Establishment of a Calcified Aortic Valve Disease-related Gene Regulatory Network by Gene Co-expression Networks Analysis
10.3969/j.issn.1000-3614.2025.05.007
- VernacularTitle:基于共表达模式的钙化性主动脉瓣疾病基因调控网络构建方法研究
- Author:
Hongxia QI
1
;
Haibo GU
;
Chengfeng WANG
;
Hui LI
;
Yan'e LI
Author Information
1. 中国医学科学院 北京协和医学院 国家心血管病中心 阜外医院 超声影像中心,北京 100037
- Publication Type:Journal Article
- Keywords:
calcified aortic valve disease;
gene regulatory module;
liquid association;
enrichment analysis
- From:
Chinese Circulation Journal
2025;40(5):486-493
- CountryChina
- Language:Chinese
-
Abstract:
Objectives:This study aims to establish a calcified aortic valve disease(CAVD)-related gene regulatory network,and clarify the impact of interactions between CAVD-related genes on CAVD.Methods:Differential expression gene method was used to screen candidate genes,and STRING software was used to construct protein-protein interaction networks for screened genes.The networks and genes with the highest scores were obtained.Monte Carlo method was used to rank the importance of genes in CAVD,and the top 1%genes were identified.Genes identified by these two methods are intersected to obtain the key genes.The correlation between the key genes and CAVD were confirmed by biological verification method.Then,key genes were used as query genes to establish CAVD gene regulatory module with a co-expression patterns-based gene regulatory network identifying method.Specifically,we first detected the underlying co-expression patterns of the seemingly uncorrelated genes,and then pieced the isolated gene pairs up into gene regulatory network with the help of the bridging genes.Finally,we verified the function of the established gene regulatory network through enrichment analysis and literature analysis.Results:We identified the CAVD gene regulatory network containing 211 genes from 18 084 candidate genes.Functional enrichment analysis indicate that the established gene regulatory network included genes with both known interactions and potential novel pathway interactions related to CAVD,serving as candidates for further experimental CAVD studies.Conclusions:Methodologically,the proposed method avoids the problem of complex computation,and relies only on available biological priors.It is also able to detect underlying co-expression patterns.The results will advance the understanding of the interactions of the CAVD-related genes and this method provides a novel way to identify underlying gene co-expression patterns in the setting of CAVD.
