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Integrated Membrane Proteomics and Machine Learning Identify HBV-Associated Membrane Proteins as Prognostic Biomarkers for Hepatocellular Carcinoma

ABSTRACT: In this study, we employed two complementary proteomic strategies to identify HBV-associated membrane proteins: (1) classical membrane fractionation coupled with mass spectrometry, and (2) BS2-mediated proximity labeling-utilizing a membrane-targeted esterase from Bacillus subtilis (BS2) to enrich membrane-proximal proteins-followed by mass spectrometry. Both approaches were applied to HBV-negative and HBV-positive tumor cell lines (HepG2 and Hep3B). Subsequently, by integrating HBV‑HCC proteomic data, we identified 98 HBV‑associated membrane‑surface proteins. Through Cox regression analysis and machine‑learning approaches, we screened and determined that multiple proteins such as TMEM192 and POLM were prognostic factors associated with RFS, while multiple proteins such as GLYATL1 and TMEM109 were prognostic factors for OS

ORGANISM(S): Homo Sapiens

SUBMITTER: Shupeng Liu

PROVIDER: PXD072903 | iProX | Thu Jan 08 00:00:00 GMT 2026

REPOSITORIES: iProX

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