Evidence map›Paper›PMID 42304923›Full record

ArticleCurrent medicinal chemistry2026

Exploring the Toxicological Impact and Mechanisms of DEHP Exposure on Prostate Cancer Through Network Toxicology and Machine Learning Algorithms.

Jinji Chen, Jianlin Chen, Junming Huang, Shaohua Chen, Guanzheng Feng

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Article in Current medicinal chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Jinji ChenDepartment of Urology, Guangxi Medical University Cancer Hospital, Nanning, 530000, China.ORCID 0000-0002-2501-0999
Jianlin ChenThe First People's Hospital of Yulin City, Yulin, 537000, China.ORCID 0009-0004-8865-3766
Junming HuangDepartment of Urology, Guangxi Medical University Cancer Hospital, Nanning, 530000, China.ORCID 0009-0008-4040-8365
Shaohua ChenDepartment of Urology, Guangxi Medical University Cancer Hospital, Nanning, 530000, China.ORCID 0000-0002-4301-7254
Guanzheng FengDepartment of Urology, Guangxi Medical University Cancer Hospital, Nanning, 530000, China.ORCID 0009-0009-1542-8388

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionProstate Cancer (PCa) is the most common male malignancy, and its initiation and progression may be influenced by environmental pollutants such as di(2-ethylhexyl) phthalate (DEHP).

methodsPotential targets of DEHP were retrieved from ChEMBL, SwissTargetPrediction, and PharmMapper databases. DEHP-related genes correlated with PCa were identified by intersecting the DEHP target gene set with PCa-associated genes. Machine learning approaches were employed to identify and characterize the core genes linked to PCa. SHapley Additive exPlanations (SHAP) analysis was used to evaluate model interpretability. Molecular docking was performed to assess the binding interactions between DEHP and the key target proteins. Cellular validation was performed using CCK-8 assay, quantitative reverse transcription PCR (RT-qPCR), and western blot analysis.

resultsA total of 53 genes were identified as potential actionable targets of DEHP in PCa pathobiology. Through machine learning, these genes were reduced to 12 genes (ACACB, CD200, FERMT2, GCNT1, GNAI2, GSTM2, IMPDH2, ITGA2, MMP26, PMM2, PRKCA, and SRD5A2), which exhibited distinct dysregulation patterns in PCa tissues. Furthermore, molecular simulation docking simulations demonstrated its robust binding interactions with key targets, including PMM2, ITGA2, GSTM2, IMPDH2, and PRKCA, warranting further experimental validation. DISCUSSION: DEHP, an industrial chemical, may contribute to PCa via multiple pathways. A 12-gene model for DEHP-associated PCa was identified, among which PMM2 may play a key role in mediating oncogenic effects via metabolic, redox, and signaling reprogramming.

conclusionsThe findings indicated that DEHP can influence the development of PCarelated pathways through targeting specific genes and signaling, exhibiting the potential to serve as a biomarker to assess the risk of PCa related to DEHP exposure.

Indexed as

AlgorithmsDiethylhexyl PhthalateMachine LearningProstatic NeoplasmsHumansMaleMolecular Docking SimulationDiethylhexyl PhthalatebioinformaticsDi(2-ethylhexyl) phthalatemachine learningmolecular dockingprostate cancersignaling reprogramming

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.