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

Abstract

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.