Prioritization of molecular signatures between BDE-209-relevant targets and ulcerative colitis: a network toxicology and bioinformatics analysis

作者
Ling Chen,Hai Zhang,Danli Hu,Haitao Zhang,D Wang,Ziyang Hong,Si Su,Le Chen
出处
期刊:BMC Clinical Pharmacology [BioMed Central]
卷期号:27 (1)
标识
DOI:10.1186/s40360-026-01170-8
摘要

BACKGROUND: Decabromodiphenyl ether (BDE-209) is a widely used flame retardant and persistent environmental contaminant. However, the overlap between predicted BDE-209-related targets and ulcerative colitis (UC)-associated molecular signatures has not been systematically evaluated. METHODS: Public UC transcriptomic datasets and predicted BDE-209-related targets were integrated for comparative bioinformatic analysis. Differential expression analysis, enrichment analysis, machine learning algorithms, protein-protein interaction network analysis, immune infiltration analysis, single-cell RNA sequencing analysis, and molecular docking were performed to prioritize candidate genes associated with BDE-209-related targets and UC-associated signatures. RESULTS: A total of 87 overlapping genes were identified between predicted BDE-209-related targets and UC-associated differentially expressed genes. Functional enrichment analysis showed that these genes were mainly enriched in inflammation- and immune-related pathways. Machine learning analysis and protein-protein interaction network analysis prioritized eight candidate hub genes (IL1B, MMP9, ICAM1, PPARG, VCAM1, AREG, FABP3, and ACSL5). Immune infiltration and single-cell transcriptomic analyses showed that these genes were mainly expressed in immune-related cell populations within UC datasets. Molecular docking analysis suggested potential structural compatibility between BDE-209 and the protein structures encoded by the identified hub genes. CONCLUSIONS: This study identified shared molecular features between predicted BDE-209-related targets and UC-associated transcriptomic signatures. These findings provide a hypothesis-generating resource for future studies investigating the potential relevance of environmental contaminant-related target networks in UC-associated inflammatory contexts.
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