Efficient metabolic fingerprinting profiling of extracellular vesicles for precise cancer diagnosis and treatment monitoring.

Breast cancer is the most prevalent cancer among women globally, underscoring the need for accurate prognostic and predictive biomarkers. Extracellular vesicles (EVs), carrying bioactive molecules such as proteins and miRNAs, have emerged as promising candidates for non-invasive liquid biopsy. However, their metabolic profiles remain underexplored. Here, we present a hybrid gold matrix (AuM), composed of gold nanospheres and nanorods, for EVs metabolome profiling via matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS). The AuM enhances ionization efficiency through its large surface area and anisotropic optical properties, enabling sensitive analysis with just 100 nL of serum and rapid processing time. Using this platform, we identified six potential metabolic biomarkers distinguishing early-stage breast cancer (BC) patients from healthy donors (HD). For treatment monitoring, serum-derived EVs from breast cancer mouse models treated with doxorubicin hydrochloride were analyzed, and a three-metabolite panel achieved 100 % accuracy in evaluating therapeutic response. This study reveals the metabolic heterogeneity of EVs and demonstrates the utility of EVs metabolomics in both diagnosis and treatment monitoring. Our findings bridge the gap between EVs-based laboratory research and clinical application, offering a promising tool for advancing precision medicine in breast cancer management.