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Journal of Chinese Pharmaceutical Sciences ›› 2024, Vol. 33 ›› Issue (10): 943-964.DOI: 10.5246/jcps.2024.10.068

• Original articles • Previous Articles     Next Articles

Elucidating the therapeutic mechanism of Cang Zhu and Huang Bai in rheumatoid arthritis: a comprehensive analysis integrating network pharmacology and GEO data

Qian Deng1, Zining Peng2, Weitian Yan1, Nian Liu2, Fanyu Meng2, Jianmei Yin2, Haozhe Zhang2, Xingqiang Wang1,*(), Jiangyun Peng1,*()   

  1. 1 Department of Rheumatology, The First Affiliated Hospital of Yunnan University of Chinese Medicine, Kunming 650000, Yunnan, China
    2 Yunnan University of Chinese Medicine, Kunming 650500, Yunnan, China
  • Received:2024-02-13 Revised:2024-06-13 Accepted:2024-09-21 Online:2024-10-31 Published:2024-10-31
  • Contact: Xingqiang Wang, Jiangyun Peng
  • Supported by:
    National Natural Science Foundations of China (Grant No. 81960863), the Education Department of Yunnan Province (Grant No. 2023Y0463).

Abstract:

In the present study, we explored the therapeutic potential of Cang Zhu-Huang Bai (CZ-HB) against rheumatoid arthritis (RA) and elucidated the associated mechanisms. The approach involved a systematic examination of the chemical ingredients of CZ-HB using TCMSP database. Subsequently, we predicted the targets corresponding to the active ingredients through the SwissTargetPrediction database. We constructed a comprehensive drug-ingredient-target network using Cytoscape (v 3.8.0), with the main ingredients of the drugs identified based on their degree values. We conducted a meticulous search across GEO, GeneCards, Therapeutic Target Database (TTD), and PharmGkb databases to identify target proteins associated with RA. The intersection of targets corresponding to the drugs' active ingredients and those associated with RA provided crucial insights. Functional analysis, including GO and KEGG pathway enrichment analyses, was performed on the intersecting targets using R (v 4.2.2). Additionally, a protein-protein interaction (PPI) network of the intersecting targets was constructed using the String platform. The resulting drug-ingredient-target-disease topology network was visualized using Cytoscape (v 3.8.0), and the Cytohubba plugin facilitated the identification of hub genes. The study revealed 35 active ingredients of CZ-HB and their corresponding 673 targets. We identified 14 major active ingredients crucial to the drug’s effects by focusing on the degree values. Furthermore, our investigation uncovered 784 targets associated with RA. Through the intersection of drug and disease targets, we pinpointed 34 active ingredients of CZ-HB capable of acting on 126 targets implicated in RA. The topological network analysis of the intersected genes identified five hub genes. The binding affinity of these hub genes to the 14 primary active ingredients of the drug was confirmed through molecular docking. The enrichment results of the intersecting genes suggested that CZ-HB exerted its anti-RA effects through a multi-component, multi-target, and multi-pathway approach.

Key words: Network pharmacology, GEO, Rheumatoid arthritis, Cang Zhu, Huang Bai, Molecular docking

Supporting: /attached/file/20241106/20241106132914_760.pdf