苍术、黄柏在类风湿关节炎中的治疗机制: 网络药理学与GEO数据的综合分析

doi:10.5246/jcps.2024.10.068

中国药学(英文版) ›› 2024, Vol. 33 ›› Issue (10): 943-964.DOI: 10.5246/jcps.2024.10.068

苍术、黄柏在类风湿关节炎中的治疗机制: 网络药理学与GEO数据的综合分析

邓茜¹, 彭紫凝², 晏蔚田¹, 刘念², 孟凡雨², 殷建美², 张昊喆², 王兴强¹^,^*(), 彭江云¹^,^*()

^1. 云南中医药大学第一附属医院风湿免疫科, 云南昆明 650000
^2. 云南中医药大学, 云南昆明 650500

收稿日期:2024-02-13 修回日期:2024-06-13 接受日期:2024-09-21 出版日期:2024-10-31 发布日期:2024-10-31
通讯作者: 王兴强, 彭江云

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 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).

摘要/Abstract

摘要：

本研究通过应用网络药理学方法探讨苍术、黄柏治疗类风湿性关节炎的作用及其可能的机制。首先, 从TCMSP数据库中获得药物的化学成分, 通过SwissTargetPrediction数据库预测药物有效成分对应的靶点, 再利用Cytoscape (v 3.8.0)构建药物-成分-靶点网络, 根据网络的度值确定药物的主要活性成分。其次, 通过GEO数据分析结合GeneCards、TTD和PharmGkb数据库的检索, 获取与RA相关的靶标。再将药物靶点与RA相关靶标取交集, 并利用R(v 4.2.2)对交集基因进行GO和KEGG富集分析。同时, 通过String平台构建交集靶点的蛋白互作(PPI)网络, 并利用Cytoscape (v 3.8.0)对药物-成分-靶点-疾病拓扑网络进行可视化, Cytohubba插件识别hub基因。发现苍术、黄柏的35种有效成分及其对应的673个靶点, 其中包含14种主要活性成分。此外, 获得784个与RA相关的靶点。再将药物与疾病相关的靶点取交集后, 发现药物的34种有效成分可以作用于RA相关的126个靶点。通过对药物-成分-疾病-靶点拓扑网络进行分析, 确定了5个hub基因。利用分子对接, 确定这些hub基因与药物的14种主要活性成分的结合亲和力。交集基因的富集分析结果也表明, 苍术、黄柏可以通过多成分、多靶点、多途径发挥抗RA作用。

关键词: 网络药理学, GEO, 类风湿性关节炎, 苍术, 黄柏, 分子对接

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

邓茜, 彭紫凝, 晏蔚田, 刘念, 孟凡雨, 殷建美, 张昊喆, 王兴强, 彭江云. 苍术、黄柏在类风湿关节炎中的治疗机制: 网络药理学与GEO数据的综合分析[J]. 中国药学(英文版), 2024, 33(10): 943-964.

Qian Deng, Zining Peng, Weitian Yan, Nian Liu, Fanyu Meng, Jianmei Yin, Haozhe Zhang, Xingqiang Wang, Jiangyun Peng. Elucidating the therapeutic mechanism of Cang Zhu and Huang Bai in rheumatoid arthritis: a comprehensive analysis integrating network pharmacology and GEO data[J]. Journal of Chinese Pharmaceutical Sciences, 2024, 33(10): 943-964.

图/表 12

Figure 1. Flowchart of the research process.

Table 1. Active ingredients of CZ-HB.

Figure 2. Drug-ingredient-target network. The green octagon represents two herbs, the circle represents 35 ingredients, and the diamond rectangle represents 673 targets.

Figure 3. Volcano plot of DEGs in GSE55235 (A), GSE55457 (B), and GSE77298 (C). The dark goldenrod dots represent significantly up-regulated genes, and the blue dots represent significantly down-regulated genes.

Figure 4. The heatmap of the top 20 up- and down-regulated robust DEGs ranked according to their p. adjust (FDR). Dark goldenrod represents the top 20 up-regulated robust DEGs, and blue represents the top 20 down-regulated robust DEGs. The values in the boxes indicate the robust DEGs log2 FC values.

Figure 5. The Venn diagram of RA-related targets.

Figure 6. (A) The Venn diagram of the overlapping targets of CZ-HB and RA. (B) The "drug-ingredient-target-disease" network of CZ-HB against RA. The hexagon represents two drugs, the circle represents 34 active ingredients, the diamond represents one disease, and the triangle represents 126 targets.

Figure 7. (A) The PPI network of overlapping genes of CZ-HB targets and RA-associated targets. (B) The Venn map shows 11 algorithms screened five crossover hub genes.

Figure 8. (A) GO functional enrichment analysis of target genes. In the figure, BP-enriched terms are shown in blue, CC-enriched terms are shown in yellow, and MF-enriched terms are shown in green. The cutoff value for the p. adjust (FDR) was set at 0.05. The p. adjust (FDR) is utilized to rank phrases within a category. (B) Barplot map of GO enrichment analysis.

Figure 9. (A) KEGG functional enrichment analysis of target genes. In the figure, the gray circle on the left is the core target; the earthy yellow circle represents the core target enrichment KEGG signaling pathway; the size of the circle represents the number of core targets contained in the current KEGG signaling pathway. (B) Bubble map of KEGG enrichment analysis.

Table 2. KEGG-enriched major pathways and contained genes.

Figure 10. (A) Binding energies of the 14 active ingredients and the five hub targets. The darker the red indicates, the greater the binding energy of the active ingredients and the hub targets. (B) Molecular docking patterns.

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苍术、黄柏在类风湿关节炎中的治疗机制: 网络药理学与GEO数据的综合分析

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

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