Identifying Shared Diagnostic Genes and Underlying Mechanisms Between Skin Exposure to Air Pollution and Skin Barrier Damage: A Bioinformatics Approach

Abstract

Air pollution poses a significant threat to skin health, contributing to inflammation, aging, and disruption of the epidermal barrier. This study aims to identify genes associated with skin exposure to air pollution and skin barrier damage, and to discover potential biomarkers for these effects. Datasets related to air pollution-exposed skin (PS) and skin barrier damage (SD) were obtained from the Gene Expression Omnibus (GEO) database. GO and KEGG enrichment analyses were initially performed on both datasets. Through functional enrichment analysis, protein–protein interaction (PPI) network construction, and the application of two machine learning algorithms, we identified 140 common genes and two key diagnostic genes, FOSL1 and TKT. Receiver operating characteristic (ROC) curve analysis was used to validate the PS and SD datasets, achieving optimal area under the curve (AUC) values. Further investigation of FOSL1 and TKT via Gene Set Enrichment Analysis (GSEA) and immune cell infiltration analysis explored their roles in PS and SD conditions. Additionally, a skin model simulating air pollution exposure using particulate matters (PMs) was developed. RT-qPCR results showed that as the concentration of PMs increased, genes related to skin barrier damage were activated. The reliability of FOSL1 and TKT was confirmed through both RT-qPCR and Western Blot analyses.