Gut Microbiome and Hepatic Transcriptomic Determinants of HCC Development in Mice with Metabolic Dysfunction-Associated Steatohepatitis.

Abstract

Purpose: Hepatocellular carcinoma (HCC) related to metabolic dysfunction-associated steatotic liver disease (MASLD) is often diagnosed at a late stage, and its incidence is increasing. Predictive biomarkers are therefore needed to identify individuals at high risk of HCC. We aimed to characterize the gut microbiome and hepatic transcriptome associated with HCC development in female mice with hepatocyte-deletion of Pten (HepPten ^-). These mice present with large variations in HCC development, making them a powerful model for biomarker discovery.

Methods & results: Sequencing of stool 16S and hepatic RNA was performed on a first set of mice. Among all liver histology parameters measured, the strongest association with microbiome composition changes was with the number of tumors detected at necropsy, followed by inflammation. The gut microbiome of mice with more than 2 tumors was enriched with Lachnospiraceae UCG and depleted of Palleniella intestinalis and Odoribacter. In contrast, hepatic transcriptomic changes were most strongly associated with tumor burden, followed by liver fibrosis. The 840 differentially expressed genes correlating with tumor burden were enriched in leukocyte extravasation and interleukin 10 receptor A (IL10RA) pathways. In addition, the abundance of Spp1-high epithelial cells is correlated with tumor burden. Association between tumor number and depletion of Palleniella intestinalis, and between tumor burden and circulating levels of C-X-C motif chemokine ligand 13 (CXCL13) and stem cell factor (SCF), was further validated in an independent set of mice.

Conclusion: We identified microbiome components contributing to liver carcinogenesis by inducing inflammation, and changes in hepatic gene expression and hepatic cells distribution that contribute to tumor growth. Such information can be highly valuable for the development of new prevention strategies as well as of new biomarkers for risk modeling in HCC.