Serological Exosome Metabolic Biopsy of Hepatocellular Carcinoma via Designed Core-Shell Nanoparticles.

Abstract

Exosome metabolite-based liquid biopsy is a promising strategy for large-scale application in practical clinics toward precise medicine. Given the current challenges in successive isolation and analysis of exosomes and their metabolites in this field, we established a low-cost, high-throughput, and rapid platform for serological exosome metabolic biopsy of hepatocellular carcinoma (HCC) via designed core-shell nanoparticles. It starts with the efficient extraction of high-quality serum exosomes and exosome metabolic features, based on which significantly obvious sample clusters are observed by unsupervised cluster analysis. The following integration of feature selection and supervised machine learning enables the identification of six key metabolites and achieves high-performance prediction between HCC, liver cirrhosis, and healthy controls. Specifically, both sensitivity and accuracy achieve 100% among any pairwise intergroup discrimination in a blind test. The quality and reliability of six key metabolites are further evaluated and validated by using different machine learning algorithms and pathway exploration. Our platform contributes to the future growth of new liquid biopsy technologies for precision diagnosis and real-time monitoring of HCC, among other conditions.