Frontiers in Cardiovascular Medicine (Aug 2023)

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

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

DOI
https://doi.org/10.3389/fcvm.2023.857578
Journal volume & issue
Vol. 10

Abstract

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BackgroundHuman aortic valve stenosis (AS) and insufficiency (AI) are common diseases in aging population. Identifying the molecular regulatory networks of AS and AI is expected to offer novel perspectives for AS and AI treatment.MethodsHighly correlated modules with the progression of AS and AI were identified by weighted genes co-expression network analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by the clusterProfiler program package. Differentially expressed genes (DEGs) were identified by the DESeqDataSetFromMatrix function of the DESeq2 program package. The protein-protein interaction (PPI) network analyses were implemented using the STRING online tool and visualized with Cytoscape software. The DEGs in AS and AI groups were overlapped with the top 30 genes with highest connectivity to screen out ten hub genes. The 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.ResultsBy WGCNA algorithm, 302 highly correlated genes with the degree of AS, degree of AI, and heart failure were identified from highly correlated modules. GO analyses showed that highly correlated genes had close relationship with collagen fibril organization, extracellular matrix organization and extracellular structure organization. KEGG analyses also manifested that protein digestion and absorption, and glutathione metabolism were probably involved in AS and AI pathological courses. Moreover, DEGs were picked out for 302 highly correlated genes in AS and AI groups relative to the normal control group. The PPI network analyses indicated the connectivity among these highly correlated genes. Finally, ten hub genes (CD74, COL1A1, TXNRD1, CCND1, COL5A1, SERPINH1, BCL6, ITGA10, FOS, and JUNB) in AS and AI were found out and verified.ConclusionOur study may provide the underlying molecular targets for the mechanism research, diagnosis, and treatment of AS and AI in the future.

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