Elucidate senescence-related gene signature and immune infiltration landscape in abdominal aortic aneurysm

Background Abdominal aortic aneurysm (AAA) refers to a lasting enlargement of the abdominal aorta. Senescence, a major risk factor of AAA, demonstrate positive connection with both the formation and rupture of aneurysms. Therefore, investigating the underlying pathogenic mechanisms of senescence in AAA and exploring relevant diagnostic and therapeutic targets is crucial. Methods Three transcriptomic datasets related to AAA were obtained from the GEO database, and collection of genes associated with cellular senescence was obtained from MSigDB. Overlapping genes of differentially expressed genes (DEGs), module genes associated with AAA, and senescence-related gene sets were identified as senescence-related DEGs of AAA and subjected to further functional enrichment analysis. Distinct machine learning algorithms were subsequently utilized to screen for senescence-associated biomarkers and develop a diagnostic nomogram. In addition, the interaction between these biomarkers and immune components in the aneurysmal environment were revealed. Consensus clustering was subsequently applied to classify AAA into distinct subtypes. Finally, validation was performed using an AAA murine model. Results A total of 11 senescence-related DEGs in AAA were identified, which mainly involved with oxidative stress, inflammatory responses, and vascular smooth muscle cell activity. Following rigorous screening, IL6, ETS1, TDO2, and TBX2 were identified as diagnostic biomarkers for senescence-related DEGs of AAA. The nomogram constructed from these biomarkers demonstrated high discriminatory ability in the training cohort (AUC = 1), though this requires further validation in larger cohorts due to potential overfitting. Immune cell infiltration and single-cell analyses indicated that the expression of the diagnostic biomarkers is linked to various immune cell types. Consensus clustering identified two AAA subtypes, which exhibiting distinct expression patterns of senescence-related biomarkers. Finally, validation in an AAA murine model confirmed the expression changes of these senescence-related biomarkers in AAA. Conclusion This study identified senescence-related biomarkers associated with AAA through transcriptomic public databases, revealing their potential functional mechanisms, relationships with immune cells, and associations with AAA subtypes. These results could offer novel candidate targets for both diagnostic and therapeutic strategies in AAA.