Integrating Network Toxicology, Machine Learning, Gut Microbiome Analysis, and Structural Analysis to Reveal the Molecular Mechanisms Linking PFOA and PFOS Exposure to Age-Related Macular Degeneration

Perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS) are persistent environmental contaminants with potential adverse effects on human health, but their involvement in age-related macular degeneration (AMD) remains poorly understood. Here, we integrated transcriptomics, network toxicology, machine learning, Mendelian randomization (MR), gut microbiome analysis, molecular docking, and molecular dynamics simulations to investigate potential molecular targets linking PFOA/PFOS exposure to AMD. A total of 97 potential PFOA/PFOS-AMD targets were identified, and integrative machine learning analysis prioritized SLC1A4, ADAM17, EGFR, and MAPK10 as core genes. MR analysis suggested associations between genetically predicted SLC1A4 and ADAM17 expression and AMD, while molecular docking and molecular dynamics simulations suggested potential interactions between PFOA/PFOS and these candidate targets. Functional analyses implicated inflammatory responses, oxidative stress, and amino acid metabolic dysregulation. GutMGene analysis identified gut bacteria and microbial metabolites potentially linked to inflammatory genes. Collectively, these findings prioritize SLC1A4 and ADAM17 as candidate targets and provide a hypothesis-generating framework for experimental studies of the possible ocular effects of PFOA/PFOS.