Evidence map›Paper›PMID 41931331›Full record

ArticleMedicine2026

Study on material basis and network mechanism of the Guizhi Fuling pills in the treatment of endometriosis and endometrial polyps.

Lianghui Cui, Jia Yang, Yuli Zhang, Yu Wang, Wei Xie, Xiaoou Xue

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Article in Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Lianghui CuiDepartment of Gynecology, Dongzhimen Hospital of Beijing University of Chinese Medicine, Beijing, China.
Jia YangInstitute of Acupuncture and Moxibustion, Beijing University of Chinese Medicine, Beijing, China.
Yuli ZhangDepartment of Gynecology, Dongzhimen Hospital of Beijing University of Chinese Medicine, Beijing, China.
Yu WangInstitute of Acupuncture and Moxibustion, China Academy of Chinese Medical Sciences, Beijing, China.
Wei XieDepartment of Gynecology, Dongzhimen Hospital of Beijing University of Chinese Medicine, Beijing, China.
Xiaoou XueDepartment of Gynecology, Dongzhimen Hospital of Beijing University of Chinese Medicine, Beijing, China.ORCID 0009-0005-1042-5998

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To explore the material basis and network mechanism of the Guizhi Fuling pills in the treatment of endometriosis and endometrial polyps based on network pharmacology and machine learning. The effective constituents and targets of the Guizhi Fuling pills were obtained based on the TCMSP and SymMAP databases. Endometrial polyps disease genes were retrieved by GenCards and OMIM databases. The active component-common disease target was obtained using Venn Diagram, which was uploaded to the STRING database and Cytoscape software to construct the protein interaction network and analyze its molecular biological mechanism. Random walk with restart was run on the compound network and protein-protein interaction network to detect core compounds, targets, and the stability of the relapse network. Then, the biological function and pathway enrichment analysis of the core targets were carried out with the help of the DAVID database, and the key targets of the pathway were further obtained. Finally, molecular docking verification was conducted. A total of 30 disease targets were obtained. According to gene ontology analysis, the treatment of endometriosis and endometrial polyps with Guizhi Fuling pills mainly focused on biological processes such as rhythm process, mechanical stimulation response, DNA binding regulation, and other biological processes. The signaling pathways are mainly concentrated in apoptosis, TNF signaling pathway, IL-17 signaling pathway, toll-like receptor signaling pathway, VEGF signaling pathway, estrogen signaling pathway, nuclear factor-kappa B signaling pathway, hepatitis signaling pathway, etc. Then, the 10 effective components and 10 target proteins were further verified by molecular docking. The results showed that β-sitosterol, luteolin, and quercetin were the most closely connected with the target NR3C1 and MMP9, respectively, which may be the key compounds and core target proteins in the "different diseases and the same treatment" of the Guizhi Fuling pills. The mechanisms of its comorbidity mainly involved estrogen, tumor, inflammation, immune response, and other signaling pathways, and were closely related to several biological processes. It can provide research direction for further research on the mechanism of "treating different diseases with the same treatment" of the Guizhi Fuling pills.

Indexed as

Drugs, Chinese HerbalEndometriosisPolypsFemaleHumansMolecular Docking SimulationNetwork PharmacologyProtein Interaction MapsSignal TransductionDrugs, Chinese Herbalguizhi-fulingendometrial polypsendometriosisGuizhi Fuling pillsmachine learningnetwork pharmacology

Identifiers

PMID41931331
PMCPMC13052944

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.