Network Toxicology, Machine Learning and Molecular Docking Uncover Propylparaben’s Potential Role in Estrogen Receptor-Positive Breast Cancer via PIK3R1

Abstract Background Propylparaben (PP), a preservative in cosmetics, food, and pharmaceuticals, is a potential endocrine disruptor. However, the molecular mechanisms linking PP exposure to estrogen receptor-positive (ER⁺) breast cancer (BC) remain unclear. Methods This study integrated network toxicology with The Cancer Genome Atlas (TCGA) data to identify carcinogenic targets of PP. Protein-protein interaction (PPI) networks were constructed, and machine learning algorithms identified core genes. Diagnostic performance was evaluated by Receiver Operating Characteristic (ROC) analysis. Mendelian Randomization (MR) assessed causal links between core genes and BC risk. Molecular docking verified the binding affinity between PP and PIK3R1. Immune infiltration was analyzed using single-sample Gene Set Enrichment Analysis. Results We identified 50 candidate genes related to BC. PPI analysis revealed 20 key genes, and machine learning narrowed it to 6 core genes. ROC analysis showed excellent diagnostic performance (AUC > 0.90). MR analysis showed that decreased PIK3R1 expression significantly increased BC risk (OR = 0.869, p = 0.017). Molecular docking confirmed strong binding between PP and PIK3R1. Immune analysis suggested a correlation between PIK3R1 expression and immune cell abundance. Conclusion PP may promote ER⁺ BC progression by binding to and suppressing PIK3R1, suggesting a potential carcinogenic effect, warranting further investigation in cohort studies.