The underlying molecular mechanisms and biomarkers of plaque vulnerability based on bioinformatics analysis

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

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.

Bioinformatics-based identification and validation of hub genes associated with aging in patients with coronary artery disease

Coronary artery disease (CAD) is the most common aging-related disease in adults. We used bioinformatics analysis to study genes associated with aging in patients with CAD. The microarray data of the GSE12288 dataset were downloaded from the Gene Expression Omnibus database to obtain 934 CAD-associated differentially expressed genes. By overlaying them with aging-related genes in the Aging Atlas database, 33 differentially expressed aging-related genes (DEARGs) were identified. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed that the 33 DEARGs were mainly enriched in cell adhesion and activation, Th17 and Th1/Th2 cell differentiation, and longevity regulation pathways. Hub genes were further screened using multiple algorithms of Cytoscape software and validation set GSE71226. Clinical samples were then collected, and the expression of hub genes in whole blood was detected by real-time quantitative polymerase chain reaction, enzyme-linked immunosorbent assay, and western blot at the transcription and translation levels. Finally, HSP90AA1 and CEBPA were identified as hub genes. The results of this study suggest that HSP90AA1 and CEBPA are closely related to CAD. These findings provide a theoretical basis for the association between aging effectors and CAD, and indicate that these genes may be promising biomarkers for the diagnosis and treatment of CAD.