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Unraveling Multi-target Mechanisms of Codonopsis pilosula in Breast Cancer: A Synergistic Approach Combining Network
Haodong Guo1, Yuting Yang2, Jiajun Li2
1Department of Plastic Surgery, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China.
Introduction:
Breast cancer is a leading cause of cancer-related mortality in women. Although the traditional Chinese medicine Codonopsis Pilosula (CP) is empirically used in its treatment, the underlying mechanisms of action remain elusive. This study aimed to apply a novel integrative network pharmacology and machine learning approach to identify bioactive compounds in CP and elucidate their anti-breast cancer mechanisms.
Methods:
The analysis utilized a comprehensive and innovative workflow that combined network pharmacology, machine learning-based target prediction, bioinformatics analyses, and molecular docking and molecular dynamics simulations. Publicly available datasets were mined for CP constituents and putative targets, and integrated with breast cancer-associated gene profiles. Key compound-target interactions were prioritized via machine learning algorithms.
Results:
Machine learning highlighted EGFR and PTGS2 as primary targets. Molecular docking and dynamics demonstrated stable binding of Taraxerol and Stigmasterol to these proteins, with EGFR-Taraxerol, EGFR-Spinasterol, PTGS2-Stigmasterol, and PTGS2-Taraxerol complexes exhibiting robust affinity and stability.
Discussion:
The findings are significant as they reveal previously unreported interactions between CP's bioactive compounds and critical breast cancer targets. This provides a molecularlevel explanation for the traditional use of CP, bridging the gap between TCM and modern pharmacology. These results offer a solid foundation for further experimental validation.
Conclusion:
This multidisciplinary, predictive strategy successfully identified key bioactive compounds in CP and their molecular targets in breast cancer. The study provides crucial mechanistic evidence for CP's therapeutic potential and highlights the power of this integrated approach for drug discovery from TCM (Traditional Chinese Medicine).
Insights
Codonopsis Pilosula (CP) compounds Taraxerol and Stigmasterol show potential against breast cancer by targeting EGFR and PTGS2. This study elucidates the molecular mechanisms behind CP
Area of Science:
- Integrative pharmacology and computational biology
- Traditional Chinese Medicine (TCM) research
- Oncology and drug discovery
Background:
- Breast cancer remains a major cause of mortality in women worldwide.
- Traditional Chinese Medicine (TCM) Codonopsis Pilosula (CP) is empirically used for breast cancer treatment, but its mechanisms are unclear.
- This study investigates the anti-breast cancer mechanisms of CP using modern scientific approaches.
Purpose of the Study:
- To identify bioactive compounds in CP and elucidate their anti-breast cancer mechanisms.
- To apply a novel integrative network pharmacology and machine learning approach.
- To provide a molecular basis for the traditional use of CP in cancer therapy.
Main Methods:
- Utilized a workflow combining network pharmacology, machine learning-based target prediction, bioinformatics, and molecular simulations.
- Mined public datasets for CP constituents and targets, integrating them with breast cancer gene profiles.
- Prioritized compound-target interactions using machine learning algorithms and validated with molecular docking and dynamics.
Main Results:
- Machine learning identified EGFR and PTGS2 as key breast cancer targets.
- Taraxerol and Stigmasterol from CP demonstrated stable binding to EGFR and PTGS2.
- Molecular docking and dynamics simulations confirmed robust affinity and stability of key compound-target complexes.
Conclusions:
- Revealed novel interactions between CP compounds and critical breast cancer targets, explaining its traditional use.
- Provided crucial mechanistic evidence for CP's therapeutic potential in breast cancer.
- Highlighted the efficacy of an integrated computational approach for TCM-based drug discovery.
