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Background <p>Dibutyl phthalate (DBP), a widely used plasticizer, has been implicated in various health concerns, including potential carcinogenic effects. However, the molecular mechanisms underlying DBP’s toxicological effects on ovarian cancer remain poorly understood. This study aimed to computationally investigate the potential association between DBP and ovarian cancer through network toxicology and bioinformatics approaches.</p> Methods <p>Transcriptomic data from GSE26712 identified ovarian cancer-related differentially expressed genes (DEGs). DBP targets were curated from SEA, SwissTargetPrediction, CTD, and TargetNet. Intersecting genes were analyzed via protein-protein interaction (PPI) networks, functional enrichment, and gene set variation analysis (GSVA). Machine learning (SVM, LASSO, RF) pinpointed key targets, validated through ROC analysis, molecular docking, tumor immune infiltration assessment, and immunofluorescence staining of clinical samples.</p> Results <p>We identified 27 shared targets linking DBP to ovarian cancer, with CTNNB1, SOD2, and KDR as central PPI nodes. Enriched pathways included Wnt signaling, apoptosis, immune responses, and fatty acid biosynthesis. GSVA highlighted dysregulation in T-cell proliferation, cytokine activity, and hypoxia response. Machine learning converged on three key genes: KDR, ANXA3, and FOLR1. ROC analysis confirmed exceptional diagnostic accuracy. These genes correlated significantly with immune infiltration. Molecular docking revealed strong DBP binding to targets. Immunofluorescence validated differential protein expression: KDR, and ANXA3 were downregulated, while FOLR1 was upregulated in cancer tissues.</p> Conclusion <p>This integrative computational study reveals a potential molecular link between DBP and ovarian cancer, proposing that DBP may influence ovarian cancer-associated pathways. The identified hub genes KDR, ANXA3, and FOLR1 represent promising diagnostic biomarkers. Our findings provide a hypothesis-generating framework suggesting that DBP exposure could contribute to ovarian carcinogenesis by disrupting key biological pathways and modulating the tumor immune microenvironment, warranting further experimental investigation.</p>

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Computational identification of diagnostic biomarkers linking Dibutyl phthalate exposure to ovarian cancer through network toxicology and machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月22日

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  • Yinghua Li,
  • Hongyu Deng,
  • Youguo Chen

摘要

Background

Dibutyl phthalate (DBP), a widely used plasticizer, has been implicated in various health concerns, including potential carcinogenic effects. However, the molecular mechanisms underlying DBP’s toxicological effects on ovarian cancer remain poorly understood. This study aimed to computationally investigate the potential association between DBP and ovarian cancer through network toxicology and bioinformatics approaches.

Methods

Transcriptomic data from GSE26712 identified ovarian cancer-related differentially expressed genes (DEGs). DBP targets were curated from SEA, SwissTargetPrediction, CTD, and TargetNet. Intersecting genes were analyzed via protein-protein interaction (PPI) networks, functional enrichment, and gene set variation analysis (GSVA). Machine learning (SVM, LASSO, RF) pinpointed key targets, validated through ROC analysis, molecular docking, tumor immune infiltration assessment, and immunofluorescence staining of clinical samples.

Results

We identified 27 shared targets linking DBP to ovarian cancer, with CTNNB1, SOD2, and KDR as central PPI nodes. Enriched pathways included Wnt signaling, apoptosis, immune responses, and fatty acid biosynthesis. GSVA highlighted dysregulation in T-cell proliferation, cytokine activity, and hypoxia response. Machine learning converged on three key genes: KDR, ANXA3, and FOLR1. ROC analysis confirmed exceptional diagnostic accuracy. These genes correlated significantly with immune infiltration. Molecular docking revealed strong DBP binding to targets. Immunofluorescence validated differential protein expression: KDR, and ANXA3 were downregulated, while FOLR1 was upregulated in cancer tissues.

Conclusion

This integrative computational study reveals a potential molecular link between DBP and ovarian cancer, proposing that DBP may influence ovarian cancer-associated pathways. The identified hub genes KDR, ANXA3, and FOLR1 represent promising diagnostic biomarkers. Our findings provide a hypothesis-generating framework suggesting that DBP exposure could contribute to ovarian carcinogenesis by disrupting key biological pathways and modulating the tumor immune microenvironment, warranting further experimental investigation.