Prediction of Lindane-Induced Breast Cancer Toxicity Through Multi-Target Network Analysis and Mechanistic Investigation
- 1College of Pharmacy, Changchun University of Chinese Medicine, Changchun 130117, China
- 2College of Innovation and Entrepreneurship, Changchun University of Chinese Medicine, Changchun 130117, China
Abstract
This study aimed to identify potential molecular targets associated with Lindane-induced molecular alterations in breast cancer and elucidate its toxicological mechanisms using an integrated strategy combining network toxicology, machine learning, molecular simulation, and experimental validation. Lindane-related targets and breast cancer transcriptomic data were collected from public databases, and differentially expressed genes and WGCNA-derived hub genes were integrated to identify candidate targets. GO and KEGG analyses were performed to explore biological functions, while machine learning models and SHAP analysis were applied to screen potential biomarkers. Molecular docking and 100 ns molecular dynamics simulations were conducted to evaluate the interaction between Lindane and key proteins. Twenty-eight overlapping targets were identified and mainly enriched in lipid metabolism, sphingolipid signaling, and p53-related pathways. The LASSO-plsRglm model achieved the highest average AUC (0.996) and identified five candidate genes, including NR3C2, AR, TGM2, ATAD2, and P2RY2. NR3C2 exhibited strong discriminatory performance (AUC = 0.982) and was consistently downregulated in breast cancer tissues. Molecular simulations suggested a potential interaction between Lindane and NR3C2. Cell experiments using human breast cancer MCF-7 cells demonstrated that Lindane increased intracellular ROS accumulation and promoted apoptosis, accompanied by decreased NR3C2 expression, upregulation of TGM2 and ATAD2, increased p53, Bax, and cleaved caspase-3 protein levels, and reduced Bcl-2 expression. NR3C2 may serve as a potential regulatory molecule involved in cellular responses to Lindane exposure in breast cancer, providing new insights into the molecular mechanisms underlying environmental Lindane exposure and breast cancer-related toxicity.
Related
- Peptidomics-guided 3D structural-mechanistic investigation of bovine colostrum peptides reveals multi-targeted
- Machine Learning for Toxicity Prediction in Low-Sample Molecular Classes
- Beyond structure prediction: Translating D‐I‐TASSER models into functional and mechanistic hypotheses
- Benchmarking graph-based models for in-silico toxicity prediction in drug discovery
- Molecular Alterations of Bovine Serum Albumin Induced by the Food Dye Acid Yellow 23: A Mechanistic Study
BibTeX
@article{lin2026prediction,
title = {{Prediction of Lindane-Induced Breast Cancer Toxicity Through Multi-Target Network Analysis and Mechanistic Investigation}},
author = {Yuze Lin and Jiahui Xu and Mingyang Sheng and Zidong Yang and Jiatong Liu and Xiangze Fan and Lin Wang and Wei Xu},
journal = {International Journal of Molecular Sciences},
year = {2026},
month = oct,
doi = {10.3390/ijms27209056},
url = {https://www.mdpi.com/1422-0067/27/20/9056},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Prediction of Lindane-Induced Breast Cancer Toxicity Through Multi-Target Network Analysis and Mechanistic Investigation
AU - Yuze Lin
AU - Jiahui Xu
AU - Mingyang Sheng
AU - Zidong Yang
AU - Jiatong Liu
AU - Xiangze Fan
AU - Lin Wang
AU - Wei Xu
PY - 2026
DA - 2026/10/01
JO - International Journal of Molecular Sciences
DO - 10.3390/ijms27209056
UR - https://www.mdpi.com/1422-0067/27/20/9056
AB - This study aimed to identify potential molecular targets associated with Lindane-induced molecular alterations in breast cancer and elucidate its toxicological mechanisms using an integrated strategy combining network toxicology, machine learning, molecular simulation, and experimental validation. Lindane-related targets and breast cancer transcriptomic data were collected from public databases, and differentially expressed genes and WGCNA-derived hub genes were integrated to identify candidate targets. GO and KEGG analyses were performed to explore biological functions, while machine learning models and SHAP analysis were applied to screen potential biomarkers. Molecular docking and 100 ns molecular dynamics simulations were conducted to evaluate the interaction between Lindane and key proteins. Twenty-eight overlapping targets were identified and mainly enriched in lipid metabolism, sphingolipid signaling, and p53-related pathways. The LASSO-plsRglm model achieved the highest average AUC (0.996) and identified five candidate genes, including NR3C2, AR, TGM2, ATAD2, and P2RY2. NR3C2 exhibited strong discriminatory performance (AUC = 0.982) and was consistently downregulated in breast cancer tissues. Molecular simulations suggested a potential interaction between Lindane and NR3C2. Cell experiments using human breast cancer MCF-7 cells demonstrated that Lindane increased intracellular ROS accumulation and promoted apoptosis, accompanied by decreased NR3C2 expression, upregulation of TGM2 and ATAD2, increased p53, Bax, and cleaved caspase-3 protein levels, and reduced Bcl-2 expression. NR3C2 may serve as a potential regulatory molecule involved in cellular responses to Lindane exposure in breast cancer, providing new insights into the molecular mechanisms underlying environmental Lindane exposure and breast cancer-related toxicity.
SN - 1422-0067
ER -