From Target Prediction to Mechanistic Insights: Revealing Air Pollution-Driven Mechanisms in Endometrial Cancer via Interpretable Machine Learning and Molecular Docking
Air pollution is a known contributor to cancer risk, although its specific impact on endometrial cancer (EC) remains unclear. This study integrates network toxicology, transcriptomics, molecular docking, and machine learning to investigate pollutant–gene interactions in EC. We identify 83 air pollut...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/16/7/841 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849714654975098880 |
|---|---|
| author | Hongyao Liu Yueqing Zou |
| author_facet | Hongyao Liu Yueqing Zou |
| author_sort | Hongyao Liu |
| collection | DOAJ |
| description | Air pollution is a known contributor to cancer risk, although its specific impact on endometrial cancer (EC) remains unclear. This study integrates network toxicology, transcriptomics, molecular docking, and machine learning to investigate pollutant–gene interactions in EC. We identify 83 air pollution-associated EC genes (APECGs), with TNF, ESR1, IL1B, NFKB1, and PTGS2 as the hub genes. A 13-gene RSF-SuperPC model, including CCNE1, SLC2A1, AHCY, and CDC25C, shows effective prognostic stratification. Molecular docking reveals strong binding between pollutants (e.g., benzene, toluene, and ethylbenzene) and key APECGs. The enrichment and SHAP analyses suggest that pollutant-driven EC progression involves DNA damage, metabolic reprogramming, epigenetic dysregulation, immune suppression, and inflammation. These findings reveal potential mechanisms linking air pollution to EC and support the development of biomarkers for high-exposure populations. Further experimental and epidemiological validation is needed to enable clinical translation. |
| format | Article |
| id | doaj-art-745ad0de19f84acebdb8a34109b14075 |
| institution | DOAJ |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj-art-745ad0de19f84acebdb8a34109b140752025-08-20T03:13:39ZengMDPI AGAtmosphere2073-44332025-07-0116784110.3390/atmos16070841From Target Prediction to Mechanistic Insights: Revealing Air Pollution-Driven Mechanisms in Endometrial Cancer via Interpretable Machine Learning and Molecular DockingHongyao Liu0Yueqing Zou1Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai 200092, ChinaDepartment of Business Administration, Zhejiang Institute of Administration, Hangzhou 311121, ChinaAir pollution is a known contributor to cancer risk, although its specific impact on endometrial cancer (EC) remains unclear. This study integrates network toxicology, transcriptomics, molecular docking, and machine learning to investigate pollutant–gene interactions in EC. We identify 83 air pollution-associated EC genes (APECGs), with TNF, ESR1, IL1B, NFKB1, and PTGS2 as the hub genes. A 13-gene RSF-SuperPC model, including CCNE1, SLC2A1, AHCY, and CDC25C, shows effective prognostic stratification. Molecular docking reveals strong binding between pollutants (e.g., benzene, toluene, and ethylbenzene) and key APECGs. The enrichment and SHAP analyses suggest that pollutant-driven EC progression involves DNA damage, metabolic reprogramming, epigenetic dysregulation, immune suppression, and inflammation. These findings reveal potential mechanisms linking air pollution to EC and support the development of biomarkers for high-exposure populations. Further experimental and epidemiological validation is needed to enable clinical translation.https://www.mdpi.com/2073-4433/16/7/841air pollutantendometrial cancerSHAPmolecular dockingmachine learning |
| spellingShingle | Hongyao Liu Yueqing Zou From Target Prediction to Mechanistic Insights: Revealing Air Pollution-Driven Mechanisms in Endometrial Cancer via Interpretable Machine Learning and Molecular Docking Atmosphere air pollutant endometrial cancer SHAP molecular docking machine learning |
| title | From Target Prediction to Mechanistic Insights: Revealing Air Pollution-Driven Mechanisms in Endometrial Cancer via Interpretable Machine Learning and Molecular Docking |
| title_full | From Target Prediction to Mechanistic Insights: Revealing Air Pollution-Driven Mechanisms in Endometrial Cancer via Interpretable Machine Learning and Molecular Docking |
| title_fullStr | From Target Prediction to Mechanistic Insights: Revealing Air Pollution-Driven Mechanisms in Endometrial Cancer via Interpretable Machine Learning and Molecular Docking |
| title_full_unstemmed | From Target Prediction to Mechanistic Insights: Revealing Air Pollution-Driven Mechanisms in Endometrial Cancer via Interpretable Machine Learning and Molecular Docking |
| title_short | From Target Prediction to Mechanistic Insights: Revealing Air Pollution-Driven Mechanisms in Endometrial Cancer via Interpretable Machine Learning and Molecular Docking |
| title_sort | from target prediction to mechanistic insights revealing air pollution driven mechanisms in endometrial cancer via interpretable machine learning and molecular docking |
| topic | air pollutant endometrial cancer SHAP molecular docking machine learning |
| url | https://www.mdpi.com/2073-4433/16/7/841 |
| work_keys_str_mv | AT hongyaoliu fromtargetpredictiontomechanisticinsightsrevealingairpollutiondrivenmechanismsinendometrialcancerviainterpretablemachinelearningandmoleculardocking AT yueqingzou fromtargetpredictiontomechanisticinsightsrevealingairpollutiondrivenmechanismsinendometrialcancerviainterpretablemachinelearningandmoleculardocking |