Bisphenol F (BPF) has gained prominence as an alternative to bisphenol A (BPA) in various manufacturing applications, yet being detected in diverse environments and posed potential public health risk. This research aims to elucidate the putative toxic targets and underlying molecular mechanisms of prostate injury induced by exposure to BPF through multi-level bioinformatics data, integrating network toxicology and molecular docking. Systematically leveraging multilevel databases, we determined 276 targets related to BPF and prostate injury. Subsequent screenings through STRING and Cytoscape tool highlighted 27 key targets, including BCL2, HSP90AA1, MAPK3, ESR1, and CASP3. GO and KEGG enrichment analyses demonstrated enrichment of targets involved in apoptosis, abnormal hormonal activities, as well as cancer-related signal transduction cascades, ligand-receptor interaction networks, and endocrine system signaling pathways. Molecular docking simulations conducted via Autodock corroborated high-affinity binding interaction between BPF and key targets. The results indicate that BPF exposure can contribute to the initiation and progression of prostate cancer and prostatic hyperplastic by modulating apoptosis and proliferation, altering nerve function in blood vessel endothelial cells, and disrupting androgen metabolism. This study offers theoretical underpinnings for comprehending the molecular mechanisms implicated in BPF-elicited prostatic toxicity, while concomitantly establishing foundational framework for the development of prophylactic and therapeutic strategies for prostatic injuries related to polycarbonate and epoxy resin plastics incorporated with BPF, as well as environments afflicted by elevated levels of these compounds.
Keyphrases
- molecular docking
- prostate cancer
- benign prostatic hyperplasia
- radical prostatectomy
- molecular dynamics simulations
- signaling pathway
- oxidative stress
- health risk
- endothelial cells
- healthcare
- endoplasmic reticulum stress
- big data
- machine learning
- pi k akt
- metabolic syndrome
- binding protein
- epithelial mesenchymal transition
- transcription factor
- human health
- deep learning
- induced apoptosis
- artificial intelligence
- molecular dynamics
- network analysis
- high glucose
- adverse drug