Decoding marker genes and immune landscape of unstable carotid plaques from cellular senescence

Abstract

Abstract Recently, cellular senescence-induced unstable carotid plaques have gained increasing attention. In this study, we utilized bioinformatics and machine learning methods to investigate the correlation between cellular senescence and the pathological mechanisms of unstable carotid plaques. Our aim was to elucidate the causes of unstable carotid plaque progression and identify new therapeutic strategies. First, differential expression analysis was performed on the test set GSE43292 to identify differentially expressed genes (DEGs) between the unstable plaque group and the control group. These DEGs were intersected with cellular senescence-associated genes to obtain 40 cellular senescence-associated DEGs. Subsequently, key genes were then identified through weighted gene co-expression network analysis, random forest, Recursive Feature Elimination for Support Vector Machines algorithm and cytoHubba plugin. The intersection yielded 3 CSA-signature genes, which were validated in the external validation set GSE163154. Additionally, we assessed the relationship between these CSA-signature genes and the immune landscape of the unstable plaque group. This study suggests that cellular senescence may play an important role in the progression mechanism of unstable plaques and is closely related to the influence of the immune microenvironment. Our research lays the foundation for studying the progression mechanism of unstable carotid plaques and provides some reference for targeted therapy.

Keywords

• Unstable plaques
• Cellular aging
• Genetics
• Immune infiltration
• Machine learning