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已发表论文
基于机器学习、网络毒理学和分子对接分析探讨乙酰柠檬酸三丁酯暴露对骨关节炎的毒理学影响
Authors Zhu Y , Deng L, Xia J, Yang J, Zhao D , Li M
Received 14 August 2025
Accepted for publication 18 November 2025
Published 2 February 2026 Volume 2026:18 560636
DOI https://doi.org/10.2147/OARRR.S560636
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Professor Chuan-Ju Liu
Yifang Zhu,1 Lin Deng,1 Junxiang Xia,1 Jing Yang,1 Dan Zhao,1 Min Li2
1Clinical Laboratory, Sichuan Province Orthopedic Hospital, Chengdu, Sichuan, People’s Republic of China; 2Department of Rheumatoid Osteoarthropathy, Sichuan Province Orthopedic Hospital, Chengdu, Sichuan, People’s Republic of China
Correspondence: Yifang Zhu, Email 53113072@qq.com
Objective: To investigate the potential toxicological effects of acetyl tributyl citrate (ATBC) on osteoarthritis (OA) and elucidate the underlying mechanisms using bioinformatics, machine learning, and network toxicology.
Methods: ATBC targets were identified from multiple databases, and OA-associated differentially expressed genes (DEGs) were sourced from GSE51588. Intersection analysis identified common targets. Functional enrichment and protein-protein interaction (PPI) network analysis were performed. Machine learning algorithms (LASSO, Random Forest and SVM) validated core targets, with ROC curves assessing diagnostic potential. Immune infiltration differences were analyzed via Cibersort. Molecular docking confirmed ATBC binding to core targets, and an adverse outcome pathway (AOP) framework was developed to elucidate ATBC’s role in exacerbating OA through key genes and pathways.
Results: Intersection analysis identified 40 common targets related to both ATBC and OA. Functional enrichment analysis revealed that these targets were significantly involved in calcium signaling pathways and neuroactive ligand-receptor interactions, both of which are implicated in OA pathogenesis. The PPI network analysis identified TNF, MMP8, CXCR4, and SLC2A1 as core targets. Machine learning algorithms further validated these core targets. ROC curve analysis showed that these genes have diagnostic potential, with AUC values ranging from 0.762 to 0.970. Immune infiltration analysis using Cibersort revealed significant differences in immune cell infiltration between OA and control groups, with core targets showing distinct correlations with various immune cells. Molecular docking confirmed strong binding affinities between ATBC and the core targets, with binding energies less than − 5 kcal/mol. A novel adverse outcome pathway (AOP) framework was established, suggesting that ATBC may influence the expression of TNF, CXCR4, MMP8, and SLC2A1, with the calcium signaling and neuroactive ligand-receptor interaction pathways potentially contributing to immune dysregulation and OA progression.
Conclusion: The identification of key targets (TNF, MMP8, CXCR4, and SLC2A1) and molecular docking results elucidates potential mechanisms by which ATBC exposure may exacerbate OA progression. The AOP provides evidence for joint-health risk assessment of plasticizers and offers readily measurable biomarkers for regulatory toxicology and future therapeutic development.
Keywords: osteoarthritis, acetyl tributyl citrate, network toxicology, molecular docking, machine learning, bioinformatics
1Clinical Laboratory, Sichuan Province Orthopedic Hospital, Chengdu, Sichuan, People’s Republic of China; 2Department of Rheumatoid Osteoarthropathy, Sichuan Province Orthopedic Hospital, Chengdu, Sichuan, People’s Republic of China
Correspondence: Yifang Zhu, Email 53113072@qq.com
Objective: To investigate the potential toxicological effects of acetyl tributyl citrate (ATBC) on osteoarthritis (OA) and elucidate the underlying mechanisms using bioinformatics, machine learning, and network toxicology.
Methods: ATBC targets were identified from multiple databases, and OA-associated differentially expressed genes (DEGs) were sourced from GSE51588. Intersection analysis identified common targets. Functional enrichment and protein-protein interaction (PPI) network analysis were performed. Machine learning algorithms (LASSO, Random Forest and SVM) validated core targets, with ROC curves assessing diagnostic potential. Immune infiltration differences were analyzed via Cibersort. Molecular docking confirmed ATBC binding to core targets, and an adverse outcome pathway (AOP) framework was developed to elucidate ATBC’s role in exacerbating OA through key genes and pathways.
Results: Intersection analysis identified 40 common targets related to both ATBC and OA. Functional enrichment analysis revealed that these targets were significantly involved in calcium signaling pathways and neuroactive ligand-receptor interactions, both of which are implicated in OA pathogenesis. The PPI network analysis identified TNF, MMP8, CXCR4, and SLC2A1 as core targets. Machine learning algorithms further validated these core targets. ROC curve analysis showed that these genes have diagnostic potential, with AUC values ranging from 0.762 to 0.970. Immune infiltration analysis using Cibersort revealed significant differences in immune cell infiltration between OA and control groups, with core targets showing distinct correlations with various immune cells. Molecular docking confirmed strong binding affinities between ATBC and the core targets, with binding energies less than − 5 kcal/mol. A novel adverse outcome pathway (AOP) framework was established, suggesting that ATBC may influence the expression of TNF, CXCR4, MMP8, and SLC2A1, with the calcium signaling and neuroactive ligand-receptor interaction pathways potentially contributing to immune dysregulation and OA progression.
Conclusion: The identification of key targets (TNF, MMP8, CXCR4, and SLC2A1) and molecular docking results elucidates potential mechanisms by which ATBC exposure may exacerbate OA progression. The AOP provides evidence for joint-health risk assessment of plasticizers and offers readily measurable biomarkers for regulatory toxicology and future therapeutic development.
Keywords: osteoarthritis, acetyl tributyl citrate, network toxicology, molecular docking, machine learning, bioinformatics