Identification of immune cell infiltration and diagnostic biomarkers in unstable atherosclerotic plaques by integrated bioinformatics analysis and machine learning - Wang, Jing; Kang, Zijian; Liu, Yandong; Li, Zifu; Liu, Yang; Liu, Jianmin (Frontiers in Immunology, 2022)
Objective: The decreased stability of atherosclerotic plaques increases the risk of ischemic stroke. However, the specific characteristics of dysregulated immune cells and effective diagnostic biomarkers associated with stability in atherosclerotic plaques are poorly characterized. This research aims to investigate the role of immune cells and explore diagnostic biomarker in the formation of unstable plaques for the sake of gaining new insights into the underlying molecular mechanisms. Method: Using CIBERSORT method, 22 types of immune cells between stable and unstable atherosclerotic carotid artery tissues from RNA-sequencing and microarray data in the carotid atherosclerotic plaque public GEO database were quantitated. Differentially expressed genes (DEGs) were further calculated and were analyzed for enrichment of GO Biological Process and KEGG. Important cell types and hub genes were screened using machine learning methods including LASSO regression and Random Forest. Single cell data and clinical samples were further used to validate critical cell types and hub genes. Finally, the DGIdb database of gene-drug interaction data was utilized to find possible therapeutic medicines and show how pharmaceuticals, genes, and immune cells interacted. Results: A significant difference in immune cell infiltration was observed between unstable and stable plaques. The proportions of M0, M1 and M2 macrophages are significantly higher, and that of CD8+ T cells and NK cells are significantly lower in unstable plaques than that in stable plaques. With respect to DEGs, antigen presentation genes (CD74, B2M, HLA-DRA), inflammation-related genes (MMP9, CTSL, IFI30) and fatty acid-binding protein (CD36, APOE) were elevated in unstable plaques, while the expression of smooth muscle contraction genes (TAGLN, ACAT2, MYH10, MYH11) was decreased in in unstable plaques. M1 macrophage had highest instability score and contributed to atherosclerotic plaque instability. CD68, PAM and IGFBP6 genes were identified as the effective diagnostic markers of unstable plaques, which was validated by validation data sets and clinical samples. In addition, insulin, nivolumab, indomethacin and α-mangostin were predicted to be potential therapeutic agents for unstable plaques. Conclusion: M1 macrophage is an important cause of unstable plaque formation, and CD68, PAM, and IGFBP6 could be used as diagnostic markers to identify unstable plaques effectively.