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Objective <p>This study investigates the impact of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) on the pathogenesis of gastric cancer and its associated molecular mechanisms, particularly the interaction between TCDD and key targets and pathways.</p> Methods <p>We employed various machine learning techniques and online databases to perform differential expression analysis on bulk gastric cancer sequencing data and organoid sequencing data to identify target genes and pathways related to TCDD and gastric cancer. A risk prediction model based on the expression levels of key intersection target genes was constructed. Network toxicology and molecular docking techniques were used to study the binding of TCDD to target proteins.</p> Results <p>A total of 24 genes were identified as potential target genes related to TCDD-induced gastric cancer. Machine learning analysis identified 5 core target genes as key intersection target genes of TCDD-induced gastric cancer, with the chemical carcinogenesis-receptor activation pathway, p53 signaling pathway, and IL-17 signaling pathway being the key pathways. Molecular docking revealed specific binding effects and binding sites between TCDD and intersection target proteins.</p> Conclusion <p>This study suggests that TCDD may affect the pathogenesis of gastric cancer by targeting specific genes and pathways. Molecular docking simulations indicate that there is a significant binding specificity effect between TCDD and target proteins, which is key to gastric cancer development.</p> Graphical abstract <p></p>

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Evaluating the carcinogenic potential and molecular mechanisms of 2,3,7,8-tetrachlorodibenzo-p-dioxin in the human stomach using organoids and bulk sequencing data: a multi-machine learning approach combined with computational simulation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月26日

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  • Youfu Tian,
  • Dede Ma,
  • Ke Yan,
  • Bing Xiao,
  • Jie Liu,
  • Miao Tan

摘要

Objective

This study investigates the impact of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) on the pathogenesis of gastric cancer and its associated molecular mechanisms, particularly the interaction between TCDD and key targets and pathways.

Methods

We employed various machine learning techniques and online databases to perform differential expression analysis on bulk gastric cancer sequencing data and organoid sequencing data to identify target genes and pathways related to TCDD and gastric cancer. A risk prediction model based on the expression levels of key intersection target genes was constructed. Network toxicology and molecular docking techniques were used to study the binding of TCDD to target proteins.

Results

A total of 24 genes were identified as potential target genes related to TCDD-induced gastric cancer. Machine learning analysis identified 5 core target genes as key intersection target genes of TCDD-induced gastric cancer, with the chemical carcinogenesis-receptor activation pathway, p53 signaling pathway, and IL-17 signaling pathway being the key pathways. Molecular docking revealed specific binding effects and binding sites between TCDD and intersection target proteins.

Conclusion

This study suggests that TCDD may affect the pathogenesis of gastric cancer by targeting specific genes and pathways. Molecular docking simulations indicate that there is a significant binding specificity effect between TCDD and target proteins, which is key to gastric cancer development.

Graphical abstract