Diagnosis and prognosis of breast cancer by high-performance serum metabolic fingerprints

High-performance metabolic analysis is emerging in the diagnosis and prognosis of breast cancer (BrCa). Still, advanced tools are in demand to deliver the application potentials of metabolic analysis. Here, we used fast nanoparticle-enhanced laser desorption/ionization mass spectrometry (NPELDI-MS) to record serum metabolic finger-prints (SMFs) of BrCa in seconds, achieving high reproducibility and low consumption of direct serum detection without treatment. Subsequently, machine learning of SMFs generated by NPELDI-MS functioned as an efficient readout to distinguish BrCa from non-BrCa with an area under the curve of 0.948. Furthermore, a metabolic prognosis scoring system was constructed using SMFs with effective prediction performance toward BrCa (P < 0.005). Finally, we identified a biomarker panel of seven metabolites that were differentially enriched in BrCa serum and their related pathways. Together, our findings provide an efficient serum metabolic tool to characterize BrCa and highlight certain metabolic signatures as potential diagnostic and prognostic factors of diseases including but not limited to BrCa.

Automated summary

High-performance metabolic analysis using fast nanoparticle-enhanced laser desorption/ionization mass spectrometry (NPELDI-MS) was used to record serum metabolic fingerprints (SMFs) of breast cancer patients. The SMFs were used for machine learning to distinguish breast cancer from non-breast cancer, and also for constructing a metabolic prognosis scoring system. Additionally, a panel of differentially enriched metabolites and related pathways in breast cancer serum were identified.