Mesoporous High-Entropy Oxides Nanoplatform Decodes Paired-Plasma Metabolic Fingerprinting of Pancreatic Cancer.

Accurate detection of small molecule metabolites in vivo is critical for rapid screening of disease biomarkers and health monitoring. Matrix-assisted laser desorption/ ionization mass spectrometry (MALDI-MS) has emerged as a promising platform for metabolic profiling, but its capability is hindered by the limited light absorption and energy transfer of conventional matrix materials. In this work, a high-efficiency metabolic detection platform based on high-entropy oxide particles (mHEO) with an interconnected mesoporous structure and tailored compositions is established. Owing to their abundant active sites and excellent light utilization efficiency, the mHEO particles show significantly improved photothermal and photochemical properties with an eightfold localized enhanced electromagnetic field and higher surface temperatures (616 °C) than nonporous HEOs (336 °C). As a result, MALDI-MS based on the mHEO matrix exhibits high sensitivity, good reproducibility (Coefficient of Variation < 10%), and ultralow detection limits with 1-3 orders of magnitude lower than their endogenous concentrations. Furthermore, the mHEO-based MALDI-MS platform is applied to analyze paired arterial/venous blood samples from pancreatic cancer (PC) patients with the assistance of machine learning. Four tumor microenvironment-associated metabolites are identified as a potential biomarker panel of PC, achieving a robust pancreas-venous plasma classification, which allows the timely screening and targeted treatment of PC.