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Background <p>This study explores the potential molecular mechanisms underlying the pathogenesis of gastric cancer induced by plasticizers, with particular emphasis on the interactions between plasticizers and key genes and signaling pathways. Methods: Machine learning algorithms were applied to multiple public datasets to identify potential target genes associated with gastric cancer. The interactions between plasticizers and target proteins were explored using network toxicology and molecular docking. Results: Our analysis identified 17 gastric cancer-related target genes associated with plasticizer exposure. Through machine learning optimization, the RF + Lasso model demonstrated superior performance (AUC: 0.816) and identified six core genes: CPB1, AKR1C1, GRM2, CA2, MMP7, and TDO2. Differential expression analysis revealed upregulation of GRM2, MMP7, and TDO2, alongside downregulation of CPB1, AKR1C1, and CA2 in gastric cancer tissues. Molecular docking confirmed specific binding interactions between plasticizers and target proteins, with binding energies ranging from − 6.4 to -11.2&#xa0;kcal/mol. Conclusion: Our findings indicate that plasticizers may drive gastric cancer tumorigenesis by modulating key genes and signaling pathways. The molecular docking results indicate that there may be specific binding interactions between the target protein and plasticizers. This research establishes a platform for further probing the impact of plasticizers on gastric cancer development, offering conceptual guidance for forthcoming functional validation and targeted therapy development.</p>

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Unveiling the mechanistic links between plasticizers and gastric cancer via network toxicology and molecular docking approaches

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月2日

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  • Rui Guo,
  • Weifeng Ma,
  • Zhi Ren,
  • Dapeng Li,
  • Meng Wang,
  • Mingtao Xu,
  • Hailun Zheng,
  • Xiquan Ke

摘要

Background

This study explores the potential molecular mechanisms underlying the pathogenesis of gastric cancer induced by plasticizers, with particular emphasis on the interactions between plasticizers and key genes and signaling pathways. Methods: Machine learning algorithms were applied to multiple public datasets to identify potential target genes associated with gastric cancer. The interactions between plasticizers and target proteins were explored using network toxicology and molecular docking. Results: Our analysis identified 17 gastric cancer-related target genes associated with plasticizer exposure. Through machine learning optimization, the RF + Lasso model demonstrated superior performance (AUC: 0.816) and identified six core genes: CPB1, AKR1C1, GRM2, CA2, MMP7, and TDO2. Differential expression analysis revealed upregulation of GRM2, MMP7, and TDO2, alongside downregulation of CPB1, AKR1C1, and CA2 in gastric cancer tissues. Molecular docking confirmed specific binding interactions between plasticizers and target proteins, with binding energies ranging from − 6.4 to -11.2 kcal/mol. Conclusion: Our findings indicate that plasticizers may drive gastric cancer tumorigenesis by modulating key genes and signaling pathways. The molecular docking results indicate that there may be specific binding interactions between the target protein and plasticizers. This research establishes a platform for further probing the impact of plasticizers on gastric cancer development, offering conceptual guidance for forthcoming functional validation and targeted therapy development.