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Background <p>Environmental pollutants, especially hydroquinone (HQ), a phenolic compound, pose major global health risks and may be linked to bladder cancer (BC). This study examines HQ’s role in BC development.</p> Methods <p>Mendelian randomization (MR) analysis was employed to explore the potential relationship between HQ and BC. Toxicological targets of HQ were identified using ChEMBL and SwissTargetPrediction databases, while BC-related genes were extracted from the GEO database. Core targets were identified through machine learning algorithms, including Lasso, SVM-RFE, and RF, and validated via molecular docking and dynamics simulations.</p> Results <p>A significant causal relationship between HQ and BC was found. Eleven candidate targets were identified through intersection analysis of HQ-related toxicological targets and BC-related differential genes. Six core targets—ALDH1A1, PMP22, TDP1, HBB, LMNA, and CA2—were selected. Single-cell sequencing analysis revealed their primary expression in fibroblasts and epithelial cells. Molecular docking and dynamics simulations confirmed strong binding between HQ and these core targets.</p> Conclusion <p>This study combines MR analysis and network toxicology to uncover the causal link between HQ and BC, identifying key molecular targets. These findings provide insights into the mechanisms of HQ in BC and offer potential biomarkers and therapeutic targets for future treatments and preventive strategies.</p>

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Exploring the potential mechanisms of hydroquinone on bladder cancer using network toxicology, Mendelian randomization analysis, molecular docking, and molecular dynamics simulations

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月27日

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  • Yu Li,
  • Yirong Ma,
  • Junyu Lai,
  • Qiang Wan,
  • Jiaming Li,
  • Jianguang Wu

摘要

Background

Environmental pollutants, especially hydroquinone (HQ), a phenolic compound, pose major global health risks and may be linked to bladder cancer (BC). This study examines HQ’s role in BC development.

Methods

Mendelian randomization (MR) analysis was employed to explore the potential relationship between HQ and BC. Toxicological targets of HQ were identified using ChEMBL and SwissTargetPrediction databases, while BC-related genes were extracted from the GEO database. Core targets were identified through machine learning algorithms, including Lasso, SVM-RFE, and RF, and validated via molecular docking and dynamics simulations.

Results

A significant causal relationship between HQ and BC was found. Eleven candidate targets were identified through intersection analysis of HQ-related toxicological targets and BC-related differential genes. Six core targets—ALDH1A1, PMP22, TDP1, HBB, LMNA, and CA2—were selected. Single-cell sequencing analysis revealed their primary expression in fibroblasts and epithelial cells. Molecular docking and dynamics simulations confirmed strong binding between HQ and these core targets.

Conclusion

This study combines MR analysis and network toxicology to uncover the causal link between HQ and BC, identifying key molecular targets. These findings provide insights into the mechanisms of HQ in BC and offer potential biomarkers and therapeutic targets for future treatments and preventive strategies.