Identification of candidate target genes for human peripheral arterial disease using weighted gene co-expression network analysis

Abstract

The aim of the present study was to identify the potential treatment targets of peripheral arterial disease (PAD) and provide further insights into the underlying mechanism of PAD, based on a weighted gene co‑expression network analysis (WGCNA) method. The mRNA expression profiles (accession. no. GSE27034), which included 19 samples from patients with PAD and 18 samples from normal control individuals were extracted from the Gene Expression Omnibus database. Subsequently, the differentially expressed genes (DEGs) were obtained using the Limma package and the co‑expression network modules were screened using the WGCNA approach. In addition, the protein‑protein interaction network for the DEGs in the most significant module was constructed using Cytoscape software. Functional enrichment analyses of the DEGs in the most significant module were also performed using the Database for Annotation, Visualization and Integrated Discovery and Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology‑Based Annotation System, respectively. A total of 148 DEGs were identified in PAD, which were used to construct the WGCN, in which two modules (gray module and turquoise module) were identified, with the gray module exhibiting a higher gene significance (GS) value than the turquoise module. In addition, a co‑expression network was constructed for 60 DEGs in the gray module. The functional enrichment results showed that the DEGs in the gray module were enriched in five Gene Ontology terms and four KEGG pathways. For example, cyclin‑dependent kinase inhibitor 1A (CDKN1A), FBJ murine osteosarcoma viral oncogene homolog (FOS) and prostaglandin‑endoperoxide synthase 2 (PTGS2) were enriched in response to glucocorticoid stimulus. The results of the present study suggested that DEGs in the gray module, including CDKN1A, FOS and PTGS2, may be associated with the pathogenesis of PAD, by modulating the cell cycle, and may offer potential for use as candidate treatment targets for PAD.

Integrated approach reveals diet, APOE genotype and sex affect immune response in APP mice

“…Considering the complexity of our study, we chose network analysis as the most appropriate tool to correlate the gene expression data to diet, APOE isoform, and sex. WGCNA is an unbiased approach that organizes genes into functional networks (modules) based on topological overlap between the genes and is widely used to investigate and reveal candidate target genes in several diseases [49–51]. Using WGCNA, we identified distinct networks that significantly correlated to HFD and APOE4 isoform in females and in males.…”

Weighted gene co-expression network analysis reveals specific modules and hub genes related to neuropathic pain in dorsal root ganglions

“…This modeling can be further correlated with clinical information, such as the stage of disease. In the last few years, WGCNA has been widely used to detect co-expressed modules and hub genes in various diseases including cancer [4], coronary artery disease [5] and peripheral arterial disease [6].…”

MicroRNA‑93 regulates angiogenesis in peripheral arterial disease by targeting CDKN1A

“…Previous studies have emphasized the functional implications of CDKN1A during the development of PAD (45,46). As previously stated, PAD is an atherosclerotic disease, in which the pathological response contributes to a complex inflammatory reaction induced by endothelial functional loss (45).…”

“…Previous studies have emphasized the functional implications of CDKN1A during the development of PAD (45,46). As previously stated, PAD is an atherosclerotic disease, in which the pathological response contributes to a complex inflammatory reaction induced by endothelial functional loss (45). In the present study, using a luciferase reporter assay, CDKN1A was identified as a direct target of miR-93 in EA.hy926 cells, suggesting that miR-93 exerts an important effect in the regulation of angiogenesis during PAD via binding to CDKN1A.…”