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CC BY 4.0 · Chinese medicine and natural products 2025; 05(02): e124-e134
DOI: 10.1055/s-0045-1809681
Original Article

Investigate the Effects of Qingre Zaoshi Jiedu Recipe on Cervical Inflammatory–Cancer Transformation Associated with High-Risk Human Papillomavirus Using Network Pharmacology, Gene Expression Omnibus, and Molecular Docking Methods

Shanyun Wang
1   Traditional Chinese Gynecology Department, Zhongshan Hospital of Traditional Chinese Medicine, Zhongshan, Guangdong, China
,
Jian Huang
2   Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Centre for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
,
Linhua Yang
3   Gynecology and Obstetric Department, Zhongshan Hospital of Traditional Chinese Medicine, Zhongshan, Guangdong, China
,
Jianfeng Zeng
4   Urology and Andrology Department, Zhongshan Hospital of Traditional Chinese Medicine, Zhongshan, Guangdong, China
› Author Affiliations
Funding This work was supported by National Chinese Medicine Inheritance and Development Demonstration Pilot Project-Zhongshan Chinese Medicine Hospital Chinese Medicine Research Project (YN2024B005).
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Abstract

Objective

To investigate the active components and mechanisms by which Qingre Zaoshi Jiedu Recipe influences the transformation from cervical high-risk human papillomavirus (HR-HPV)-related cervicitis to cancer, utilizing network pharmacology, Gene Expression Omnibus (GEO) data, and molecular docking techniques.

Methods

Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), HERB, and the Encyclopedia of Traditional Chinese Medicine (ETCM) databases were used to screen out the active ingredients and related targets of Qingre Zaoshi Jiedu Recipe. The GeneCards database for disease targets at different stages of HR-HPV-related cervicitis–cancer were searched. The String platform to construct a protein-protein interaction network and identify key targets was utilized. Enrichment analysis of intersecting genes was performed using the DAVID database. The GSE149763 dataset from the GEO to identify differential genes involved in the transformation from cervicitis to cervical cancer by comparing cervicitis with cervical intraepithelial neoplasia III and cervical cancer was utilized. R language to generate volcano plots, heat maps, and key target expression trend charts were employed. Molecular docking of key pathway targets and main compounds of Qingre Zaoshi Jiedu Recipe for HR-HPV-related cervicitis–cancer was performed using AutoDock Vina.

Results

The study identified 185 main active ingredients of Qingre Zaoshi Jiedu Recipe. The protein–protein interaction network indicates that the core targets for interfering with HR-HPV-related inflammation–cancer transformation include TNF, IL-6, IL-1β, CXCL8, IL-1α, IFN-γ, IL-10, CCL2, CCL5, and CXCL10. KEGG pathway analysis indicates that Qingre Zaoshi Jiedu Recipe primarily affects HR-HPV-related inflammation–cancer transformation via the Toll-like receptor signaling pathway. GEO analysis identified the Toll-like receptor pathway as crucial across various stages of cervicitis–cancer lesions, with CXCL10 emerging as a key target. Molecular docking analysis revealed that the primary components of Qingre Zaoshi Jiedu Recipe effectively bind to TLR4.

Conclusion

Qingre Zaoshi Jiedu Recipe can interfere with HR-HPV-related cervicitis–cancer transformation by acting on TLR4 through the Toll-like receptor pathway.

Keywords

Qingre Zaoshi Jiedu Recipe - inflammatory-cancer transformation - HR-HPV - CXCL10 - TLR4 - Toll-like receptor signaling pathway

CRediT Authorship Contribution Statement

Shanyun Wang and Jian Huang: visualization, funding acquisition, data curation, project administration, writing—original draft, and writing—review and editing; Linhua Yang: writing—review and editing; Jianfeng Zeng: supervision, formal analysis, methodology, validation, and writing—review and editing.




Publication History

Received: 12 January 2025

Accepted: 17 March 2025

Article published online:
27 June 2025

© 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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