Autophagy-Related Genes in Atherosclerosis

Abstract

Background: Atherosclerosis (AS) is a common chronic vascular inflammatory disease and one of the main causes of cardiovascular/cerebrovascular diseases (CVDs). Autophagy-related genes (ARGs) play a crucial part in pathophysiological processes of AS. However, the expression profile of ARGs has rarely been adopted to explore the relationship between autophagy and AS. Therefore, using the expression profile of ARGs to explore the relationship between autophagy and AS may provide new insights for the treatment of CVDs.

Methods: The differentially expressed ARGs of the GSE57691 dataset were obtained from the Human Autophagy Database (HADb) and the Gene Expression Omnibus (GEO) database, and the GSE57691 dataset contains 9 aortic atheroma tissues and 10 normal aortic tissues. The differentially expressed ARGs of the GSE57691 dataset were analyzed by protein-protein interaction (PPI), gene ontology analysis (GO), and Kyoto Encyclopedia of Genes and Genomes analysis (KEGG) and were chosen to explore related miRNAs/transcriptional factors.

Results: The GSE57691 dataset had a total of 41 differentially expressed ARGs. The GO analysis results revealed that ARGs were mainly enriched in autophagy, autophagosome, and protein serine/threonine kinase activity. KEGG analysis results showed that ARGs were mainly enriched in autophagy-animal and longevity regulating signaling pathways. Expressions of ATG5, MAP1LC3B, MAPK3, MAPK8, and RB1CC1 were regarded as focus in the PPI regulatory networks. Furthermore, 11 related miRNAs and 6 related transcription factors were obtained by miRNAs/transcription factor target network analysis.

Conclusions: Autophagy and ARGs may play a vital role in regulating the pathophysiology of AS.

Figure 1

Intragroup data repeatability of the GSE57691 dataset verified by Pearson's correlation and PCA analysis. (a) Pearson's correlation analysis of intragroup data from the GSE57691 dataset. The color represents the degree of correlation. 0< correlation <1 indicates a positive correlation, and −1< correlation <0 indicates a negative correlation. When the absolute value of a number is large, there exists a strong correlation. (b) PCA analysis of intragroup data from the GSE57691 dataset. In the scatter diagram, PC1 and PC2 represent X-axis and Y-axis, respectively, where each point is a sample. The distance between the two samples represent the difference in gene expression patterns.

Figure 2

Differential expressions of ARGs between the control group and AS group. (a) The 41 differentially expressed ARGs from the GSE57691 dataset. C indicates the control group and T indicates the AS group. (b) Volcano plot of differentially expressed ARGs. Red indicates high expression genes, green indicates low expression genes, and black indicates that there is no difference in these genes between the AS group and control group.

Figure 3

GO and KEGG enrichment analysis of 41 differentially expressed ARGs. (a) Histogram of GO enrichment. (b) Histogram of KEGG enrichment.

Figure 4

PPI regulatory network and subnet module analysis of differentially expressed ARGs. The nodes represent the ARGs, and the lines indicate the interaction of two ARGs. The size and color of nodes are positively correlated with the degree and closeness centrality. (a) PPI regulatory networks. (b), (c) Subnet module analysis of PPI regulatory networks.

Figure 5

miRNAs/transcription factor target networks. The red circles indicate the upregulated ARGs, and the green circles indicate the downregulated ARGs. The violet triangle indicates miRNAs, and the yellow quadrilateral indicates transcription factors.