Identification of key pathways and genes in carotid atherosclerosis through bioinformatics analysis of RNA-seq data

Abstract

While acknowledging carotid atherosclerosis (CAS) as a risk factor for ischemic stroke, reports on its pathogenesis are scarce. This study aimed to explore the potential mechanism of CAS through RNA-seq data analysis. Carotid intima tissue samples from CAS patients and healthy subjects were subjected to RNA-seq analysis, which yielded, 1,427 differentially expressed genes (DEGs) related to CAS. Further, enrichment analysis (Gene Ontology, KEGG pathway, and MOCDE analysis) was performed on the DEGs. Hub genes identified via the protein-protein interaction network (PPI) were then analyzed using TRRUST, DisGeNET, PaGenBase, and CMAP databases. Results implicated inflammation and immunity in the pathogenesis of CAS. Also, lung disease was associated with CAS. Hub genes were expressed in multiple diseases, mainly regulated by RELA and NFKB1. Moreover, three small-molecule compounds were found via the CMAP database for management of CAS; hub genes served as potential targets. Collectively, inflammation and immunity are the potential pathological mechanisms of CAS. This study implicates CeForanide, Chenodeoxycholic acid, and 0317956-0000 as potential drug candidates for CAS treatment.

Keywords: RNA-seq; bioinformatics analysis; carotid atherosclerosis; enrichment analysis; hub gene.

Figure 1

The analysis flow chart of the study. CAS: Carotid atherosclerosis. DEGs: differentially expressed genes. DEseq2: R package for transcriptional sequencing. FC: Fold change. |logFC|>3: The logarithm of the ratio of mRNA expression in CAS patients to that of healthy people is more than 3 times. Adj.p.Val: corrected p-value. PPI: protein-protein interaction. TF enrichment: transcription factor enrichment.

Figure 2

Analysis of DEGs. Boxplot (A) and density plot (B) showing that gene expression levels of different samples are evenly distributed. (C) Correlation diagram of gene expression level between samples indicates that the correlation coefficient between samples of the same group is high and the sample selection is reliable. (D) Principal component analysis diagram (PCA) showing that samples of different groups were significantly different, and samples within the same group were relatively uniform. (E) Heat map of DEGs, red represents high expression, blue represents low expression. (F) Volcano map of DEGs, screening parameters were |logFC|>3, adj.P.Val <0.05, 710 up-regulated genes (red dots), and 717 down-regulated genes (green dots) were identified, genes with no significant changes were labeled as black dots. ASA: Carotid atherosclerosis group. NA: Normal control group. FC: Fold change. Adj.p.Val: corrected p-value.

Figure 3

GO and KEGG enrichment analysis of DEGs. (A) GO enrichment analysis of DEGs, BP represents the biological process, CC represents the cellular component, and MF represents the molecular function. (B) KEGG pathway analysis of DEGs, adj.p.Val is corrected p-value, the count represents the number of DEGs. (C) Interaction between DEGs and top 5 terms of GO entries. (D) Interaction between DEGs and top 5 disease pathways in KEGG. DEGs: Differentially expressed genes. KEGG: Kyoto Encyclopedia of Genes and Genomes. FC: Fold change. Adj.p.Val: corrected p-value.

Figure 4

MCODE analysis of DEGs. DEGs were divided into 18 modules based on functions, and protein-protein interaction networks were constructed for DEGs of each module. DEGs: Differentially expressed genes.

Figure 5

PPI network constructing and hub gene screening of DEGs. (A) PPI network constructed using STRING Online Database contained 1055 nodes and 1883 edges. (B) PPI network was optimized through Cytoscape software. (C) 20 hub genes identified through degree method in cytoHubba. PPI network: protein-protein interaction network.

Figure 6

Bioinformatics analysis of hub genes. (A) Biological functions of hub genes analyzed through Metascape database. (B) Enrichment of transcriptional regulators of hub genes using the TRRUST database. (C) DisGeNET database enrichment analysis of diseases involving hub genes. (D) Tissue characteristics of hub genes as analyzed using PaGenBase database.