Significance Breast cancer (BrCa) is the most common cancer worldwide, and high-performance metabolic analysis is emerging in diagnosis and prognosis of BrCa. Here, we used nanoparticle-enhanced laser desorption/ionization mass spectrometry to record serum metabolic fingerprints of BrCa in seconds, achieving high reproducibility and low consumption of direct serum detection. Our analytical method, combined with the aid of machine learning algorithms, was demonstrated to provide high diagnostic efficiency with accuracy of 88.8% and desirable prognostic prediction ( P < 0.005). Furthermore, seven metabolic biomarkers differentially enriched in BrCa serum and their related pathways were identified. Together, our findings provide a tool to characterize BrCa and highlight certain metabolic signatures as potential diagnostic and prognostic factors of diseases including but not limited to BrCa.

Mass spectrometry (MS) promises small-metabolite profiling as atool of the future and calls for the comprehensive understanding of key procedures to enhance its capability. Herein, we studied cation adduction and fragmentation of small metabolites by ac ombination of theoretical and experimental approaches,via nanoparticle-assisted laser desorption/ ionization (LDI)-MS and MS/MS.W ec alculated the energies of adduction conformers and atomic bond orders to establish the rules of cation-metabolite affinity and multiple cation adductions in charge transfer.F urther,w ed emonstrated the reaction paths of adducted ions and mapped the potential energy surfaces to characterizethe loss of given groups during fragmentation. Finally,w es uccessfully controlled metabolite fragmentation by selected and multiple adductions to enhance the atomic/fragment coverage as defined for metabolite identification toward profiling.C onsidering the success of MS in the analysis of large biomolecules,o ur work may have an impact and guide to advanced analysis of small metabolites.