Application of untargeted metabolomics for the safety assessment of genetically modified maize (Zea mays L.) and the identification of genetic background-associated biomarkers

Untargeted metabolomics has been increasingly applied in safety assessments of genetically modified (GM) maize. In this study, we propose a stepwise workflow that integrates untargeted UPLC-qTOFMS and targeted GC-TOFMS-based metabolite profiling with multivariate statistics for equivalence assessment of maize samples ( n = 3, biological replicates). Using this workflow, 179 metabolites were identified, and principal component analysis revealed no substantial variation between GM lines and their parental lines. Nine biomarkers associated with genetic background were selected based on three statistical criteria: variable importance in projection (VIP) > 1.3, area under the receiver operating characteristic curve (AUROC) > 0.9, and log 2 fold change (log 2 FC) > 1.5. These biomarkers, including seven fatty acids and two phenolic compounds, were validated through correlation analysis ( r > 0.6208, p < 0.0012) and hierarchical clustering, which supported their biological relevance. This integrative workflow not only demonstrates metabolic equivalence between the GM lines and their parental lines but also establishes a practical platform for the discovery of biomarkers associated with genetic background. Despite limited monoculture samples, this study provides a cornerstone for future large-scale, multiculture GM crop evaluations using the proposed workflow.