvant chemotherapy through plasma metabolomics

“…Metabolomics, a technology that simultaneously identi es and quanti es numerous metabolites, has unveiled pathophysiological mechanisms [12]. This approach has been used to explore metabolite biomarkers predicting responses to adjuvant therapies.…”