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September-2026 Volume 32 Issue 3

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Article Open Access

Air pollution and hepatocellular carcinoma: Integrated network toxicology, machine learning, molecular docking and multiomics analysis

  • Authors:
    • Xiaorong Wang
    • Huihao Qin
    • Jing Jing
    • Yun Liu
    • Lei Guo
    • Hongquan Liu
    • Wei Zhang
  • View Affiliations / Copyright

    Affiliations: The Third Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, P.R. China, Department of Radiology, Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210028, P.R. China, Department of Neurology, Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210028, P.R. China, Department of Interventional Radiology, Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210028, P.R. China
    Copyright: © Wang et al. This is an open access article distributed under the terms of Creative Commons Attribution License [CC BY_NC 4.0].
  • Article Number: 382
    |
    Published online on: June 30, 2026
       https://doi.org/10.3892/ol.2026.15737
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Abstract

Air pollution is closely related to the incidence and prognosis of hepatocellular carcinoma (HCC); however, the mechanisms remain elusive. Therefore, using network toxicology and molecular docking analysis, the present study aimed to identify candidate molecular pathways potentially linking 10 air pollutants (APs) and HCC through machine learning and multi‑omics analyses. Using weighted gene co‑expression network analysis, differential gene expression analysis and multiple online databases, AP‑related and HCC‑related genes (AP‑HCC‑Gs) were obtained for functional and pathway enrichment analyses. A total of 101 combinations of 10 machine learning algorithms were applied to construct an AP‑related and HCC‑related prognostic signature (AP‑HCC‑PS), and three machine learning algorithms were used to screen AP‑related and HCC‑related diagnostic markers (AP‑HCC‑DMs). Based on the AP‑HCC‑PS and AP‑HCC‑DMs, molecular docking and nomogram analyses were conducted. The AP‑HCC‑PS and AP‑HCC‑DMs were intersected, and single‑cell RNA, reverse transcription‑quantitative PCR, small interfering RNA, wound healing and immune cell infiltration analyses were performed. A total of 43 AP‑HCC‑Gs were identified, which were mainly involved in cell proliferation, oxidative stress and the immune response. The AP‑HCC‑PS (seven AP‑HCC‑Gs) and two AP‑HCC‑DMs showed good prognostic and diagnostic value. Molecular docking analysis suggested the potential binding affinity between these targets and APs. The hub gene proteasome 20S subunit β5 (PSMB5) was highly expressed in regulatory T cells (Tregs) and positively associated with Treg infiltration. AP exposure may be associated with HCC incidence through two AP‑HCC‑DMs and with prognosis through seven AP‑HCC‑Gs, with PSMB5 potentially representing an important candidate gene in HCC progression. The present study provided a multi‑dimensional analytical framework for identifying candidate genes and pathways potentially linking APs to HCC, thereby advancing hypotheses regarding environmental carcinogenesis.

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Copy and paste a formatted citation
Spandidos Publications style
Wang X, Qin H, Jing J, Liu Y, Guo L, Liu H and Zhang W: Air pollution and hepatocellular carcinoma: Integrated network toxicology, machine learning, molecular docking and multiomics analysis. Oncol Lett 32: 382, 2026.
APA
Wang, X., Qin, H., Jing, J., Liu, Y., Guo, L., Liu, H., & Zhang, W. (2026). Air pollution and hepatocellular carcinoma: Integrated network toxicology, machine learning, molecular docking and multiomics analysis. Oncology Letters, 32, 382. https://doi.org/10.3892/ol.2026.15737
MLA
Wang, X., Qin, H., Jing, J., Liu, Y., Guo, L., Liu, H., Zhang, W."Air pollution and hepatocellular carcinoma: Integrated network toxicology, machine learning, molecular docking and multiomics analysis". Oncology Letters 32.3 (2026): 382.
Chicago
Wang, X., Qin, H., Jing, J., Liu, Y., Guo, L., Liu, H., Zhang, W."Air pollution and hepatocellular carcinoma: Integrated network toxicology, machine learning, molecular docking and multiomics analysis". Oncology Letters 32, no. 3 (2026): 382. https://doi.org/10.3892/ol.2026.15737
Copy and paste a formatted citation
x
Spandidos Publications style
Wang X, Qin H, Jing J, Liu Y, Guo L, Liu H and Zhang W: Air pollution and hepatocellular carcinoma: Integrated network toxicology, machine learning, molecular docking and multiomics analysis. Oncol Lett 32: 382, 2026.
APA
Wang, X., Qin, H., Jing, J., Liu, Y., Guo, L., Liu, H., & Zhang, W. (2026). Air pollution and hepatocellular carcinoma: Integrated network toxicology, machine learning, molecular docking and multiomics analysis. Oncology Letters, 32, 382. https://doi.org/10.3892/ol.2026.15737
MLA
Wang, X., Qin, H., Jing, J., Liu, Y., Guo, L., Liu, H., Zhang, W."Air pollution and hepatocellular carcinoma: Integrated network toxicology, machine learning, molecular docking and multiomics analysis". Oncology Letters 32.3 (2026): 382.
Chicago
Wang, X., Qin, H., Jing, J., Liu, Y., Guo, L., Liu, H., Zhang, W."Air pollution and hepatocellular carcinoma: Integrated network toxicology, machine learning, molecular docking and multiomics analysis". Oncology Letters 32, no. 3 (2026): 382. https://doi.org/10.3892/ol.2026.15737
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