Background: Arteriosclerosis (AS) is a globally prevalent disease for which there is currently no effective treatment. Therefore, identifying potential drug targets is of crucial importance. The Cyclic guanosine monophosphate–adenosine monophosphate synthase (cGAS)–STING pathway is an intracellular immune sensing mechanism that detects DNA damage and viral infections, and it could contribute to the progression of AS through its influence on immune regulation. By using bioinformatics methods, we identified critical genes involved in the progression of AS that are associated with the cGAS–STING pathway, providing potential molecular targets for further investigation.

Methods: Datasets related to AS were extracted from the Gene Expression Omnibus (GEO) database, including GSE100927, GSE28829, and GSE43292. Furthermore, we conducted a search in the GeneCards database to identify genes associated with the cGAS–STING signaling pathway. Key gene identification was carried out through comparative expression analysis using Limma and weighted gene co-expression network analysis (WGCNA). Machine learning (ML) algorithms were then applied to screen and evaluate the diagnostic value of potential biomarkers. After obtaining both cGAS–STING-related and AS-related differentially expressed genes (cASDEGs), we utilized gene set enrichment analysis (GSEA) approach to analyze the potential function of cASDEGs and CIBERSORT algorithm to assess immune cell infiltration in AS. Finally, experiments including reverse transcription quantitative polymerase chain reaction (RT-qPCR), Western blotting and immunofluorescence were conducted to validate the expression patterns of cASDEGs.

Results: By intersecting the Limma and WGCNA results, we identified a total of 741 differentially expressed genes (DEGs) related to AS from the GSE100927 dataset. An intersection of these DEGs with the cGAS–STING-related genes identified 16 cASDEGs. Four pivotal cASDEGs (C-src tyrosine kinase (CSK), fatty acid binding protein 5 (FABP5), B-cell lymphoma 2-associated athanogene (BAG2), and alpha-galactosidase A (GLA)) were identified through the ML-based screening, all of which showing significant diagnostic relevance. Further immune profiling uncovered dysregulated immunity in AS and a link between these genes and immune cell interactions. Experimental validation confirmed that the four central cASDEGs exhibited expression patterns aligning with the bioinformatics predictions.

Conclusion: Our study identified four genes (CSK, FABP5, BAG2, and GLA) that may promote the development of AS through the cGAS–STING pathway, providing new insights into the pathogenesis and potential treatment of AS.