Objective: To explore the differentially expressed genes (DEGs) along with infiltrating immune cells landscape and their potential mechanisms in the progression of atherosclerosis from onset to plaque rupture.
Methods: In this study, three atherosclerosis-related microarray datasets were downloaded from the NCBI-GEO database. The gene set enrichment analysis (GSEA) was performed for interpreting the biological insights of gene expression data. The CIBERSORTx algorithm was applied to infer the relative proportions of infiltrating immune cells of the atherosclerotic samples. DEGs of the datasets were screened using R. The protein interaction network was constructed via STRING. The cluster genes were analyzed by the Cytoscape software. Gene ontology (GO) enrichment was performed via geneontology.org. The least absolute shrinkage and selection operator (LASSO) logistic regression algorithm and receiver operating characteristics (ROC) analyses were performed to build machine learning models for differentiating atherosclerosis status. The Pearson correlation analysis was carried out to illustrate the relationship between cluster genes and immune cells. The expression levels of the cluster genes were validated in two external cohorts. Transcriptional factors and drug-gene interaction analysis were performed to investigate the promising targets for atherosclerosis intervention.
Results: Pathways related to immunoinflammatory responses were identified according to GSEA analysis, and the detailed fractions infiltrating immune cells were compared between the early and advanced atherosclerosis. Additionally, we identified 170 DEGs in atherosclerosis progression (|log2FC|≥1 and adjusted p < 0.05). They were mainly enriched in GO terms relating to inflammatory response and innate immune response. A cluster of nine genes, such as ITGB2, C1QC, LY86, CTSS, C1QA, CSF1R, LAPTM5, VSIG4, and CD163, were found to be significant, and their correlations with infiltrating immune cells were calculated. The cluster genes were also validated to be upregulated in two external cohorts. Moreover, C1QA and ITGB2 may exert pathogenic functions in the entire process of atherogenesis.
Conclusions: We reanalyzed the transcriptomic signature of atherosclerosis development from onset to plaque rupture along with the landscape of the immune cell, as well as revealed new insights and specific prospective DEGs for the investigation of disease-associated dynamic molecular processes and their regulations with immune cells.
Keywords: atherosclerosis; immune cells; machine learning; microarray; plaque rupture.
Copyright © 2022 Xu, Chen and Yang.