AUTHOR=Yang Yang , Xiao Bing , Feng Xin , Chen Yue , Wang Qunhui , Fang Jing , Zhou Ping , Wei Xiang , Cheng Lin 

TITLE=Identification of hub genes and key signaling pathways by weighted gene co-expression network analysis for human aortic stenosis and insufficiency

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 10 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2023.857578

DOI=10.3389/fcvm.2023.857578

ISSN=2297-055X

ABSTRACT=Human aortic valve stenosis (AS) and insufficiency (AI) are common diseases in aging population. However, the molecular regulatory networks of AS and AI are still unclear. Here, we performed weighted genes co-expression network analysis (WGCNA) modules construction. By WGCNA algorithm, we identified highly correlated modules with the progression of AS and AI. Furthermore, 302 highly correlated genes with the degree of AS, degree of AI, and heart failure were identified from these modules. Gene Ontology (GO) analyses showed that highly correlated genes had close relationship with collagen fibril organization, extracellular matrix organization and extracellular structure organization. Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses also indicated that protein digestion and absorption, and glutathione metabolism were probably involved in AS and AI. Moreover, differentially expressed genes (DEGs) were picked out for 302 highly correlated genes in AS and AI groups relative to the normal control group. The protein‐protein interaction (PPI) network analyses were implemented using the STRING online tool and visualized with Cytoscape software to evaluate the connectivity among these highly correlated genes. The DEGs in AS and AI groups were overlapped with the top 30 genes with highest connectivity to screen out the hub genes. Finally, ten hub genes (CD74, COL1A1, TXNRD1, CCND1, COL5A1, SERPINH1, BCL6, ITGA10, FOS, and JUNB) in AS and AI were found out. The mRNA expression levels of ten hub genes were verified by analyzing the data in high throughput RNA-sequencing dataset and real-time PCR assay using AS and AI aortic valve samples. Our study may provide the underlying molecular targets for the mechanism research, diagnosis, and treatment of AS and AI in the future.