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

backgroundHepatocellular carcinoma (HCC) is a highly prevalent and fatal digestive system malignancy, challenging to treat due to its latent onset and non-specific symptoms in advanced stages. Somatic mutations play a crucial role in hepatocarcinogenesis, with nearly half of HCC patients carrying oncogenic driver mutations such as TP53, CTNNB1, or TERT. In parallel, germline susceptibility variants identified by genome-wide association studies (GWAS) — including loci near TERT, MBOAT7, TM6SF2, and PNPLA3 — reveal inherited predisposition that shapes the molecular landscape for HCC development. Despite recent therapeutic advancements, long-term survival remains suboptimal, necessitating a deeper understanding of its pathogenesis and the identification of precise molecular targets. Traditional genomic studies, such as genome-wide association studies (GWAS), have successfully identified associated variants; however, due to their statistical design, they do not provide direct causal inference, functional supportive analyses, or comprehensive insight into multi-level molecular regulation and tumor microenvironment heterogeneity, serving instead as a critical starting point for subsequent functional and integrative analyses.

methodsTo address these gaps, this study employed an integrated multi-omics approach combining HCC GWAS summary data (FinnGen) with expression (eQTL from GTEx V8), methylation (mQTL), and protein (pQTL from ARIC, UKBPPP, DECODE) quantitative trait loci data. We utilized Summary-data-based Mendelian Randomization (SMR) to infer causal associations between molecular traits and HCC risk, prioritizing candidates with higher clinical translation potential. To refine SMR-based prioritization of candidate genes, bulk transcriptome sequencing and ELISA-based quantification were performed as complementary analyses on peripheral blood samples from 10 HCC patients and 10 healthy controls. Following SMR-based gene prioritization, bulk transcriptome and spatial transcriptomic analyses were first used to refine candidate selection and guide subsequent quantification, thereby avoiding unnecessary assays and optimizing the use of clinical samples and research resources. These analyses aimed to assess whether expression changes were directionally consistent with eQTL and pQTL effects, providing supportive—rather than confirmatory—evidence for the inferred genetic associations. Spatial transcriptomics was applied to HCC tissue sections to map region-specific expression patterns of candidate genes. Finally, publicly available single-cell RNA sequencing (scRNA-seq) data was analyzed to resolve cell composition changes, cell-type-specific expression, and intercellular communication networks within the HCC tumor microenvironment.

resultsMulti-omics SMR analysis identified numerous loci causally associated with HCC risk, with eQTL SMR revealing enrichment in critical cancer pathways (“Signal transduction,” “Cancer: overview,” “Immune system”). A robust and replicated causal signal for proteins was found on chromosome 19 across three independent pQTL cohorts, with a secondary signal on chromosome 2. The intersection of mQTL, eQTL, and pQTL SMR analyses yielded a core set of 16 candidate genes. Peripheral blood transcriptome profiling showed a clear separation between HCC and controls, with 13 of these 16 genes (e.g., LY9, ST6GAL1, SHMT1) significantly differentially expressed. ELISA validated elevated protein levels of ST6GAL1, PSMB1, LY9, and JUND, and decreased SOD4 in HCC patients. Spatial transcriptomics revealed significant intra-tumoral heterogeneity and distinct, localized expression patterns for genes like ST6GAL1, LGALS1, and JUND. Single-cell RNA sequencing unveiled shifts in cell type composition (e.g., increased Cytotoxic CD4 + T cells and MDSCs, decreased hepatocytes in tumors), cell-type specific expression of candidate genes, and complex intercellular communication networks.

conclusionBy integrating germline (GWAS-based) and somatic evidence, this study provides a comprehensive view of HCC pathogenesis. This integrated strategy successfully identified a core set of genes and proteins with potential causal links to HCC, elucidating their functional convergence in cancer biology. These findings offer novel molecular insights and candidate targets for precise diagnosis, prognostic assessment, and targeted therapy of HCC, laying a solid foundation for future translational research.