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Background <p>This study investigates the molecular mechanisms by which di-n-butyl phthalate (DBP) and mono-n-butyl phthalate (MnBP)-induced diabetic kidney disease (DKD).</p> Methods <p>Differential expression analysis and Weighted Gene Co-expression Network Analysis were used to identify DKD-associated targets. Machine learning, molecular docking, molecular dynamics simulations, and public databases were integrated to explore the interaction between DBP/MnBP and target proteins.</p> Results <p>Six core genes were identified: <i>DUSP1</i>, <i>PTGS2</i>, <i>FOSB</i>, <i>GDF15</i>, <i>NR4A1</i>, and <i>CXCR2</i>. Among these, <i>DUSP1</i> and <i>FOSB</i> showed excellent performance in single-gene ROC curves, box plots, public databases, and molecular docking. Molecular docking and molecular dynamics simulations demonstrated a stable binding affinity between DBP/MnBP and the target proteins.</p> Conclusion <p>This research suggests that DBP/MnBP may promote DKD by targeting these six core genes. The binding capacity and stability of DBP/MnBP with these genes were confirmed by machine learning, molecular docking, and molecular dynamics simulations. These findings provide a direction for future in-depth research on the DBP/MnBP-induced DKD mechanism.</p>

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Deconvolution of molecular mechanisms in di-n-butyl phthalate/mono-n-butyl phthalate induced diabetic kidney disease by integrated machine learning and molecular docking

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月29日

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  • Wenjie Chen,
  • Suyue Hou,
  • Jijia Hu

摘要

Background

This study investigates the molecular mechanisms by which di-n-butyl phthalate (DBP) and mono-n-butyl phthalate (MnBP)-induced diabetic kidney disease (DKD).

Methods

Differential expression analysis and Weighted Gene Co-expression Network Analysis were used to identify DKD-associated targets. Machine learning, molecular docking, molecular dynamics simulations, and public databases were integrated to explore the interaction between DBP/MnBP and target proteins.

Results

Six core genes were identified: DUSP1, PTGS2, FOSB, GDF15, NR4A1, and CXCR2. Among these, DUSP1 and FOSB showed excellent performance in single-gene ROC curves, box plots, public databases, and molecular docking. Molecular docking and molecular dynamics simulations demonstrated a stable binding affinity between DBP/MnBP and the target proteins.

Conclusion

This research suggests that DBP/MnBP may promote DKD by targeting these six core genes. The binding capacity and stability of DBP/MnBP with these genes were confirmed by machine learning, molecular docking, and molecular dynamics simulations. These findings provide a direction for future in-depth research on the DBP/MnBP-induced DKD mechanism.