Abstract

Hepatocellular carcinoma (HCC) is a prevalent primary liver cancer often associated with chronic hepatitis B virus infection (CHB) and liver cirrhosis (LC), underscoring the critical need for biomarker discovery to improve patient outcomes. Emerging as a promising avenue for biomarker development, proteomic technology leveraging liquid biopsy from small extracellular vesicles (sEV) offers new insights. Here, we evaluated various methods for sEV isolation and identified polysaccharide chitosan (CS) as an optimal approach. Subsequently, we employed optimized CS-based magnetic beads (Mag-CS) for sEV separation from serum samples of healthy controls, CHB, LC, and HBV-HCC patients. Leveraging data-independent acquisition mass spectrometry coupled with machine learning, we uncovered potential vesicular protein biomarker signatures (KNG1, F11, KLKB1, CAPNS1, CDH1, CPN2, NME2) capable of distinguishing HBV-HCC from CHB, LC, and non-HCC conditions. Collectively, our findings highlight the utility of Mag-CS-based sEV isolation for identifying early detection biomarkers in HBV-HCC.

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