This study identified four senescence-related diagnostic biomarkers (IL6, ETS1, TDO2, and TBX2) for abdominal aortic aneurysm through transcriptomic analysis of public databases, revealing their associations with immune cell infiltration, AAA subtypes, and confirming their expression changes in a murine AAA model.
Key Findings
Results
A total of 11 senescence-related differentially expressed genes (DEGs) were identified in AAA from the intersection of DEGs, WGCNA module genes, and senescence-related gene sets.
Three transcriptomic datasets related to AAA were obtained from the GEO database.
Senescence-related gene sets were collected from MSigDB.
The 11 senescence-related DEGs were primarily involved with oxidative stress, inflammatory responses, and vascular smooth muscle cell activity.
Overlapping genes of DEGs, module genes associated with AAA, and senescence-related gene sets were identified as senescence-related DEGs of AAA.
Results
Four senescence-related genes — IL6, ETS1, TDO2, and TBX2 — were identified as diagnostic biomarkers for AAA following screening with multiple machine learning algorithms.
Multiple distinct machine learning algorithms were utilized to screen for senescence-associated biomarkers.
These four biomarkers were selected from the pool of 11 senescence-related DEGs.
A diagnostic nomogram was constructed from these four biomarkers.
The nomogram demonstrated high discriminatory ability in the training cohort with an AUC of 1.
Results
The diagnostic nomogram constructed from IL6, ETS1, TDO2, and TBX2 showed an AUC of 1 in the training cohort, though the authors noted this requires further validation due to potential overfitting.
The nomogram demonstrated 'high discriminatory ability in the training cohort (AUC = 1).'
The authors explicitly acknowledged 'this requires further validation in larger cohorts due to potential overfitting.'
The nomogram was developed to serve as a diagnostic tool for AAA.
Results
Immune cell infiltration and single-cell analyses indicated that the expression of the four diagnostic biomarkers is linked to various immune cell types in the aneurysmal environment.
The interaction between the four biomarkers (IL6, ETS1, TDO2, TBX2) and immune components in the aneurysmal environment was analyzed.
Both bulk immune cell infiltration analysis and single-cell analyses were performed.
The biomarkers were associated with various immune cell types, though specific cell types are not enumerated in the abstract.
Results
Consensus clustering classified AAA into two distinct molecular subtypes with different expression patterns of senescence-related biomarkers.
Consensus clustering was applied to classify AAA samples into distinct subtypes.
Two AAA subtypes were identified.
The two subtypes exhibited 'distinct expression patterns of senescence-related biomarkers.'
Results
Expression changes of the four senescence-related biomarkers identified in silico were confirmed in a murine AAA model.
A murine AAA model was used for experimental validation.
The expression changes of IL6, ETS1, TDO2, and TBX2 were validated in this animal model.
The murine model validation served as in vivo confirmation of the transcriptomic findings from public databases.