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September 26, 2025Frontiers in Chemistry2 citationsOpen Access

Polybrominated diphenyl ether profiles in adipose tissues of breast cancer patients and their carcinogenic potential investigation based on network toxicology and molecular docking

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QZQihao ZhaoXLXi LiuHCHaoyi Chen

Key Points

  • BDE-47, BDE-138, BDE-153, BDE-183, and BDE-209 were identified as primary PBDEs accumulating in adipose tissues, highlighting their potential role in breast cancer.
  • Machine learning identified key genes like CASP3, ESR1, and MMP9 as significant in the pathways linking PBDEs to breast cancer, indicating their importance in tumorigenesis.
  • Utilizing gas chromatography-mass spectrometry and molecular docking, the study analyzed adipose tissue samples from 183 breast cancer patients and 145 controls to derive meaningful insights.
  • The findings underline the significance of PBDE mixtures in breast cancer pathogenesis, suggesting potential preventive and therapeutic strategies for affected patients.

Abstract

Introduction Existing epidemiological and experimental evidence have unveiled individual PBDE congeners facilitate the initiation of breast cancer. However, the comprehensive molecular mechanisms by which PBDE mixtures contribute to breast cancer pathogenesis remains poorly understood. This study aims to identify the PBDE congeners that preferentially accumulate in female adipose tissues and to intricate their interactions and key targets and molecular pathways implicated in breast cancer tumorigenesis. Materials and methods Adipose tissue specimens were collected from 183 patients with breast cancer and 145 women with benign breast disease or non breast-related diseases. Adipose PBDEs concentrations were determined by gas chromatograph-mass spectrometer. The ChEMBL, STITCH, GeneCards, OMIM, TCGA-BRCA databases, as well as a protein-protein interaction (PPI) network, were utilized to identify the primary targets of PBDEs and their interactions. Molecular docking was performed using Autodock Vina to validate the binding affinities between chemicals and targets. Functional enrichment analysis was then performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Machine learning strategies were applied to refine core genes involved in pathogenesis of breast cancer. Results BDE-47, BDE-138, BDE-153, BDE-183 and BDE-209 were recognized as the major PBDE congeners accumulated in adipose tissues. The top 20 candidate target genes were enriched for response to chemical stress, gland development, protein ligase binding, lipid and atherosclerosis and chemical carcinogenesis. The intersected genes and pathways between breast cancer and chemical carcinogenesis revealed significant associations with pathways in the PD-1/PD-L1 checkpoint and the HIF-1 signaling pathway. Machine learning strategies nominated CASP3, ESR1, MMP9, PARP1, and PPARG as crucial genes involved in breast cancer pathogenesis, exhibiting high-affinity binding to the major PBDE congeners. Conclusion This integrative network study uncovers a mechanistic framwork linking adipose-accumulated PBDE mixtures to breast cancer pathogenesis. These findings provide insights for preventive and therapeutic interventions against PBDE-associated breast cancer.

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Cite This Study

Zhao et al. (2025) studied this question.

synapsesocial.com/papers/68d6c682b1249cec298b2848https://doi.org/10.3389/fchem.2025.1630283
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