Table 1.
Transcriptome datasets used in this study.
Fig 1.
Identification of gene modules associated with AAA.
(A) Selection of soft threshold power (β) based on scale-free topology model fit and mean connectivity. (B) Hierarchical clustering of gene modules post-merging. (C) Visualization of the gene dendrogram and module colors derived from hierarchical clustering. (D) Heatmap displaying the relationship between gene modules and AAA traits, with gene modules showing a correlation coefficient > 0.5 or <−0.5 and a P-value < 0.05 marked by an asterisk (*). (E) Correlation analysis between module membership and gene significance within the key gene modules. AAA, abdominal aortic aneurysm; HC, healthy control.
Fig 2.
Identification of DEGs between AAA and control groups.
(A) Heatmap showcasing the top 30 most significant DEGs. (B) Volcano plot illustrating DEGs between AAA and control groups, with red circles indicating up-regulated genes (log2 FC > 1, adjusted P < 0.05) and blue circles indicating down-regulated genes (log2 FC < −0.5, adjusted P < 0.05). (C) Identification of overlapping genes among WGCNA-derived key module genes, DEGs, and senescence-related genes. WGCNA, weighted genes co-expression network analysis; DEGs, differentially expressed genes.
Fig 3.
Functional analysis of senescence-related DEGs in AAA.
(A) PPI network illustrating the potential interactions among 11 senescence-related DEGs in AAA. (B), (C), and (D) display the top 10 most significant enrichment GO terms for Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) of these 11 senescence-related DEGs. (E) and (F) present the top 10 most significant pathways enriched in KEGG and REACTOME, respectively.
Fig 4.
Recognition of senescence-related biomarkers in AAA.
(A) LASSO logistic regression was utilized to identify senescence-related DEGs with the lowest binomial deviance. (B) Hub genes were selected based on the highest accuracy and lowest error after 5-fold cross-validation using the SVM-RFE algorithm. (C) Senescence-related DEGs were ranked by importance through random forest analysis. (D) The diagnostic error for HC, AAA, and total groups was visualized using random forest. (E) Venn diagram illustrating the overlap of 6 biomarkers identified by the three algorithms. ROC curves were generated from the 6 biomarkers in GSE57691 (F) and GSE183464 (G). Comparison of the normalized expression levels of the 6 biomarkers in GSE57691 (H) and GSE183464 (I). Kruskal-Walli’s test was applied in (H) and (I). *, P < 0.05; **, P < 0.01; ***, P < 0.001; ns, not significant.
Fig 5.
Immune infiltration landscape of AAA.
(A) Bar plot depicting the relative proportions of immune cell types infiltrated in AAA compared to controls. (B) Differential proportions of immune cell populations between AAA and control groups. The correlation between immune cell types and the expression levels of IL6 (C), ETS1 (D), TDO2 (E), and TBX2 (F) in AAA. Kruskal-Walli’s test in (B), Spearman correlation analysis in (C), (D), (E), and (F). *, P < 0.05; **, P < 0.01; ***, P < 0.001; ns, not significant.
Fig 6.
Single-cell RNA-seq analysis of AAA.
(A) Violin plot showing the distribution of single-cell sequencing data for each AAA sample after quality control. (B) Heatmap displaying the expression of characteristic genes across single-cell subpopulations in AAA tissue. (C) t-SNE plot illustrating the presence of 10 distinct immune cell types within AAA tissue. (D) Dot plot and (E) feather plot presenting the expression patterns of IL6, ETS1, TDO2, and TBX2 across various cell populations.
Fig 7.
Validation of biomarker expression in a murine AAA model.
(A) Representative images of the general aorta from sham-operated and AAA groups, with aortic diameter quantified (n = 5). Immunohistochemistry images showing ETS1 (B) and TDO2 (C) staining in sham-operated versus AAA groups. qPCR analysis of IL6, ETS1, TBX2, and TDO2 expression levels in murine aortas (D) and blood (E) 14 days after operation (n = 5). Students’ t test. *, P < 0.05; **, P < 0.01; ***, P < 0.001; ns, not significant.