Multi-Omics SMR and Experimental supportive analyses Decipher Causal Drivers Hepatocellular Carcinoma

Hepatocellular 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. Overall design: To 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.