Efficacy-driving Mechanism of Danhong Injection for Stable Angina Pectoris Based on Composition-activity Relationship of Target Modules
10.13422/j.cnki.syfjx.20241515
- VernacularTitle:基于靶点模块组效关系的丹红注射液治疗心绞痛疗效驱动机制
- Author:
Siwei TIAN
1
;
Wenjing ZONG
1
;
Jun LIU
2
;
Wei YANG
2
;
Qikai NIU
1
;
Siqi ZHANG
1
;
Jing'ai WANG
1
;
Huamin ZHANG
3
;
Zhong WANG
2
;
Bing LI
1
Author Information
1. Institute of Chinese Materia Medica， China Academy of Chinese Medical Sciences， State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs， Institute of Chinese Materia Medica， China Academy of Chinese Medical Sciences， Beijing 100700， China
2. Institute of Basic Research in Clinical Medicine， China Academy of Chinese Medical Sciences， Beijing 100700，China
3. Institute of Basic Theory for Chinese Medicine， China Academy of Chinese Medical Sciences，Beijing 100700，China
- Publication Type:Journal Article
- Keywords:
target module;
efficacy driven;
Danhong injection;
mechanism
- From:
Chinese Journal of Experimental Traditional Medical Formulae
2024;30(23):121-128
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveTo explore the efficacy-driving mechanism of Danhong injection （DHI） in the treatment of stable angina pectoris （SAP） based on the composition-activity relationship of target modules and clarify the pharmacological effects of DHI. MethodAccording to the angina frequency （AF） in the Seattle Angina Questionnaire （SAQ） that was obtained in the previous clinical trial， the patients before and after DHI treatment were grouped based on efficacy. The transcriptomic data of the patients before treatment and in the best efficacy group 30 days post-treatment were selected as the data source， and then weighted gene co-expression network analysis （WGCNA） was employed to construct the co-expression network. Relevant modules in the network were identified and associated with clinical features. In addition， the On-modules （Z value below 0） were identified by Zsummary. The topological indicators such as density， centrality， and clustering coefficient were adopted to explore the dynamics of DHI efficacy at the network level and module level， respectively. In addition， the driver genes were screened by the personalized network control （PNC） algorithm. Finally， rat H9C2 cells were used to establish the model of hypoxia/reoxygenation （H/R）， which was used to confirm the potential therapeutic target of DHI for SAP and provide a scientific basis for revealing the therapeutic mechanism of DHI. ResultWe identified 19 modules in the best efficacy group of DHI for SAP， and the comparison between day 0 and day 30 revealed 12 On-modules. The changes of network topological indicators at the network and module levels confirmed the correlation between the best efficacy of DHI treatment and topological dynamics. Finally， the driver genes， Klotho and fibroblast growth factor 22 （FGF22）， in DHI treatment of SAP were verified by the H9C2 cell model of H/R. ConclusionBased on clinical transcriptome data， this study determined the composition-activity relationship of target modules of DHI for SAP， which provided a scientific basis for deciphering the efficacy-driven mechanism of DHI for SAP.
