Current medicinal chemistry · 2026
Exploring the Toxicological Impact and Mechanisms of DEHP Exposure on Prostate Cancer Through Network Toxicology and Machine Learning Algorithms.
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Chen Jinji, Chen Jianlin, Huang Junming, Chen Shaohua, Feng Guanzheng
Аннотация
INTRODUCTION: Prostate 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).
METHODS: Potential 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.
RESULTS: A 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.
CONCLUSIONS: The 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.
METHODS: Potential 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.
RESULTS: A 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.
CONCLUSIONS: The 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.