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. 2021 Feb 5:12:601952.
doi: 10.3389/fphys.2021.601952. eCollection 2021.

Weighted Gene Co-expression Network Analysis Identifies Crucial Genes Mediating Progression of Carotid Plaque

Mengyin Chen  1 , Siliang Chen  1 , Dan Yang  2 , Jiawei Zhou  1 , Bao Liu  1 , Yuexin Chen  1 , Wei Ye  1 , Hui Zhang  1 , Lei Ji  1 , Yuehong Zheng  1
Affiliations

Weighted Gene Co-expression Network Analysis Identifies Crucial Genes Mediating Progression of Carotid Plaque

Mengyin Chen et al. Front Physiol. .

Abstract

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.

Keywords: RNA sequencing; carotid plaque; crucial genes; gene expression omnibus; weighted gene co-expression network analysis.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Workflow of the whole study.
FIGURE 2
FIGURE 2
DEG screening. (A) Heatmap. The heatmap showed the expression pattern of genes. (B) Volcano plot. The x-axis represents the -log10(adj.P.Val) while they-axis represents log2(fold-change). The red dots represent up-regulated DEGs while the green dots represent the down-regulated DEGs.
FIGURE 3
FIGURE 3
Construction of the co-expression network for advanced plaque. (A) No outliers were detected in the sample clustering and all samples were included in further study. (B,C) The cut-off for soft-threshold β was set to be 0.85 and β = 14 was selected. (D,E) The co-expression network we constructed met the requirements of scale-free topology. (F) In advanced plaque sample, 7 gene modules were detected.
FIGURE 4
FIGURE 4
Functional enrichment analysis for GO anal and KEGG pathways for black module of advanced plaque. (A,B) Bar plot and dot plot for GO terms of genes in black module. Terms in bar plot and dot plot were ordered by adjusted p value and count number respectively. (C,D) Bar plot and dot plot for KEGG pathways of genes in black module. Terms in bar plot and dot plot were ordered by adjusted p value and count number respectively.
FIGURE 5
FIGURE 5
MCODE cluster and crucial genes. (A) Subnetwork of the most significant MCODE cluster. The red boxes represent the up-regulated genes while the green boxes represent down-regulated genes. (B) Genes with top-10 degree were considered as crucial genes. NCF2, IQGAP2, and CD86 were the genes with the highest degree among these crucial genes.
FIGURE 6
FIGURE 6
ROC curves for NCF2, IQGAP2, and CD86. (A) The AUC for NCF2 was 0.918. (B) The AUC for IQGAP2 was 0.853. (C) The AUC for CD86 was 0.903.
FIGURE 7
FIGURE 7
Validation of crucial genes by GSE43292. All crucial genes were found in the DEG co-expression network of the validation set (***p < 0.001).
FIGURE 8
FIGURE 8
Lasso regression and LDA analysis. (A) Lasso coefficient profiles of 100 genes. (B) 10-fold cross-validation for selecting minimal λ based on 1-SE criteria for recurrence. A total of 15 genes were selected. (C) There was a clear shift in LDA function, with a left shift being observed for early plaques and a right shift for advanced plaques.
FIGURE 9
FIGURE 9
Single gene GSEA for 3 crucial genes with highest degree.
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