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Shaping a pro-carcinogenic hepatic microenvironment by TCDD: An integrated approach combining network toxicology, machine learning, molecular docking, molecular dynamics and experimental validation.

Ecotoxicology and environmental safety 2026 Vol.309() p. 119708

Sun Y, Ma J, Zhou X, Cao R, Su M, Li S, Lu X, Peng D, Zhang J, Peng C, Lu J

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The increasing prevalence of environmental contaminants has raised concerns regarding their potential contribution to hepatic dysfunction and associated diseases.

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APA Sun Y, Ma J, et al. (2026). Shaping a pro-carcinogenic hepatic microenvironment by TCDD: An integrated approach combining network toxicology, machine learning, molecular docking, molecular dynamics and experimental validation.. Ecotoxicology and environmental safety, 309, 119708. https://doi.org/10.1016/j.ecoenv.2026.119708
MLA Sun Y, et al.. "Shaping a pro-carcinogenic hepatic microenvironment by TCDD: An integrated approach combining network toxicology, machine learning, molecular docking, molecular dynamics and experimental validation.." Ecotoxicology and environmental safety, vol. 309, 2026, pp. 119708.
PMID 41529475

Abstract

The increasing prevalence of environmental contaminants has raised concerns regarding their potential contribution to hepatic dysfunction and associated diseases. 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), classified as a Group 1 carcinogen and the most toxic congener of dioxins, has been implicated in adverse hepatic outcomes. However, the molecular mechanisms by which TCDD-driven signaling cascades establish a pro-carcinogenic microenvironment in the liver remain insufficiently elucidated. By integrating network toxicology with machine learning, CYP1A2, CYP2C9, and HSP90AB1 were identified as the core targets of TCDD-elicited hepatocellular carcinoma (HCC). Stable complex formation between TCDD and each target, exhibiting low conformational flexibility and robust binding affinity, was revealed through molecular docking and molecular dynamics simulations. Subsequently, TCDD-elicited hepatotoxic effects were predominantly demonstrated by Protein-Protein Interaction (PPI), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and immune infiltration analyses to be mediated via activation of chemical carcinogenesis-receptor signaling pathways, perturbation of lipid metabolism, and disruption of immune microenvironment homeostasis. In male C57BL/6 J mice, TCDD exposure was associated with significantly elevated serum markers indicative of hepatocellular injury, metabolic dysfunction, systemic inflammation, and pre-neoplastic transformation, together with markedly disturbed oxidative-stress indices. Then, histopathological examination revealed disrupted hepatic cords, collagen deposition, lipid accumulation, and widespread apoptosis, further complemented by glycogen metabolic disturbances, enhanced proliferation, and elevated pre-neoplastic biomarkers, which collectively established a pro-carcinogenic hepatic microenvironment. Immunofluorescence results indicated significant promotion of M1 (pro-inflammatory) macrophage polarization and suppression of M2 (anti-inflammatory) phenotypes, resulting in an increased M1/M2 ratio and a pro-inflammatory microenvironment. Consistently, down-regulation of CYP1A2 and CYP2C9 and up-regulation of HSP90AB1 were shown by immunofluorescence/Western blotting/RT-qPCR, impairing signaling networks and immune homeostasis and ultimately leading to the establishment of hepatotoxicity and carcinogenic microenvironments. Collectively, the TCDD "target binding-pathway dysregulation-immune imbalance-pathological damage" cascade has been systematically delineated, providing novel targets and a theoretical framework for therapeutic intervention against pollutant-associated liver diseases.

MeSH Terms

Animals; Polychlorinated Dibenzodioxins; Machine Learning; Molecular Docking Simulation; Male; Mice; Molecular Dynamics Simulation; Liver; Mice, Inbred C57BL; Liver Neoplasms; Environmental Pollutants; Carcinoma, Hepatocellular

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