Abstract 6523: A multi-omics analysis correlating intratumoral microbial abundance with methylation patterns in hepatocellular carcinoma

Abstract Background: Liver Hepatocellular Carcinoma (LIHC) is estimated to be the fourth leading cause of cancer worldwide. LIHC is often aggressive, as most patients are diagnosed at advanced stages. Our study aims to understand the molecular mechanisms driving the development and progression of LIHC. The microbiome has been hypothesized to contribute to many diseases, including cancer. Although microbes reside primarily within the gut, they may translocate to the liver by the gut-liver axis. Such microbes may produce metabolites that can regulate epigenetics. DNA methylation influences tumor progression by silencing tumor suppressors and activating oncogenes. Using the theoretical framework of metabolite interactions, we explore the correlation between microbe abundance and methylation patterns to expand research on the pathogenesis of LIHC. Methods: We analyzed sequencing data for 376 primary LIHC tumor samples and 85 solid tissue normal samples from The Cancer Genome Atlas (TCGA). Using the PathoScope 2.0 framework, we successfully extracted microbial read counts from 446 samples. Microbial contamination correction was performed by three methods: sequencing date, sequencing plate, and overall microbial abundance. Bacterial species identified as contaminants were excluded from downstream analysis. Differential abundance analysis was conducted to identify bacteria species with altered abundance between normal and tumor samples. We used the Kruskal-Wallis test with a Bonferroni-adjusted p-value threshold of 0.05. Methylation data was downloaded from TCGA and filtered by a panel of genes relevant to LIHC oncogenesis. Methylation score disparities between cancer and normal tissues were assessed by the Kruskal-Wallis test. We employed Spearman's rank correlation to identify associations between microbial counts and methylation scores. Results: We identified 27 differentially expressed microbes and 533 differentially methylated sites in LIHC tissue (p-value < 0.05). Between these microbes and methylation sites, we found 58 statistically significant correlations between the microbial abundance and methylation scores (p-value < 0.05). Notably, species involved in these correlations include the pathogenic E. coli O26:H11 str. 11368 and E. coli UMN026 and the probiotic Ligilactobacillus salivarius. Additional findings indicate significant microbial and methylation correlations, with further analysis underway to integrate these findings with clinical variables including cancer stage and alcohol consumption history. Additionally, microbial metabolic pathways are profiled using the HUMAnN 3.0 pipeline to provide insights into the potential mechanisms by which the microbiome may influence methylation. Further research is needed to elucidate the specific pathways mediating epigenetic modifications induced by the bacterial microbiome. Citation Format: Tianyi Chen, Annie N. Do, Suravi Bajaj, Erin Jang, Rishi Yalamarty, Pranava Gande, Kevin Zhang, Ellie Sang, Wei Tse Li, Jessica Wang-Rodriguez, Weg Ongkeko. A multi-omics analysis correlating intratumoral microbial abundance with methylation patterns in hepatocellular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6523.