Abstract
Background
Atherosclerosis and metabolic syndrome are the main causes of cardiovascular events, but their underlying mechanisms are not clear. In this study, we focused on identifying genes associated with diagnostic biomarkers and effective therapeutic targets associated with these two diseases.
Methods
Transcriptional data sets of atherosclerosis and metabolic syndrome were obtained from GEO database. The differentially expressed genes were analyzed by RSTUDIO software, and the function-rich and protein-protein interactions of the common differentially expressed genes were analyzed.
Results
A total of 1242 differential genes (778 up-regulated genes and 464 down-regulated genes) were screened from GSE28829 data set. A total of 1021 differential genes (492 up-regulated genes and 529 down-regulated genes) were screened from the data set GSE98895.Then 23 up-regulated genes and 11 down-regulated genes were screened by VENN diagram.Functional enrichment analysis showed that cytokines and immune activation were involved in the occurrence and development of these two diseases.Through the construction of PPI network and Cytoscape software analysis, we finally screened 10 HUB genes.The immune infiltration analysis was further improved. The results showed that the infiltration scores of 7 kinds of immune cells in GSE28829 were significantly different among groups (Wilcoxon Test < 0.05), while in GSE98895, the infiltration scores of 4 kinds of immune cells were significantly different between groups (Wilcoxon Test < 0.05).Spearman method was used to analyze the correlation between the expression of 10 key genes and 22 kinds of immune cell infiltration scores in two data sets.The results showed that there were 42 pairs of significant correlations between 10 genes and 22 kinds of immune cells in GSE28829 (| Cor | > 0.3 & P < 0.05).There were 41 pairs of significant correlations between 10 genes and 22 kinds of immune cells in GSE98895 (| Cor | > 0.3 & P < 0.05).Finally, our results identified 10 small molecules with the highest absolute enrichment value, and the three most significant key genes (CX3CR1, TLR5, IL32) were further verified in the data expression matrix and clinical blood samples.
Conclusion
We have established a co-expression network between atherosclerotic progression and metabolic syndrome, and identified key genes between the two diseases. this may be helpful to provide new research ideas for the diagnosis and treatment of atherosclerosis complicated with metabolic syndrome.