Integration of network toxicology, machine learning and single-cell sequencing reveals the effects of soil pollutants on prostate cancer

Various analytical methods to elucidate mechanisms by which soil pollutants contribute to prostate cancer through interactions with key molecular targets DNMT1 and mTOR. These findings inform the prevention and treatment of environmentally related cancers and provide novel insights into the molecular connection between soil pollutants and prostate cancer, and laying the groundwork for preventive and intervention strategies targeting environmentally related cancers.

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Publication Details

Journal
Ecotoxicology and Environmental Safety
Published
2025-09-01
DOI
https://doi.org/10.1016/j.ecoenv.2025.119010
Citations
5
Primary Topic
Molecular Biology Techniques and Applications
Type
article
Field-Weighted Citation Impact
4.03
Controls
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article

Integration of network toxicology, machine learning and single-cell sequencing reveals the effects of soil pollutants on prostate cancer

Qingfu Deng, Xinyao Zhu, Yang Zeng, Qilong Wu et al.
5 citations
Ecotoxicology and Environmental Safety
Molecular Biology Techniques and Applications
4.03
article

Integration of network toxicology, machine learning and single-cell sequencing reveals the effects of soil pollutants on prostate cancer

Qingfu Deng, Xinyao Zhu, Yang Zeng, Qilong Wu, Tao Zhou, Juan Wang, Zhiyu Liu, Yuqi Li
article en
5 citations

Abstract

Various analytical methods to elucidate mechanisms by which soil pollutants contribute to prostate cancer through interactions with key molecular targets DNMT1 and mTOR. These findings inform the prevention and treatment of environmentally related cancers and provide novel insights into the molecular connection between soil pollutants and prostate cancer, and laying the groundwork for preventive and intervention strategies targeting environmentally related cancers.

Ecotoxicology and Environmental SafetyVol. 303
Southwest Medical University (CN), Affiliated Hospital of Southwest Medical University (CN)
Openalex Percentile: Top 6%
Molecular Biology Techniques and Applications
4.03
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