Xiang Qi,1 Shan Cao,2 Jian Chen,1 XiaoLei Yin1 

1Traditional Chinese Medicine (Zhong Jing) College, Henan University of Chinese Medicine, Zhengzhou, Henan, People’s Republic of China; 2School of Medicine, Henan University of Chinese Medicine, Zhengzhou, Henan, People’s Republic of China

Correspondence: Shan Cao, Email caoshan2000@163.com

Background: Atherosclerosis is a chronic inflammatory disease characterized by lipid accumulation in the vascular wall. The roles of ferroptosis and cellular senescence in Atherosclerosis remain unclear. This study aimed to identify genes related to ferroptosis and cellular senescence in Atherosclerosis using bioinformatics approaches.
Methods: Atherosclerosis gene expression datasets were obtained from the GEO database. Differentially expressed genes (DEGs) were identified and intersected with key genes from WGCNA modules, ferroptosis-related genes, and senescence-related genes to obtain common genes (CF-DEGs). Consensus clustering based on CF-DEGs was conducted to identify molecular subtypes, followed by differential expression analysis. Enrichment and immune infiltration analyses were used to investigate the biological functions and immune features of subtype-specific differentially expressed genes. Eight machine learning algorithms were applied to identify hub genes and construct a diagnostic model. Single-cell RNA-seq data were used to assess the roles of hub genes in cell communication and differentiation. Finally, animal experiments were performed to validate the expression of the hub genes.
Results: A total of 23 CF-DEGs were identified, based on which two molecular subtypes were defined. A total of 421 DEGs were found between subtypes. Immune infiltration analysis revealed significant differences in eight immune cell types, including activated dendritic cells, macrophages, NK cells, and several T cell subsets. Enrichment analysis showed that these genes were involved in fatty acid metabolism, inflammation, and immune regulation. IL1B and CCL4 were identified as hub genes. Single-cell analysis indicated that their expression changed during the monocyte-to-macrophage transition and influenced cell communication. In Atherosclerosis animal models, both genes were significantly upregulated.
Conclusion: IL1B and CCL4 are potential diagnostic biomarkers associated with ferroptosis and cellular senescence in Atherosclerosis. These findings may offer new insights into the mechanisms and diagnosis of Atherosclerosis.

Keywords: atherosclerosis, biomarker, cell senescence, ferroptosis, machine learning, single-cell RNA-Seq