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Exploring the toxicological impact of DEHP exposure on colorectal cancer through network toxicology, machine learning and bioinformatics analysis.

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BMC pharmacology & toxicology 📖 저널 OA 100% 2023: 2/2 OA 2025: 7/7 OA 2026: 12/12 OA 2023~2026 2025 Vol.27(1) p. 11
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Wang L, Qin Y, Fan W

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[OBJECTIVE] Colorectal cancer (CRC) ranks among the most prevalent malignant tumors, yet its underlying mechanisms remain not fully understood.

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APA Wang L, Qin Y, Fan W (2025). Exploring the toxicological impact of DEHP exposure on colorectal cancer through network toxicology, machine learning and bioinformatics analysis.. BMC pharmacology & toxicology, 27(1), 11. https://doi.org/10.1186/s40360-025-01065-0
MLA Wang L, et al.. "Exploring the toxicological impact of DEHP exposure on colorectal cancer through network toxicology, machine learning and bioinformatics analysis.." BMC pharmacology & toxicology, vol. 27, no. 1, 2025, pp. 11.
PMID 41373010 ↗

Abstract

[OBJECTIVE] Colorectal cancer (CRC) ranks among the most prevalent malignant tumors, yet its underlying mechanisms remain not fully understood. The role of environmental factors is critical in its development and progression. Di-(2-ethylhexyl) phthalate (DEHP), a widespread environmental contaminant, poses significant hazards to human health. Prior research indicates that exposure to DEHP can disrupt cellular processes like proliferation, differentiation, and apoptosis, which may contribute to cancer development. Nevertheless, the specific role and molecular pathways involved with DEHP in CRC are still poorly defined.

[METHODS] We integrated multiple public databases to identify overlapping targets of DEHP and CRC. Machine learning methods were applied to prioritize core targets, whose expression levels and diagnostic performance were then validated in the Gene Expression Omnibus (GEO) datasets. The association between core targets and immune cell infiltration was evaluated using CIBERSORT-based deconvolution. Finally, molecular docking was performed to predict the interactions between DEHP and the hub proteins, and, based on the docking results, an explicit-solvent molecular dynamics (MD) simulation of the top-ranked DEHP–target complex was conducted to further assess binding stability.

[RESULTS] Analysis of publicly available databases revealed 57 potential targets that were significantly enriched in signaling pathways related to “microRNAs in cancer” and “chemical carcinogenesis–receptor activation.” Machine learning methods identified five primary targets: CASP3, BCL6, BRD4, PPARA, and PRKCD. The expression levels of these genes in CRC tissues were significantly different from those in control tissues and showed reasonable diagnostic performance, as well as correlations with immune cell infiltration. Molecular docking suggested that DEHP can bind all five hub proteins, with PPARA exhibiting the most favorable binding affinity (− 6.7 kcal/mol). The 100-ns MD simulation further supported this binding mode by demonstrating that the DEHP–PPARA complex maintains a dynamically stable conformation with persistent key interactions.

[CONCLUSIONS] Our findings generate preliminary mechanistic hypotheses on how DEHP exposure may contribute to CRC by perturbing known cancer-related pathways and reshaping the intestinal immune microenvironment, with PPARA emerging as a key candidate mediator. The study highlights the utility of combining network toxicology, machine learning, immune deconvolution, molecular docking, and MD simulations to evaluate the potential carcinogenic risks of environmental pollutants.

[SUPPLEMENTARY INFORMATION] The online version contains supplementary material available at 10.1186/s40360-025-01065-0.

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