<p>Global understanding of arterial aging remains limited despite its well-recognized medical and economic impact, hindering the development of effective mitigation strategies. The aorta, the major elastic artery, experiences a loss of homeostasis during aging that results from highly complex interactions. A promising approach to unravel the underlaying mechanisms is to investigate how aging affects the aortic transcriptome, leveraging the tremendous technological advances in RNA sequencing (RNA-seq). However, corresponding RNA-seq studies remain scarce and their interpretation hindered by a lack of standardization across experiments. In this study, we performed a meta-analysis of three publicly available bulk RNA-seq datasets from young and aged mouse aortas. After analyzing each dataset separately using a consistent bioinformatic pipeline, we combined differentially expressed genes across studies using Fisher’s method. Subsequent gene set enrichment and protein–protein interaction analyses revealed coherent and functionally annotated changes in gene expression, notably associated with a broad immune response and possible infiltration of immune cells, extracellular matrix remodeling, osteochondrogenic signaling and mineralization, and glycolytic stress. We further identified a list of genes strongly altered by aging across studies. Our findings contribute to a better characterization of aortic aging at the molecular level and may support the development of targeted therapeutic strategies.</p>