Multi-omics SMR and experimental supportive analyses decipher causal drivers hepatocellular carcinoma

Area of Science:

• Genomics
• Oncology
• Molecular Biology

Background:

• Hepatocellular carcinoma (HCC) is a deadly cancer with limited treatment options.
• Somatic mutations and inherited genetic variants contribute to HCC development.
• Current genomic studies identify variants but lack causal inference and functional insights.

Purpose of the Study:

• To integrate multi-omics data for identifying causal molecular drivers of HCC.
• To explore the role of germline and somatic variations in hepatocarcinogenesis.
• To discover novel molecular targets for HCC diagnosis and therapy.

Main Methods:

• Integrated multi-omics analysis combining GWAS, eQTL, mQTL, and pQTL data.
• Summary-data-based Mendelian Randomization (SMR) to infer causal associations.
• Bulk transcriptome, ELISA, spatial transcriptomics, and scRNA-seq for validation and tumor microenvironment analysis.

Main Results:

• Identified numerous loci causally associated with HCC risk, enriched in cancer pathways.
• Discovered a core set of 16 candidate genes with potential causal links to HCC.
• Revealed significant intra-tumoral heterogeneity, altered cell composition, and intercellular communication networks.

Conclusions:

• Integrated germline and somatic evidence provides a comprehensive view of HCC pathogenesis.
• Identified novel genes and proteins with potential causal roles in HCC.
• Findings offer candidate targets for precise diagnosis, prognosis, and targeted HCC therapy.