Gastric cancer (GC) presents high mortality worldwide
because of
delayed diagnosis. Currently, exosome-based liquid biopsy has been
applied in diagnosis and monitoring of diseases including cancers,
whereas disease detection based on exosomes at the metabolic level
is rarely reported. Herein, the specific aptamer-coupled Au-decorated
polymorphic carbon (CoMPC@Au-Apt) is constructed for the capture of
urinary exosomes from early GC patients and healthy controls (HCs)
and the subsequent exosome metabolic pattern profiling without extra
elution process. Combining with machine learning algorithm on all
exosome metabolic patterns, the early GC patients are excellently
discriminated from HCs, with an accuracy of 100% for both the discovery
set and blind test. Ulteriorly, three key metabolic features with
clear identities are determined as a biomarker panel, obtaining a
more than 90% diagnostic accuracy for early GC in the discovery set
and validation set. Moreover, the change law of the key metabolic
features along with GC development is revealed through making a comparison
among HCs and GC at early stage and advanced stage, manifesting their
monitoring ability toward GC. This work illustrates the high specificity
of exosomes and the great prospective of exosome metabolic analysis
in disease diagnosis and monitoring, which will promote exosome-driven
precision medicine toward practical clinical application.