Table_2_Weighted Gene Co-expression Network Analysis Identifies Crucial Genes Mediating Progression of Carotid Plaque.XLSX (517.84 kB)
Download file

Table_2_Weighted Gene Co-expression Network Analysis Identifies Crucial Genes Mediating Progression of Carotid Plaque.XLSX

Download (517.84 kB)
dataset
posted on 05.02.2021, 13:43 by Mengyin Chen, Siliang Chen, Dan Yang, Jiawei Zhou, Bao Liu, Yuexin Chen, Wei Ye, Hui Zhang, Lei Ji, Yuehong Zheng
Background

Surface rupture of carotid plaque can cause severe cerebrovascular disease, including transient ischemic attack and stroke. The aim of this study was to elucidate the molecular mechanism governing carotid plaque progression and to provide candidate treatment targets for carotid atherosclerosis.

Methods

The microarray dataset GSE28829 and the RNA-seq dataset GSE104140, which contain advanced plaque and early plaque samples, were utilized in our analysis. Differentially expressed genes (DEGs) were screened using the “limma” R package. Gene modules for both early and advanced plaques were identified based on co-expression networks constructed by weighted gene co-expression network analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes Genomes (KEGG) analyses were employed in each module. In addition, hub genes for each module were identified. Crucial genes were identified by molecular complex detection (MCODE) based on the DEG co-expression network and were validated by the GSE43292 dataset. Gene set enrichment analysis (GSEA) for crucial genes was performed. Sensitivity analysis was performed to evaluate the robustness of the networks that we constructed.

Results

A total of 436 DEGs were screened, of which 335 were up-regulated and 81 were down-regulated. The pathways related to inflammation and immune response were determined to be concentrated in the black module of the advanced plaques. The hub gene of the black module was ARHGAP18 (Rho GTPase activating protein 18). NCF2 (neutrophil cytosolic factor 2), IQGAP2 (IQ motif containing GTPase activating protein 2) and CD86 (CD86 molecule) had the highest connectivity among the crucial genes. All crucial genes were validated successfully, and sensitivity analysis demonstrated that our results were reliable.

Conclusion

To the best of our knowledge, this study is the first to combine DEGs and WGCNA to establish a DEG co-expression network in carotid plaques, and it proposes potential therapeutic targets for carotid atherosclerosis.

History

References