Weighted gene co-expression network analysis identifies specific modules and hub genes related to coronary artery disease

Abstract

This investigation seeks to dissect coronary artery disease molecular target candidates along with its underlying molecular mechanisms. Data on patients with CAD across three separate array data sets, GSE66360, GSE19339 and GSE97320 were extracted. The gene expression profiles were obtained by normalizing and removing the differences between the three data sets, and important modules linked to coronary heart disease were identified using weighted gene co-expression network analysis (WGCNA). Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and genomes (KEGG) pathway enrichment analyses were applied in order to identify statistically significant genetic modules with the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool (version 6.8; http://david.abcc.ncifcrf.gov ). The online STRING tool was used to construct a protein-protein interaction (PPI) network, followed by the use of Molecular Complex Detection (MCODE) plug-ins in Cytoscape software to identify hub genes. Two significant modules (green-yellow and magenta) were identified in the CAD samples. Genes in the magenta module were noted to be involved in inflammatory and immune-related pathways, based on GO and KEGG enrichment analyses. After the MCODE analysis, two different MCODE complexes were identified in the magenta module, and four hub genes (ITGAM, degree = 39; CAMP, degree = 37; TYROBP, degree = 28; ICAM1, degree = 18) were uncovered to be critical players in mediating CAD. Independent verification data as well as our RT-qPCR results were highly consistent with the above finding. ITGAM, CAMP, TYROBP and ICAM1 are potential targets in CAD. The underlying mechanism may be related to the transendothelial migration of leukocytes and the immune response.

Figure 1

Analysis of network topology for various soft-thresholding powers. The left panel shows the scale-free fit index (y-axis) as a function of the soft-thresholding power (x-axis). The right panel displays the mean connectivity (degree, y-axis) as a function of the soft-thresholding power (x-axis).

Figure 2

Heatmap plot of topological overlap in the gene network. In the heatmap, each row and column correspond to a gene, light color denotes low topological overlap, and progressively darker red denotes higher topological overlap. Darker squares along the diagonal correspond to modules. The gene dendrogram and module assignment are shown along the left and top.

Figure 3

Clustering dendrogram of genes. Gene clustering tree (dendrogram) obtained by hierarchical clustering of adjacency-based dissimilarity. The colored row below the dendrogram indicates module membership identified by the dynamic tree cut method, together with assigned merged module colors and the original module colors.

Figure 4

Module-feature associations. Each row corresponds to a modulEigengene and the column to the clinical phenotype. Each cell contains the corresponding correlation in the first line and the P-value in the second line. The table is color-coded by correlation according to the color legend.

Figure 5

GO functional and KEGG pathway enrichment analyses for genes in the object module. The x-axis shows the number of genes and the y-axis shows the GO and KEGG pathway terms. The − log10 (P-value) of each term is colored according to the legend. (A) GO functional enrichment analysis. (B) KEGG pathway enrichment analysis.

Figure 6

PPI network construction and identification of hub genes. (A) PPI network of genes in magenta module. The edge shows the interaction between two genes. Significant modules identified from the PPI network using the MCODE with a score > 6.0. (A-1) Molecular-1 with MCODE score = 26.2. (A-2) Molecular-2 with MCODE score = 13.4.

Figure 7

Four identified hub genes were verified by RT-qPCR. *P < 0.001.

Figure 8

The ROC curves for the predictive values of ITGAM, CAMP, TYROBP and ICAM1 to identify CAD patients from healthy controls. (A) The AUC of ITGAM in CAD was 0.714 with a sensitivity of 74.3% and a specificity of 76.2%. (B) The AUC of CAMP in CAD was 0.897 with a sensitivity of 81.2% and a specificity of 91.1%. (C) The AUC of TYROBP in CAD was 0.761 with a sensitivity of 77.1% and a specificity of 79.7%. (D) The AUC of ICAM1 in CAD was 0.848 with a sensitivity of 83.3% and a specificity of 86.7%.