We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Pollutants as Hidden Triggers: A Computational View on Oral and Lung Cancer Development
Summary
This computational review maps how environmental pollutants including microplastics induce oxidative stress, DNA damage, and epigenetic changes that drive oral and lung cancer development, applying tools such as molecular docking, QSAR models, and multi-omics analysis to identify pollutant-gene interactions. As microplastics accumulate in lung and oral tissues and carry co-contaminants such as PAHs and heavy metals, understanding their role in cancer pathways is increasingly important for public health risk assessment.
Environmental pollutants such as particulate matter (PM), gaseous pollutants, polycyclic aromatic hydrocarbons (PAHs), heavy metals, occupational toxins, biological agents, and microplastics disrupt critical molecular mechanisms. They induce oxidative stress, DNA damage, inflammation, mutations, and epigenetic modifications, ultimately affecting cancer-associated genes and pathways. While the association between environmental pollutants and lung cancer is well-established, their role in oral cancer remains underexplored, despite the oral cavity being a primary site of exposure. This review highlights how computational approaches are being applied to address this gap. Multi-omics analyses provide insight into pollutant-driven molecular changes. While tools such as molecular docking, quantitative structure–activity relationship (QSAR) models, and machine learning enable toxicity prediction and pollutant–protein interaction studies. We also discuss how epidemiological and statistical models complement these computational approaches to reveal population-level risks. Finally, we discuss major challenges, including data heterogeneity, limited model validation, and integration with biological evidence, and outline future directions such as identification of cross-cancer mechanisms using AI-driven multi-omics integration and a focus on high-burden pollutants like PM2.5, PAHs, and microplastics. This computational perspective holds promise for improving early detection, guiding exposure risk assessment, and informing evidence-based public health strategies for cancer prevention.