1. Research Aims
This study aims to:
1.1 Characterize the dynamic evolution of blood proteome and metabolome profiles across HBV infection stages, including chronic infection, functional cure, cirrhosis and hepatocellular carcinoma (HCC), while identifying core regulatory targets.
1.2 Evaluate whether multi-omics signatures can improve early detection of HCC and cirrhosis beyond current limitations of AFP and ultrasound-based screening.
1.3 Investigate key molecular drivers of HBV-related liver disease progression through systematic validation.
1.4 Examine demographic factors influencing clinical outcomes in liver disease patients using prospective cohort data.
2. Research Objectives
The specific objectives are:
2.1 To construct a comprehensive multi-omics atlas spanning the HBV disease continuum, integrating proteomic, metabolomic and clinical data.
2.2 To develop and validate predictive models for liver disease progression using advanced machine learning approaches.
2.3 To identify and verify blood-based biomarkers suitable for clinical translation in HCC screening and HBV cure monitoring.
3. Scientific Rationale
3.1 Clinical Need:
Chronic HBV infection affects 290 million people worldwide, with current functional cure rates remaining suboptimal.HCC diagnosis frequently occurs at advanced stages due to inadequate early detection methods.
3.2 Methodology:
* The study will analyze 4,000 HBV/cirrhosis/HCC cases from UK Biobank, including:High-throughput plasma proteomics (Olink 1536-plex platform)!Serum metabolomics profiling (LC-MS/MS)!Comprehensive clinical records
* Advanced bioinformatics and AI approaches will be applied for:Multi-omics data integration!Pathway and network analysis!Predictive model development
3.3 Validation Strategy:Mechanistic studies will employ CRISPR-based gene editing and animal models!Clinical validation will utilize independent prospective!cohorts with cost-effectiveness evaluation
