Single-cell sequencing and machine learning reveal the role of dioxin-interacting genes in HCC prognosis and immune microenvironment.

Dioxins are persistent environmental pollutants that bioaccumulate in the food chain, posing significant risks to human health. Despite their low environmental concentrations, dioxins accumulate in tissues, particularly in top predators and humans, reaching levels far exceeding environmental exposure. Although the carcinogenic potential of dioxins is well recognized, the molecular mechanisms driving hepatocellular carcinoma (HCC) development remain poorly understood. To address this gap, single-cell and bulk transcriptomic analyses, combined with machine learning, identified dioxin-interacting differentially expressed genes (DI-DEGs) associated with immune evasion, metabolic reprogramming, and cell cycle regulation in HCC. A prognostic model based on these DI-DEGs stratified patients into high- and low-risk groups, revealing distinct survival outcomes. High-risk patients exhibited increased cell proliferation and aggressive tumor traits, while low-risk patients showed better metabolic stability and immune surveillance. Drug sensitivity analysis revealed differential responses to chemotherapy agents, suggesting therapeutic implications. In vitro experiments demonstrated that environmentally relevant dioxin concentrations promote HCC cell proliferation and migration. Additionally, molecular docking revealed strong binding between dioxin and DI-DEG proteins, suggesting them as therapeutic targets to mitigate dioxin's harmful effects. These findings underscore the pivotal role of dioxin accumulation in liver cancer progression and provide a foundation for personalized treatment strategies and improved prognostic assessment.