Although numerous experimental studies have suggested a significant association between ambient particulate matter (PM) and respiratory damage, the etiological relationship between ambient PM and environmental skin diseases is not clearly understood. Here, we aimed to explore the association between PM and skin diseases through biological big data analysis. Differential gene expression profiles associated with PM and environmental skin diseases were retrieved from public genome databases. The co-expression among them was analyzed using a text-mining-based network analysis software. Activation/inhibition patterns from RNA-sequencing data performed with PM2.5-treated normal human epidermal keratinocytes (NHEK) were overlapped to select key regulators of the analyzed pathways. We explored the adverse effects of PM on the skin and attempted to elucidate their relationships using public genome data. We found that changes in upstream regulators and inflammatory signaling networks mediated by MMP-1, MMP-9, PLAU, S100A9, IL-6, and S100A8 were predicted as the key pathways underlying PM-induced skin diseases. Our integrative approach using a literature-based co-expression analysis and experimental validation not only improves the reliability of prediction but also provides assistance to clarify underlying mechanisms of ambient PM-induced dermal toxicity that can be applied to screen the relationship between other chemicals and adverse effects.

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Air pollutants are in the spotlight because the human body can easily be exposed to them. Among air pollutants, the particulate matter (PM) represents one of the most serious toxicants that can enter the human body through various exposure routes. PMs have various adverse effects and classified as severe carcinogen by International Agency for Research on Cancer. Their physical and chemical characteristics are distinguished by their size. In this review, we summarized the published information on the physicochemical characteristics and adverse effects of PMs on the skin, including carcinogenicity. Through comparisons of biological networks constructed from relationships discussed in the previous scientific publications, we show it is possible to predict skin cancers and other disorders from particle-size-specific signaling alterations of PM-responsive genes. Our review not only helps to grasp the biological association between ambient PMs and skin diseases including cancer, but also provides new approaches to interpret chemical-gene-disease associations regarding the adverse effects of these heterogeneous particles.

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