Integrated analysis of the diagnostic value and mechanism of action of the senescence-associated secretory phenotype in thoracic aortic aneurysm relying on bulk RNA-Seq and scRNA-Seq

Integrated analysis of the diagnostic value and mechanism of action of the senescence-associated secretory phenotype in thoracic aortic aneurysm relying on bulk RNA-Seq and scRNA-Seq | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrated analysis of the diagnostic value and mechanism of action of the senescence-associated secretory phenotype in thoracic aortic aneurysm relying on bulk RNA-Seq and scRNA-Seq Tengyue Zhao, Bingjie Wang, Ce Feng, Hao Yu, Kaichuan He, Ziying Chen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7912284/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Thoracic aortic aneurysm (TAA) eventually causes aortic intima rupture, and the senescence-associated secretory phenotype (SASP) has been found to promote TAA. Thus, identifying TAA early via SASP-related genes (SASP-RGs) is highly significant. Methods Differential analysis was performed on TAA datasets from GEO; SASP-RGs were expanded via weighted gene co-expression network analysis. Candidate key genes were obtained by combining these two analyses with protein-protein interaction, then identified via least absolute shrinkage and selection operator and expression verification. A nomogram was built using key genes, and immune cell infiltration in TAA was examined. Meanwhile, pathway enrichment and regulatory networks of key genes were explored. Key TAA-related cells in single-cell datasets and key gene expression in them were determined, with key gene expression validated in clinical samples. Results Among 36 candidates, CR1 and F13A1 were key genes; the nomogram had good diagnostic value. In TAA, infiltration of immune cells like macrophages and natural killer cells increased, showing positive correlation with key genes. Key genes were associated with the "chemokine signaling pathway" and regulated by transcription factors (e.g., SCL, SOX2). Macrophages were key cells; F13A1 expression fluctuated during macrophage development. Key gene expression was verified by PCR in clinical samples. Conclusion SASP-RGs play important roles in TAA and have diagnostic value, providing a basis for TAA early diagnosis and prevention. Thoracic aortic aneurysm Senescence-associated secretory phenotype Key genes Diagnosis Aging Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Thoracic aortic aneurysm (TAA) is a life-threatening cardiovascular disease with complex pathogenesis. Genetic predisposition accounts for approximately 20% of cases, involving mutations in genes such as FBN1 and TGFBR1, while key environmental risk factors include hypertension and smoking 1 , 2 . Pathologically, TAA is characterized by progressive dilation of the thoracic aorta exceeding 50% of the normal diameter. In Western countries, TAA-related mortality constitutes 1–2% of total mortality. Acute aortic dissection (AAD) may lead to sudden death, and survivors often suffer from multi-organ dysfunction 3 . Current therapeutic strategies for TAA primarily include open surgical repair and thoracic endovascular aortic repair (TEVAR). However, both approaches are associated with high perioperative mortality, and TEVAR carries a notable risk of endoleak. Small TAAs are typically managed with watchful waiting, while pharmacological interventions remain limited 4 . TAA is often asymptomatic in early stages, yet poses a high risk of rupture and fatality. For instance, approximately 50% of patients with AAD die before hospital evaluation 5 . Therefore, early diagnosis and intervention are critical for TAA patients to reduce mortality and improve quality of life. The senescence-associated secretory phenotype (SASP) refers to the phenomenon wherein senescent cells secrete inflammatory cytokines, chemokines, and matrix metalloproteinases (MMPs). During organismal aging, SASP reinforces senescence through paracrine/autocrine mechanisms, driving chronic inflammation and accelerating aging, while paradoxically participating in tissue repair and embryonic development. Accumulating evidence indicates that SASP is closely linked to the pathogenesis of cancer, cardiovascular diseases, and neurodegenerative disorders 6 , 7 . Although the role of SASP in the pathogenesis and diagnostic value of TAA remains incompletely defined, emerging evidence suggests its potential involvement in TAA progression. Inflammation plays a pivotal role in TAA development, and SASP-derived inflammatory mediators may exacerbate local inflammatory responses, thereby disrupting the normal structure and function of the vascular wall. Additionally, vascular smooth muscle cell (VSMC) senescence is critically implicated in TAA pathogenesis, and SASP likely promotes VSMC senescence, further compromising vascular wall stability. Furthermore, apoptosis has been associated with TAA, and SASP may modulate apoptotic pathways, perturbing cellular homeostasis within the vascular wall and ultimately contributing to TAA initiation and progression 8 . Single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing are pivotal technologies for gene expression profiling. scRNA-seq employs optimized next-generation sequencing (NGS) to resolve cellular heterogeneity at the single-cell level, enabling the identification of gene expression signatures across distinct cell subpopulations. For instance, scRNA-seq has identified 20 distinct cell clusters in ovarian cancer and delineated 15 pancreatic cell subtypes in acute pancreatitis 9 . In contrast, bulk RNA sequencing analyzes gene expression profiles at the population level, providing an integrated overview of transcriptional activity, as demonstrated in studies of bladder cancer and hepatocellular carcinoma 10 . The combined application of scRNA-seq and bulk RNA sequencing allows multi-scale investigation of gene expression regulation—from global patterns to single-cell resolution—thereby overcoming limitations inherent to either approach alone. In cancer research, this integrative strategy facilitates exploration of tumor microenvironment dynamics, including cell-cell interactions and regulatory mechanisms. For example, studies on cancer-associated fibroblasts (CAFs) have elucidated their transcriptional heterogeneity and subpopulation-specific roles 11 , 12 . In the context of SASP research, integrated sequencing enables comprehensive analysis of SASP-related gene expression and intercellular signaling, offering novel insights into SASP-driven mechanisms in TAA pathogenesis and accelerating therapeutic discovery. This study aims to employ bulk RNA sequencing and scRNA-seq technologies to investigate the diagnostic value and mechanistic roles of SASP-related genes in TAA. By systematically integrating data obtained from both approaches, we seek to identify and validate novel TAA diagnostic biomarkers, thereby providing more precise tools for early disease detection. Concurrently, this research will elucidate the molecular mechanisms by which SASP drives TAA pathogenesis, pinpoint potential therapeutic targets, and lay a robust theoretical foundation for developing innovative therapeutic strategies. From a clinical perspective, our findings are expected to improve clinical outcomes for TAA patients, offering critical translational implications for disease management. 2. Materials and methods 2.1 Data source In Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ), the datasets GSE26155 (platform: GPL5175), GSE219204 (GPL24539), and GSE143921 (GPL18573) were selected (Access time: January 7, 2025). In training set GSE26155, only 43 thoracic aortic aneurysm (TAA) tissue samples and 43 normal thoracic aortic tissue samples (control) in GSE26155 were retained, while the rest of the samples were excluded. Meanwhile, 8 smooth muscle cell samples from TAA tissues and 3 smooth muscle samples from normal thoracic artery tissues in GSE219204 were selected as validation set. The remaining samples in GSE219204 were excluded. Both of these datasets were mRNA microarray datasets. GSE143921 was a single-cell RNA sequencing (scRNA-seq) dataset, encompassing 3 TAA ascending aortic tissue samples and 3 normal ascending aortic tissue samples.Relying on the records in literature 13 , all 83 senescence-associated secretory phenotype-related genes (SASP-RGs) were listed in Table S1 . 2.2 Weighted gene co-expression network analysis (WGCNA) The ssGSEA scores of SASP-RGs in training set samples were computed via the ssGSEA algorithm in "GSVA" package (v 1.42.0) 14 . The magnitude of the scores could reflect the degree of interrelation within the samples and SASP-RGs. In TAA and control, Wilcoxon test was employed to determine whether there was a marked difference in this score between groups (p < 0.05). If a difference was detected, SASP-RGs were considered to be associated with TAA. Subsequently, WGCNA was employed to identify the module genes in training set that were most correlated with ssGSEA scores of SASP-RGs. In "WGCNA" package (v 1.71) 15 , hierarchical clustering was first executed on all samples in training set to detect and remove outliers. Subsequently, to ensure that the constructed network was better conformed to a scale-free distribution, the scale-free network evaluation coefficient R 2 was set to 0.80. The soft threshold with a value exceeding 0.80 and connectivity close to 0 was selected as the optimal soft threshold. Then, with minModuleSize set to 100 and mergeCutHeight set to 0.25, co-expression modules were gained by merging modules with high similarity. The Pearson correlation between these co-expression modules and ssGSEA scores of SASP-RGs was examined (|correlation (cor)| >0.3, p 0.8, |Gene Significance (GS)| >0.7) to acquire WGCNA genes. 2.3 Screening of candidate genes Differentially expressed genes (DEGs) between TAA and control in training set were examined through "limma" package (v 3.54.1) 16 (|log 2 fold change (FC)| >0.5, adj.p < 0.05). A volcano plot was generated with the use of "ggplot2" package (v 3.3.6) 17 to display expression of DEGs. DEGs were sorted on the basis of log 2 FC, and the names of the top 10 DEGs with the highest and lowest log 2 FC values were labeled. Subsequently, a heatmap was diagrammed via "ComplexHeatmap" package (v 2.14.0) 18 to visually present the expression amounts of these 20 labeled DEGs. Subsequently, DEGs were intersected with SASP-RGs, and the overlapping genes were taken as candidate genes. These candidate genes were presented via "ggvenn" package (v 0.1.9) 19 . 2.4 Functional and interaction analysis of candidate genes Functional analysis, including Gene Ontology (GO) and Kyoto EncyclDMedia of Genes and Genomes (KEGG) analyses, was implemented by "clusterProfiler" package (v 4.6.2) (p < 0.05) 20 . Among them, GO annotated functions of genes, covering biological processes (BPs) at a macroscopic level, molecular functions (MFs) at a molecular level, and cellular components (CCs) where the genes were located. KEGG studied the pathways in which genes were involved and revealed the relevant biological mechanisms of genes. Relying on results gained from the analysis, the number of genes replete with each functional part or pathway was sorted in descending order. The top 5 entries of each part in GO and top 10 pathways in KEGG were presented. Subsequently, in STRING database ( https://string-db.org/ ), with a confidence level set to be ≥ 0.4, the protein-level interactions among candidate genes were examined. The candidate genes with interactions were incorporated into Cytoscape software (v 3.9.1) for the construction of a protein-protein interaction (PPI) network 21 . Meanwhile, within CytoHubba plugin of Cytoscape software (v 3.9.1), ‘Degree of Maximum Neighborhood Component (DMNC)’, ‘Stress’, and ‘ClusteringCoefficient’ algorithms were selected to score interactions of candidate genes in PPI network. The candidate genes ranked among the top 100 scores in each algorithm were intersected to acquire candidate key genes, which were then presented via "ggvenn" package (v 0.1.9). 2.5 Confirmation of key genes Machine learning was conducted in training set. First, least absolute shrinkage and selection operator (LASSO) regression analysis was executed on candidate key genes in "glmnet" package (v 4.1-4) to construct a LASSO model. The model introduced a penalty term (regularization coefficient, lambda) to achieve variable selection. Genes that had a marked impact on disease were screened out, and unimportant genes were excluded. When the lambda value reached the minimum, LASSO model achieved optimality. We denoted candidate key genes in optimal LASSO model as LASSO genes. Subsequently, in both training set and validation set, the expression of LASSO genes was verified through Wilcoxon test. LASSO genes that presented differential expression and had consistent expression trends in two datasets were denoted as key genes (p < 0.05). Meanwhile, box plots were diagrammed in "ggplot2" package (v 3.3.6) to visually present this difference. 2.6 Diagnostic value of key genes To explore the diagnostic value of key genes for TAA, a nomogram was developed. Specifically, in training set, key genes were incorporated into "rms" package (v6.5.0) to create the nomogram 22 . The scores of each key gene were aggregated to form a total score, which corresponded to the probability of a sample in training set having TAA. Relying on nomogram, a calibration curve was diagrammed again in "rms" package (v6.5.0), and bias correction was implemented with 1,000 repetitions via "Bootstrapping". In addition, the goodness-of-fit of the model was evaluated by Hosmer-Lemeshow (HL) test (p > 0.05). Meanwhile, decision curve analysis (DCA) was conducted in "rmda" package (v 1.6) 23 , and a DCA curve was generated to determine clinical benefit of the nomogram. 2.7 Immune cell infiltration analysis In training set, 28 types of immune cells 24 in ssGSEA algorithm of "GSVA" package (v 1.42.0) 14 were selected for analysis. The enrichment levels of these immune cells in TAA and control were observed. Meanwhile, Wilcoxon test was employed to analyze whether there were differences in the enrichment levels of immune cells between groups (p < 0.05), and a box plot was created in "ggplot2" package (v 3.3.6) for presentation. Next, in "psych" package (v 2.3.12) 25 , Spearman correlations were examined among differentially infiltrated immune cells (DIICs), as well as between DIICs and key genes (|cor| >0.3, p < 0.05). Meanwhile, a interrelation heatmap was diagrammed in "corrplot" package (v 0.92) 26 . 2.8 Gene set enrichment analysis (GSEA) Relying on default gene sets in MSigDB ( https://www.gsea-msigdb.org/gsea/msigdb ), GSEA was executed on key genes in training set. First, Spearman correlations between key genes and remaining genes were examined in "psych" package (v 2.3.12), and all genes were sorted in descending order on the basis of interrelation coefficients. Utilizing the sorting results, GSEA was implemented in "clusterProfiler" package (v 4.6.2) (|normalized enrichment score (NES)|>1, p < 0.05). For the top 5 signaling pathways with the smallest p-values, plots were generated in "enrichplot" package (v 1.18.0) 26 . 2.9 Network construction GeneMANIA database ( https://genemania.org/ ) could explore genes functionally connected with key genes. Thus, a gene-gene interaction network was generated via this database, and the top 7 shared functions of these genes were presented. In addition, the upstream microRNAs (miRNAs) and transcription factors (TFs) of key genes were predicted, and a TF-miRNA-mRNA network was created respectively in Cytoscape software (v 3.9.1). Specifically, miRNAs targeting key genes were identified in microcosm database ( http://www.ebi.ac.uk/enright-srv/microcosm/ ), and the TFs targeting key genes were identified in NetworkAnalyst database ( https://www.networkanalyst.ca/NetworkAnalyst/ ). 2.10 Processing and annotation of scRNA-seq data In "Seurat" package (v 5.0.1) 27 , scRNA-seq dataset was pre-processed and annotated. Specifically, first, quality control was executed on the data. Standards of 200 < nFeature_RNA < 4,000 and nCount_RNA < 6,0000 were set to acquire qualified cells and genes. Subsequently, vst method was adopted to extract the top 2,000 genes with the greatest variance among the qualified genes, and the names of the top 10 genes with the greatest variance were presented. Next, principal component analysis (PCA) was carried out on samples in the data. Thereafter, JackStrawPlot function was employed to compare distribution of p-values of each principal component (PC) and sort the percentage of each PC. Meanwhile, a PCA variance drop-off plot was diagrammed. In the variance drop-off plot, PCs at the inflection point were retained for subsequent clustering analysis. In the clustering analysis, FindNeighbors and FindClusters functions were employed to cluster cells into different clusters (resolution = 0.4). Simultaneously, Uniform Manifold Approximation and Projection (UMAP) clustering method was incorporated for dimensionality reduction and presentation. Subsequently, by integrating FindAllMarkers function (logFC = 0.5, Minpct = 0.2, only.pos = TRUE), the literature 28 , and the marker genes in CellMarker database ( http://bio-bigdata.hrbmu.edu.cn/cellmarker/ ), different clusters were annotated as different cell types. 2.11 Confirmation of key cells Initially, the proportion of key gene expression in different cell types was observed. Subsequently, the difference in expression abundances of key cells between TAA and the control was examined via Wilcoxon test (p < 0.05). There were differential expressions of key genes, and the cells with high expression of key genes were recorded as key cells. 2.12 Pseudo-time analysis and cell-cell communication Here, key cells were re-clustered. High-variable genes were filtered on the basis of the criteria of "average expression amount ≥ 0.1 and empirical_dispersion ≥ 1 * fit_dispersion", and dimensionality reduction was executed by virtue of "DDRTree" package (v 0.1.5) ( https://cran.r-project.org/package = DDRTree). After key cells were clustered into different clusters, the developmental trajectories of key cells were examined in "Monocle" package (v 2.26.0) 29 . Subsequently, Branched Expression Analysis Modeling (BEAM) method in "Monocle" package (v 2.26.0) was employed to analyze key nodes, and expression heatmap of key genes in different pseudo-time sequences of key cells was plotted by virtue of plot_pseudo-time_heatmap function. For all the annotated cells, the inter-cellular communication networks in TAA and the control were examined in "CellChat" package (v 1.6.1) 30 . Meanwhile, bubble plots were generated to observe receptor-ligand interactions between different cell types. 2.13 Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) From June 1, 2024 to December 30, 2025, five thoracic aortic aneurysm (TAA) samples and five control samples were collected to validate the expression of key genes. The TAA group consisted of surgically resected thoracic aortic aneurysm tissue specimens (from patients who underwent thoracic aortic aneurysm resection), while the control group included normal aortic wall tissues obtained by aortic wall punch during coronary artery bypass grafting (CABG). All specimens were secondary utilization of routinely resected/discarded tissues during surgery, eliminating the need for additional sampling for the study. This research has been approved by the Ethics Committee of the Second Affiliated Hospital of Hebei Medical University, The study has obtained informed consent from all participants and/or their legal guardians. Research involving human research participants was conducted in accordance with the Helsinki Declaration. Inclusion criteria for TAA patients:1. Aged ≥ 45 years; 2. Diagnosed with thoracic aortic aneurysm via whole-aorta computed tomography angiography (CTA) and underwent thoracic aortic aneurysm resection; 3. Complete medical records and comprehensive auxiliary examinations.Exclusion criteria for TAA patients: 1. Receiving other surgeries such as aortic valve replacement simultaneously with aortic valve replacement; 2. Ehlers-Danlos syndrome; 3. Marfan syndrome; 4. History of endovascular aortic repair for aortic dissection or abdominal aortic aneurysm; 5. Other known hereditary vascular or connective tissue diseases;6. Cancer; 7. Infection; 8. Inflammatory aortic aneurysm; 9. Comorbid immune system diseases or receiving immunosuppressants or modulators (e.g., psoriasis, systemic lupus erythematosus, Behçet's disease, atopic dermatitis, ulcerative colitis, etc.).. Total RNA was extracted from tissue samples after lysis with 1 ml of TRIzol reagent. After the total RNA was air - dried naturally, it was redissolved and its concentration was measured again. Subsequently, 2 µg of total RNA was taken for reverse transcription into cDNA. The reverse transcription kit used was Hifair® Ⅲ 1st Strand cDNA Synthesis SuperMix for qPCR (Yeasen, China), and the reverse transcription was carried out in an S1000™ Thermal Cycler ordinary PCR instrument (BIO - RAD, USA) ( Table S2 ). Then, the cDNA was diluted 5–20 times and amplified using Servicebio® 2×Universal Blue SYBR Green qPCR Master Mix (Servicebio, China). The amplification instrument was a CFX Connect real - time quantitative fluorescence PCR instrument (BIO - RAD, USA) (40 cycles, Table S3 ). The primer sequences were shown in Table S4 . GAPDH was used as the internal reference gene for the relative quantification of key genes. The relative expression abundances of key genes were computed by 2 −△△CT method. The graphs were plotted by virtue of Graphpad Prism 10, and the t-test was employed to compare the expression differences (p < 0.05). 2.14 Statistical analysis All statistical analyses in this study were conducted in R language (v 4.2.3). Wilcoxon test was applied to analyze differences between two groups. The inter-group test for RT-qPCR was executed by virtue of t-test. A p-value under 0.05 was considered as criterion for statistical significance. 3. Results 3.1 Identification of WGCNA modules associated with SASP-RGs in TAA The ssGSEA scores of SASP-RGs were higher in TAA (p < 0.001), enabling subsequent WGCNA analysis (Fig. 1 A). Additionally, no outlier samples were present in training set (Fig. 1 B). When the soft-threshold was set to 6, the connectivity was close to 0 (Fig. 1 C). Subsequently, genes were clustered into 10 modules (excluding un-clusterable gray module) (Fig. 1 D). Among them, the yellow module had the highest interrelation with ssGSEA scores of SASP-RGs, reaching 0.88 (p < 0.001) (Fig. 1 E). Furthermore, after restricting |MM| and |GS| of the yellow module, all 330 module genes connected with SASP-RGs were gained (Fig. 1 F, Table S5 ), denoted as WGCNA genes. 3.2 Identification and functional annotation of candidate genes in TAA There were 593 DEGs between TAA and the control. Among them, 470 DEGs presented an upward expression trend in TAA, while 123 DEGs presented a downward trend (Fig. 2 A-B, Table S6 ). These 593 DEGs had 229 overlapping candidate genes with 330 WGCNA genes (Fig. 2 C). In terms of GO-BP, candidate genes were mainly involved in 950 processes such as "leukocyte mediated immunity", "leukocyte cell-cell adhesion", and "positive regulation of cell activation". Regarding GO-MF, candidate genes were primarily connected with 90 functions like "immune receptor activity", "GTPase regulator activity", and "nucleoside-triphosphatase regulator activity". In terms of GO-CC, candidate genes were mostly located in 85 components including "external side of plasma membrane" and "secretory granule membrane" (Fig. 2 D, Table S7 ). In KEGG, candidate genes were associated with 65 signaling pathways, such as "tuberculosis", "staphylococcus aureus infection", and "cell adhesion molecules" (Fig. 2 E, Table S8 ). Among 229 candidate genes, 205 had interactions. The three genes with the highest Degree were PTPRC, CD4, and ITGB2 (Fig. 2 F). Relying on algorithms of DMNC, Stress, and Clustering Coefficient, the genes among these 205 candidate genes that ranked in the top 100 in terms of scores were shown as in figures (Fig. 2 G-I). After taking intersection, 36 overlapping candidate key genes were gained (Fig. 2 J). 3.3 Identification of key genes via LASSO regression and expression verification When lambda.min was set at 0.02239849, LASSO model reached its optimal state (Fig. 3 A-B). LASSO genes included in model were F13A1, FERMT3, AIF1, CD300A, TLR7, CD28, CD69, CR1, CCR1, and IL7R. Among them, CR1 and F13A1 had high expression abundances in TAA samples of both training set and validation set (p < 0.05) (Fig. 3 C-D). 3.4 Diagnostic value of the nomogram and immune cell infiltration characteristics in TAA The nomogram constructed via key genes presented that each key gene corresponded to a score, and the combined total score was directly proportional to the disease risk of TAA (Fig. 4 A). Subsequently, on the basis of the evaluation of calibration curve, the prediction curve of the nomogram had a high degree of overlap with the actual curve. HL test indicated that there was no marked deviation between predicted values and true values (p = 0.215) (Fig. 4 B). In addition, the clinical benefit of the nomogram was exceed that of that of extreme values "none" and "all", demonstrating a good benefit (Fig. 4 C). This nomogram - based diagnostic model could serve as a potentially developable tool for TAA diagnosis. Overall, infiltration level of neutrophils was low in all samples, while that of central memory CD4 T cells was high in all samples (Fig. 4 D). With exception of CD56dim natural killer cells, effector memory CD4 T cells, memory B cells, neutrophils, monocytes, and type 17 T helper cells, the expression of the remaining immune cells was higher in TAA (p < 0.05), such as macrophages, eosinophils, mast cells, and regulatory T cells (Fig. 4 E). There were positive interrelations among all DIICs. Specifically, the interrelation between regulatory T cells and myeloid-derived suppressor cells (MDSC) was as high as 0.96 (p 0.40, p < 0.001). The interrelation between F13A1 and T follicular helper cells reached 0.88, and the interrelation between CR1 and gamma delta T cells was as high as 0.90 (Fig. 4 F). This suggested that these DIICs might play important roles in the progression of TAA. 3.5 Pathway enrichment and regulatory network analysis of key genes In GSEA, CR1 was replete with 64 pathways, with the most marked ones being "cytokine-cytokine receptor interaction", "natural killer cell mediated cytotoxicity", "hematopoietic cell lineage", "chemokine signaling pathway", and "systemic lupus erythematosus" (Fig. 5 A, Table S9 ). F13A1 was markedly replete with 64 pathways such as "cytokine-cytokine receptor interaction", "natural killer cell mediated cytotoxicity", "hematopoietic cell lineage", "chemokine signaling pathway", and "systemic lupus erythematosus" (Fig. 5 B, Table S10 ). Evidently, key genes were all markedly associated with "cytokine-cytokine receptor interaction" and "chemokine signaling pathway". This indicated that these key genes might have participated in multiple biological processes connected with immune response and cell-cell communication, and were likely to have been closely involved in the pathophysiological mechanisms of TAA. In GGI network, the interaction relationships between key genes and functionally similar genes (such as F13B, FGB, CD46) were mainly physical interactions. They jointly participated in functions such as "complement activation" and "protein activation cascade" (Fig. 5 C). CR1 predicted 26 miRNAs, such as hsa-miR-6867-3p and hsa-miR-578; F13A1 predicted 18 miRNAs, such as hsa-miR-3682-3p and hsa-miR-7853-5p (Fig. 5 D). In addition, CR1 and F13A1 predicted 23 and 18 TFs respectively, and they jointly targeted SCL, SOX2, P63, MYC, and PBX1 (Fig. 5 D). This interrelation and regulatory relationship reflected the complex mechanism of action of key genes. 3.6 scRNA-seq data processing and cell type annotation Relying on screening criteria, 5,236 cells and 31,845 genes remained in the dataset ( Figure S1 A-B ). Among them, the top 2,000 highly variable genes were shown in the figure, such as HBA2 and HBB (Fig. 6 A). In addition, cluster analysis indicated a high degree of differentiation between TAA and control samples ( Figure S1 C ). In the elbow plot of variance, the first 15 PCs contributed the most to the variation and were at the inflection point. Therefore, dims = 15 was selected for cluster analysis (Fig. 6 B). After clustering, the cells were allocated into 12 clusters (Fig. 6 C). These 12 clusters were annotated as smooth muscle cells, erythrocyte cells, monocytes, fibroblasts, endothelial cells, macrophages, CD8 T cells, and plasma cells ( Table S1 , Fig. 6 D). 3.7 Identification of macrophages as key cells and dynamic expression of F13A1 during macrophage development Relying on expression abundances and expression differences of key genes, macrophages were marked as key cells (Fig. 7 A-C). Key cells were clustered into 2 clusters and had 5 differentiation states. During the development states of macrophages, state1 was in initial stage of development, and state4 was in final stage of development (Fig. 7 D-F). Among them, the expression amount of F13A1 gradually rose from the initial to the middle stage of development, then declined, and there was no expression in later stage (Fig. 7 G). This implied that F13A1 was connected with macrophage development. 3.8 Cell-cell communication changes involving macrophages in TAA Compared with the control, the quantity and intensity of communication between macrophages and fibroblasts as well as endothelial cells in TAA declined, while the communication effect with monocytes rose (Fig. 8 A-D). In addition, in the control, the connection between fibroblasts and macrophages was mainly through C3−(ITGAX + ITGB2), and the main ligand - receptor pairs between macrophages and endothelial cells were CXCL8 − ACKR1 (Fig. 8 E). In TAA, the communication between fibroblasts and macrophages was weakened, while the communication between macrophages and monocytes was enhanced, and its ligand-receptor pair was ANXA1 − FPR1 (Fig. 8 F). These findings suggested that the communication patterns among different cell types changed in TAA, which might have played a crucial role in the pathological process of TAA, potentially providing new insights into the underlying mechanisms and possible therapeutic targets for this disease. 3.9 Validation of key gene expression in clinical TAA samples via RT-qPCR Compared with the control, the key genes CR1 and F13A1 were both significantly highly expressed in the TAA samples (p < 0.01)(Fig. 9 A-B). This finding was consistent with the expression trends observed in the training and validation sets. This concordance not only strengthens the reliability of our data analysis but also suggests that CR1 and F13A1 may play crucial and consistent roles in the biological processes underlying TAA. Their elevated expression in TAA samples across different datasets implies that these genes could potentially serve as stable biomarkers for the diagnosis, prognosis assessment, or even as potential therapeutic targets for TAA. Conclusion Transcriptomic and single-cell sequencing data for TAA were retrieved from GEO. Eighty-three senescence-associated secretory phenotype-related genes (SASP-RGs) were curated from literature and expanded via WGCNA. CR1and F13A1 were identified via intersecting differential expression and WGCNA results, refined through LASSO regression, and validated for TAA diagnostic utility in a nomogram model. Immune infiltration analysis revealed significant immune cell-TAA correlations with these genes, while GSEA highlighted enriched pathways (e.g., cytokine-cytokine receptor interactions). Regulatory miRNAs and shared transcription factors (SCL, SOX2) were predicted for both genes. Single-cell analysis implicated macrophages in TAA progression, with F13A1 showing differentiation-dependent expression. Clinical specimens confirmed dysregulation of both genes. Discussion TAA is an insidious disease with nonspecific early symptoms, such as chest tightness, chest pain, shortness of breath, and cough, which often lead to misdiagnosis 31 . The pathogenesis of TAA involves a complex multifactorial process, demonstrating strong associations with genetic predisposition and age-related degenerative changes. In this study, we integrated TAA transcriptomic data with SASP-related genes and identified key candidates such as CR1 and F13A1(Fig. 2 A、B). A predictive nomogram was constructed, followed by comprehensive analyses of immune infiltration, pathway enrichment, and regulatory mechanisms (Fig. 4 ). Furthermore, single-cell resolution analysis revealed macrophages as pivotal cellular players in TAA progression. These findings provide a theoretical foundation for early diagnosis and mechanistic exploration of TAA 32 , 33 . Cellular senescence contributes to TAA pathogenesis through mitochondrial dysfunction, elevated oxidative stress, and increased SASP 8 , 34 , 35 . In this study, we integrated transcriptomic data from TAA with SAPA-associated genes to identify key diagnostic genes for TAA within the SAPA framework using bioinformatics approaches. Further investigations revealed the mechanisms underlying immune infiltration, pathway enrichment, and regulatory networks mediated by these key genes in TAA. Additionally, single-cell data analysis identified critical cellular players driving TAA progression, providing a theoretical foundation for elucidating the role of SAPA in TAA pathogenesis. Through rigorous screening, CR1 and F13A1 were identified as pivotal genes in TAA. Notably, these critical genes exhibited significant upregulation in TAA specimens, a finding subsequently validated by PCR experiments (Fig. 9 ).CR1as a regulatory component of the complement cascade, has been implicated in the pathogenesis, progression, and prognosis of various diseases through its genetic polymorphisms. Three distinct CR1 polymorphic patterns demonstrate disease-specific effects: 1) Length and density polymorphisms predominantly influence Alzheimer's disease (AD) pathogenesis 36 ; 2) Knops blood group antigen polymorphisms and erythrocytic CR1 density variations modulate malaria susceptibility 37 ; 3) Erythrocytic CR1 density polymorphisms exhibit geographic and ethnic variations in their associations with neoplastic disorders 38 , cardiovascular diseases 39 , and leprosy. CR1 demonstrates a significant association with inflammatory cytokines/chemokines, which play pivotal roles in orchestrating immune responses through their regulation of immune cell migration and activation. CR1 dysfunction may induce dysregulated expression of these inflammatory mediators, consequently impairing normal immune cell functionality. This pathophysiological alteration manifests as diminished pathogen clearance capacity and persistent inflammatory activation. Notably, such chronic inflammatory states exhibit a close correlation with the SASP, suggesting a potential mechanistic link between CR1-mediated immune regulation and aging-related inflammatory processes 40 , 41 . F13A1 encodes the A subunit of coagulation factor XIII, the final zymogen activated in the coagulation cascade. It plays a critical role in multiple physiological processes including blood coagulation, fibrinolysis, and platelet activation. Emerging evidence further associates F13A1 with inflammatory responses 42 – 44 . Given the potential pivotal role of CR1 in TAA, this study employed GSEA to reveal that CR1 and F13A1 are primarily associated with the chemokine signaling pathway (Fig. 5 ). Activation of this pathway induces cytoskeletal rearrangement, directing immune cell migration toward inflammatory sites, injured tissues, or specific organs, thereby playing crucial roles in immune defense, inflammatory responses, and tissue repair processes 45 , 46 . During aging, altered expression of chemokines and their receptors impairs immune cell trafficking and tissue regeneration, leading to diminished host responsiveness to infections and injuries 47 , 48 . Notably, chemokines facilitate inflammatory cell recruitment to vascular walls, contributing to inflammatory reactions in TAA pathogenesis. This process promotes vascular wall damage and remodeling, ultimately shaping the inflammatory microenvironment critical for TAA progression 49 , 50 . Through the analysis of critical genes CR1 and F13A1, this study successfully constructed a nomogram, a graphical computational tool that integrates multiple variables into a comprehensive predictive index for intuitive disease risk assessment. Notably, this represents the first diagnostic model for TAA established using SASP-related genes. The positive correlation between key gene expression scores and TAA disease risk provides clinicians with a user-friendly tool for visual risk stratification. Validation through calibration curves and Hosmer-Lemeshow tests confirmed the nomogram's satisfactory predictive accuracy and reliability in TAA risk evaluation. Compared with conventional diagnostic approaches, this model demonstrates superior performance in risk quantification and synergistic integration of polygenic information(Fig. 4 C), offering enhanced clinical applicability for comprehensive risk assessment 51 – 54 . Emerging evidence indicates that immune cell infiltration plays a pivotal role in the pathogenesis and progression of TAA, with macrophages and T lymphocytes serving as key contributors in this process. As critical mediators of signal transduction and intercellular communication, macrophages demonstrate significant regulatory functions in both physiological homeostasis and pathological remodeling of the aortic wall. Notably, specific subsets of natural killer T (NKT) cells and CD8⁺ T lymphocytes have been identified to exert potential protective effects against TAA development(Fig. 4 D-F). These mechanistic insights substantially advance our understanding of TAA pathogenesis and provide a scientific foundation for the development of targeted immunomodulatory therapeutic strategies 55 , 56 . The immune infiltration analysis revealed significant infiltration of multiple immune cell populations in TAA, including macrophages, eosinophils, mast cells, and regulatory T cells (Tregs), demonstrating a complex immune microenvironment. During the pathological progression of thoracic aortic aneurysm (TAA), massive macrophage accumulation occurs with pronounced infiltration from the adventitia into the media, a process regulated by multiple factors including the CCL2/CCR2 axis. These macrophages robustly activate inflammatory responses through secretion of various pro-inflammatory cytokines such as tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), and interleukin-6 (IL-6). These cytokines not only promote self-amplification through macrophage activation, proliferation, and sustained infiltration, but also recruit additional immune cells including neutrophils, collectively establishing a potent inflammatory microenvironment. Within this milieu, escalating inflammation induces vascular smooth muscle cell (VSMC) dysfunction and phenotypic switching, accompanied by severe extracellular matrix (ECM) degradation characterized by elastic fiber fragmentation and collagen breakdown. The persistent inflammatory cascade and progressive ECM deterioration synergistically compromise aortic wall structural integrity and biomechanical properties, ultimately driving TAA formation and progression 57 – 60 . Our single-cell analysis revealed macrophages as the predominant expressors of the F13A1 gene, identifying them as key cellular players in TAA pathogenesis. Subsequent clustering analysis further delineated two distinct F13A1-expressing macrophage subtypes exhibiting functional heterogeneity. This finding underscores the critical involvement of macrophage heterogeneity in TAA progression, reinforcing the pivotal role of macrophage subpopulations in disease dynamics. Transcription factors (TFs) are a class of protein molecules that regulate gene transcription, the transcription factors SCL, SOX2, p63, MYC, and PBX1 play pivotal roles in modulating the expression of CR1 and F13A1(Fig. 5 D). These transcription factors may bind to cis-acting elements within the promoter regions of CR1 and F13A1 genes, he progression of thoracic aortic aneurysm may involve modulation of mRNA transcription in CR1 and F13A1, thereby influencing critical biological processes in vascular smooth muscle cells (VSMCs), including proliferation, apoptosis, migration, as well as the synthesis and degradation of extracellular matrix components. For instance, MYC, a pivotal proto-oncogene 61 , 62 , exhibits dysregulated expression that potentially drives abnormal cellular proliferation and disrupts normal physiological functions of thoracic aortic wall cells 63 , 64 , ultimately contributing to aneurysm pathogenesis 65 .Systematic monitoring of these transcription factors and their associated mRNA levels could provide novel biomarkers for early diagnosis and dynamic assessment of thoracic aortic aneurysm progression. This molecular surveillance approach may establish a foundation for developing targeted therapeutic strategies to intervene in aneurysm development. In conclusion, this study investigated the mechanistic role of SASP in TAA pathogenesis through transcriptomic profiling and single-cell resolution analyses. We identified CR1 and F13A1 as pivotal genes and subsequently developed a nomogram model demonstrating potential diagnostic value. Furthermore, single-cell characterization revealed macrophages as critical cellular components interacting with these key genes during TAA progression. However, several limitations warrant consideration: the experimental validation cohort exhibited restricted sample size, and comprehensive exploration of molecular mechanisms underlying these genetic determinants remains imperative. Additionally, while the nomogram demonstrates theoretical diagnostic potential, future iterations should incorporate additional multidimensional features to enhance predictive accuracy for TAA risk assessment. Further mechanistic studies are required to elucidate the precise functional contributions of these identified genes in TAA pathophysiology. Declarations Ethics approval and consent to participate The study was approved by the Ethics Committee of The Second Hospital of Hebei Medical University (2025-R557). The study has obtained informed consent from all participants and/or their legal guardians. Research involving human research participants was conducted in accordance with the Helsinki Declaration. And written informed consent was obtained from all participants. Consent for publication All the authors approved the publication. Availability of data and materials The datasets are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was supported by Hebei Provincial Department of Finance Government Funding or Public hospital reform and high-quality development demonstration project (No.36); Hebei Provincial Department of Finance Government Funding or Specialty Capacity Building and Specialty Leader Train (No. 303-2022-27-07). Authors' contributions ZTY and WBJ wrote the manuscript; FC and LY completed the experimental data collection; YH, HKC and LY were responsible for experimental data collation and statistical analysis. ZTY and WBJ guided the revision of the manuscript; LY and MQL provided manuscript. Acknowledgments Not applicable. References Zhou Z, Cecchi AC, Prakash SK, Milewicz DM. Risk Factors for Thoracic Aortic Dissection. Genes (Basel). 2022;13. 10.3390/genes13101814 . Rodrigues Bento J, et al. The Genetics and Typical Traits of Thoracic Aortic Aneurysm and Dissection. Annu Rev Genomics Hum Genet. 2022;23:223–53. 10.1146/annurev-genom-111521-104455 . Gouveia EMR, et al. Incidence and Prevalence of Thoracic Aortic Aneurysms: A Systematic Review and Meta-analysis of Population-Based Studies. Semin Thorac Cardiovasc Surg. 2022;34:1–16. 10.1053/j.semtcvs.2021.02.029 . Watkins AC, Dalal A, Lee JT, Dake MD. Current Status of Endoluminal Treatment of Descending Thoracic Aortic Aneurysms. Cardiovasc Intervent Radiol. 2020;43:1770–8. 10.1007/s00270-020-02526-1 . Wang Y, et al. Animal Models, Pathogenesis, and Potential Treatment of Thoracic Aortic Aneurysm. Int J Mol Sci. 2024;25. 10.3390/ijms25020901 . Maus M, et al. Iron accumulation drives fibrosis, senescence and the senescence-associated secretory phenotype. Nat Metab. 2023;5:2111–30. 10.1038/s42255-023-00928-2 . Kumari R, Jat P. Mechanisms of Cellular Senescence: Cell Cycle Arrest and Senescence Associated Secretory Phenotype. Front Cell Dev Biol. 2021;9:645593. 10.3389/fcell.2021.645593 . Aschacher T, et al. Impacts of Telomeric Length, Chronic Hypoxia, Senescence, and Senescence-Associated Secretory Phenotype on the Development of Thoracic Aortic Aneurysm. Int J Mol Sci. 2022;23. 10.3390/ijms232415498 . Fang Z, Li J, Cao F, Li F. Integration of scRNA-Seq and Bulk RNA-Seq Reveals Molecular Characterization of the Immune Microenvironment in Acute Pancreatitis. Biomolecules. 2022;13. 10.3390/biom13010078 . Tan Z, et al. Comprehensive analysis of scRNA-Seq and bulk RNA-Seq reveals dynamic changes in the tumor immune microenvironment of bladder cancer and establishes a prognostic model. J Transl Med. 2023;21:223. 10.1186/s12967-023-04056-z . Liang L, et al. Integration of scRNA-Seq and Bulk RNA-Seq to Analyse the Heterogeneity of Ovarian Cancer Immune Cells and Establish a Molecular Risk Model. Front Oncol. 2021;11:711020. 10.3389/fonc.2021.711020 . Chi H, et al. T-cell exhaustion signatures characterize the immune landscape and predict HCC prognosis via integrating single-cell RNA-seq and bulk RNA-sequencing. Front Immunol. 2023;14:1137025. 10.3389/fimmu.2023.1137025 . Saul D, et al. A new gene set identifies senescent cells and predicts senescence-associated pathways across tissues. Nat Commun. 2022;13:4827. 10.1038/s41467-022-32552-1 . Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013;14. 10.1186/1471-2105-14-7 . Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559. 10.1186/1471-2105-9-559 . Ritchie ME, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47. 10.1093/nar/gkv007 . Gustavsson EK, Zhang D, Reynolds RH, Garcia-Ruiz S, Ryten M. ggtranscript: an R package for the visualization and interpretation of transcript isoforms using ggplot2. Bioinformatics 38, 3844–3846. 10.1093/bioinformatics/btac409 (2022). Gu Z, Hübschmann D. Make Interactive Complex Heatmaps in R. Bioinformatics. 2022;38:1460–2. 10.1093/bioinformatics/btab806 . Zheng Y, et al. Ferroptosis and Autophagy-Related Genes in the Pathogenesis of Ischemic Cardiomyopathy. Front Cardiovasc Med. 2022;9:906753. 10.3389/fcvm.2022.906753 . Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics. 2012;16:284–7. 10.1089/omi.2011.0118 . Liu P, Xu H, Shi Y, Deng L, Chen X. Potential Molecular Mechanisms of Plantain in the Treatment of Gout and Hyperuricemia Based on Network Pharmacology. Evid Based Complement Alternat Med 2020, 3023127. 10.1155/2020/3023127 (2020). Sachs MC, plotROC. A Tool for Plotting ROC Curves. J Stat Softw. 2017;79. 10.18637/jss.v079.c02 . Liu C, He Y, Luo J. Application of Chest CT Imaging Feature Model in Distinguishing Squamous Cell Carcinoma and Adenocarcinoma of the Lung. Cancer Manag Res. 2024;16:547–57. 10.2147/cmar.S462951 . Zhou J, et al. Identification of aging-related biomarkers and immune infiltration characteristics in osteoarthritis based on bioinformatics analysis and machine learning. Front Immunol. 2023;14:1168780. 10.3389/fimmu.2023.1168780 . Correction to Lancet Psych. 2022; 9: 938. Lancet Psychiatry 10, e10. 10.1016/s2215-0366(22)00411-4 (2023). Wang L, et al. Cuproptosis related genes associated with Jab1 shapes tumor microenvironment and pharmacological profile in nasopharyngeal carcinoma. Front Immunol. 2022;13:989286. 10.3389/fimmu.2022.989286 . Satija R, Farrell JA, Gennert D, Schier AF, Regev A. Spatial reconstruction of single-cell gene expression data. Nat Biotechnol. 2015;33:495–502. 10.1038/nbt.3192 . Li Y, et al. Single-Cell Transcriptome Analysis Reveals Dynamic Cell Populations and Differential Gene Expression Patterns in Control and Aneurysmal Human Aortic Tissue. Circulation. 2020;142:1374–88. 10.1161/circulationaha.120.046528 . Trapnell C, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014;32:381–6. 10.1038/nbt.2859 . Azadian S, et al. Reconstructing the cell-cell interaction network among mouse immune cells. Biotechnol Bioeng. 2023;120:2756–64. 10.1002/bit.28431 . Arnaoutakis GJ, et al. Misdiagnosis of Thoracic Aortic Emergencies Occurs Frequently Among Transfers to Aortic Referral Centers: An Analysis of Over 3700 Patients. J Am Heart Assoc. 2022;11:e025026. 10.1161/jaha.121.025026 . Elefteriades JA, Ziganshin BA. Familial Legacy of Thoracic Aortic Aneurysm. Circulation. 2020;142:929–31. 10.1161/circulationaha.120.049124 . Tyrrell DJ, et al. Aging Alters the Aortic Proteome in Health and Thoracic Aortic Aneurysm. Arterioscler Thromb Vasc Biol. 2022;42:1060–76. 10.1161/atvbaha.122.317643 . Watson AMD, Chen YC, Peter K. Vascular Aging and Vascular Disease Have Much in Common! Arterioscler Thromb Vasc Biol. 2022;42:1077–80. 10.1161/atvbaha.122.317892 . Wan H, et al. Bioinformatics analysis of aging-related genes in thoracic aortic aneurysm and dissection. Front Cardiovasc Med. 2023;10:1089312. 10.3389/fcvm.2023.1089312 . Veteleanu A, et al. Alzheimer's disease-associated complement gene variants influence plasma complement protein levels. J Neuroinflammation. 2023;20. 10.1186/s12974-023-02850-6 . Lorenzini PA, et al. Population-specific positive selection on low CR1 expression in malaria-endemic regions. PLoS ONE. 2023;18:e0280282. 10.1371/journal.pone.0280282 . Jeong JM, et al. CX 3 CR1 + macrophages interact with HSCs to promote HCC through CD8 + T-cell suppression. Hepatology. 2024. 10.1097/hep.0000000000001021 . Jeyalan V, Austin D, Loh SX, Wangsaputra VK, Spyridopoulos I. Fractalkine/CX(3)CR1 in Dilated Cardiomyopathy: A Potential Future Target for Immunomodulatory Therapy? Cells 12. 10.3390/cells12192377 (2023). Li Y, et al. Genomics of Alzheimer's disease implicates the innate and adaptive immune systems. Cell Mol Life Sci. 2021;78:7397–426. 10.1007/s00018-021-03986-5 . Wang YH, et al. Identification of highly reliable risk genes for Alzheimer's disease through joint-tissue integrative analysis. Front Aging Neurosci. 2023;15:1183119. 10.3389/fnagi.2023.1183119 . Dull K, Fazekas F, Törőcsik D, Factor. XIII-A in Diseases: Role Beyond Blood Coagulation. Int J Mol Sci. 2021;22. 10.3390/ijms22031459 . Ercan H, et al. Alterations of the Platelet Proteome in Lung Cancer: Accelerated F13A1 and ER Processing as New Actors in Hypercoagulability. Cancers (Basel). 2021;13. 10.3390/cancers13092260 . Kaartinen MT, et al. F13A1 transglutaminase expression in human adipose tissue increases in acquired excess weight and associates with inflammatory status of adipocytes. Int J Obes (Lond). 2021;45:577–87. 10.1038/s41366-020-00722-0 . Comerford I, McColl SR. Atypical chemokine receptors in the immune system. Nat Rev Immunol. 2024;24:753–69. 10.1038/s41577-024-01025-5 . Du Y, et al. Chemokines form nanoparticles with DNA and can superinduce TLR-driven immune inflammation. J Exp Med. 2022;219. 10.1084/jem.20212142 . Han X, et al. Potential Regulators of the Senescence-Associated Secretory Phenotype During Senescence and Aging. J Gerontol Biol Sci Med Sci. 2022;77:2207–18. 10.1093/gerona/glac097 . Ohtani N. Deciphering the mechanism for induction of senescence-associated secretory phenotype (SASP) and its role in ageing and cancer development. J Biochem. 2019;166:289–95. 10.1093/jb/mvz055 . Zhu J, Meganathan I, MacAruthur R, Kassiri Z. Inflammation in Abdominal Aortic Aneurysm: Cause or Comorbidity? Can J Cardiol. 2024;40:2378–91. 10.1016/j.cjca.2024.08.274 . Zhao H, Xu Y, Cui J. CXCR2 (rs3890158 and rs2230054) and CXCL4 (rs352008 and rs1801572) gene polymorphisms in patients with thoracic aortic aneurysm. Biotechnol Genet Eng Rev. 2024;40:4300–12. 10.1080/02648725.2023.2210011 . Zhao P, Zhen H, Zhao H, Huang Y, Cao B. Identification of hub genes and potential molecular mechanisms related to radiotherapy sensitivity in rectal cancer based on multiple datasets. J Transl Med. 2023;21:176. 10.1186/s12967-023-04029-2 . Liu F, Huang Y, Liu F, Wang H. Identification of immune-related genes in diagnosing atherosclerosis with rheumatoid arthritis through bioinformatics analysis and machine learning. Front Immunol. 2023;14:1126647. 10.3389/fimmu.2023.1126647 . Zhang B, Xie L, Liu J, Liu A, He M. Construction and validation of a cuproptosis-related prognostic model for glioblastoma. Front Immunol. 2023;14:1082974. 10.3389/fimmu.2023.1082974 . Wang J, et al. NET-related gene signature for predicting AML prognosis. Sci Rep. 2024;14:9115. 10.1038/s41598-024-59464-y . Wang Q, et al. Integrating Bulk Transcriptome and Single-Cell RNA Sequencing Data Reveals the Landscape of the Immune Microenvironment in Thoracic Aortic Aneurysms. Front Cardiovasc Med. 2022;9:846421. 10.3389/fcvm.2022.846421 . Gao H, et al. Deciphering the circulating immunological landscape of thoracic aortic aneurysm: Insights from a two-sample Mendelian randomization study. Heliyon. 2024;10:e31198. 10.1016/j.heliyon.2024.e31198 . Song W, Tu G, Qin L, Wei L, Chen J. Macrophage in Sporadic Thoracic Aortic Aneurysm and Dissection: Potential Therapeutic and Preventing Target. Rev Cardiovasc Med. 2023;24:340. 10.31083/j.rcm2412340 . Wang X, et al. Dexamethasone reduces the formation of thoracic aortic aneurysm and dissection in a murine model. Exp Cell Res. 2021;405:112703. 10.1016/j.yexcr.2021.112703 . Liu C, et al. Interleukin-3 stimulates matrix metalloproteinase 12 production from macrophages promoting thoracic aortic aneurysm/dissection. Clin Sci (Lond). 2018;132:655–68. 10.1042/cs20171529 . Deng J, et al. Murine model of elastase-induced proximal thoracic aortic aneurysm through a midline incision in the anterior neck. Front Cardiovasc Med. 2023;10:953514. 10.3389/fcvm.2023.953514 . Desi N, et al. Global analysis of RNA-binding proteins identifies a positive feedback loop between LARP1 and MYC that promotes tumorigenesis. Cell Mol Life Sci. 2022;79:147. 10.1007/s00018-021-04093-1 . Zhang Y, et al. c-Myc inhibits LAPTM5 expression in B-cell lymphomas. Ann Hematol. 2023;102:3499–513. 10.1007/s00277-023-05434-9 . Oeller M, et al. Transcription Factors STAT3 and MYC Are Key Players of Human Platelet Lysate-Induced Cell Proliferation. Int J Mol Sci. 2022;23. 10.3390/ijms232415782 . Wang J, et al. Transcription factor AP-2α activates RNA polymerase III-directed transcription and tumor cell proliferation by controlling expression of c-MYC and p53. J Biol Chem. 2023;299:102945. 10.1016/j.jbc.2023.102945 . Li J, et al. Circ-LAMP2 regulates aortic smooth muscle cell proliferation and apoptosis in thoracic aortic aneurysm via modulation of autophagy and NF-κB pathway. Hum Pathol. 2021;WITHDRAWN. 10.1016/j.humpath.2021.09.002 . Additional Declarations No competing interests reported. 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17:08:45","extension":"html","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":178985,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7912284/v1/1594ca1948a54a8a45c7635f.html"},{"id":95323827,"identity":"f3073fa3-4636-48f2-b1d1-4bfde82fad4d","added_by":"auto","created_at":"2025-11-06 17:08:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1024160,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of WGCNA modules associated with SASP-RGs in TAA.\u003c/strong\u003e A Box plot showing significantly higher ssGSEA scores of SASP-RGs in TAA tissues compared to controls (Wilcoxon test, p \u0026lt; 0.001). B Hierarchical clustering dendrogram of training set samples, confirming no outlier samples. C Scale-free network fitting analysis to determine the optimal soft threshold (set to 6, where connectivity approaches 0 and scale-free topology index R² ≥ 0.8). D Dendrogram of gene clustering results, showing 10 modules (excluding the gray module of unclustered genes). E Correlation analysis between module eigengenes and ssGSEA scores of SASP-RGs, with the yellow module exhibiting the highest correlation (cor = 0.88, p \u0026lt; 0.001). F Screening of 330 WGCNA genes from the yellow module based on strict thresholds (|Module Membership (MM)| \u0026gt; 0.8, |Gene Significance (GS)| \u0026gt; 0.7).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7912284/v1/bc1a5cdd4bbcf5a2ae6c0e80.png"},{"id":95323830,"identity":"df01ec87-7fc8-4f70-806a-1cebd3e280c7","added_by":"auto","created_at":"2025-11-06 17:08:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3872353,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification and functional annotation of candidate genes in TAA. \u003c/strong\u003eA Volcano plot showing differentially expressed genes (DEGs) between TAA and control tissues, with 470 upregulated and 123 downregulated genes (|log2FC| \u0026gt; 0.5, adj. p \u0026lt; 0.05). B Heatmap of the top 10 upregulated and downregulated DEGs, highlighting their expression patterns in TAA and control samples. C Venn diagram illustrating the intersection of 593 DEGs with 330 WGCNA genes, yielding 229 candidate genes. D Gene Ontology (GO) enrichment analysis of candidate genes, showing top biological processes (BPs), molecular functions (MFs), and cellular components (CCs). E Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, highlighting top enriched pathways such as \"tuberculosis\" and \"cell adhesion molecules\". F Protein-protein interaction (PPI) network of candidate genes, with PTPRC, CD4, and ITGB2 exhibiting the highest connectivity (Degree). G-I Top 100 genes ranked by \"Degree of Maximum Neighborhood Component (DMNC)\", \"Stress\", and \"ClusteringCoefficient\" algorithms in the PPI network. J Venn diagram of the top 100 genes from each algorithm, identifying 36 overlapping candidate key genes.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7912284/v1/3af4fc4958b9894e90d9ccf9.png"},{"id":95323829,"identity":"25bfead2-06c7-4a2f-8d86-adff77899199","added_by":"auto","created_at":"2025-11-06 17:08:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1750232,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of key genes via LASSO regression and expression verification.\u003c/strong\u003e A LASSO coefficient profile of the 36 candidate key genes. The x-axis represents the log(λ) value, and the y-axis represents the coefficient of each gene. As λ increases, the number of non-zero coefficients gradually decreases, indicating that more genes are excluded from the model. B Cross-validation for LASSO regression. The x-axis is log(λ), and the y-axis is the mean squared error (MSE). The vertical dashed lines indicate the optimal λ values: the left line corresponds to λ.min (0.02239849), which is the λ with the smallest MSE; the right line corresponds to λ.1se, which is the largest λ where the MSE is within 1 standard error of the minimum. The optimal LASSO model was determined using λ.min. C Expression verification of LASSO genes in the training set (GSE26155). The box plots show the expression levels of CR1 and F13A1 in TAA tissues and normal control tissues. Both genes are significantly highly expressed in TAA tissues (p \u0026lt; 0.05). D Expression verification of LASSO genes in the validation set (GSE219204). Similarly, the box plots demonstrate that CR1 and F13A1 are significantly upregulated in TAA smooth muscle cell samples compared to normal smooth muscle cell samples (p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7912284/v1/62ceb47c1d9b3281ce84f641.png"},{"id":95323833,"identity":"3aee30a1-b186-436d-88f8-3765254f0cdf","added_by":"auto","created_at":"2025-11-06 17:08:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1659434,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiagnostic value of the nomogram and immune cell infiltration characteristics in TAA. \u003c/strong\u003eA Nomogram for predicting the risk of thoracic aortic aneurysm (TAA). The nomogram integrates the expression levels of key genes (CR1 and F13A1) to calculate a total score, which corresponds to the probability of TAA. Each gene is assigned a specific score based on its expression level, and the sum of these scores (total score) is mapped to the bottom scale to determine the TAA risk probability. B Calibration curve of the nomogram. The x-axis represents the predicted probability of TAA, and the y-axis represents the actual probability. The diagonal dashed line indicates the ideal scenario where predicted probabilities perfectly match actual probabilities. The solid line represents the performance of the nomogram, with bootstrap correction (1,000 repetitions) applied. The Hosmer-Lemeshow test yielded a p-value of 0.215, indicating good consistency between predicted and actual values. C Decision curve analysis (DCA) of the nomogram. The x-axis is the threshold probability, and the y-axis is the net benefit. The red line represents the nomogram model, while the gray lines represent the extreme strategies of \"treating all\" (assuming all samples are TAA) and \"treating none\" (assuming no samples are TAA). The nomogram shows a higher net benefit than the extreme strategies across a wide range of threshold probabilities, demonstrating its clinical utility. D Heatmap of immune cell infiltration levels in TAA and control samples. The color gradient (blue to red) indicates the relative enrichment level of 28 immune cell types (rows) in each sample (columns). Neutrophils show low infiltration in all samples, while central memory CD4 T cells exhibit high infiltration across samples. E Box plots showing the difference in infiltration levels of differentially infiltrated immune cells (DIICs) between TAA and control groups. Immune cells such as macrophages, eosinophils, mast cells, and regulatory T cells are significantly more infiltrated in TAA (p \u0026lt; 0.05), while CD56dim natural killer cells, effector memory CD4 T cells, memory B cells, neutrophils, monocytes, and type 17 T helper cells show lower infiltration in TAA. F Correlation heatmap of DIICs and key genes. The color gradient (blue to red) represents Spearman correlation coefficients. All DIICs show positive correlations, with the strongest correlation between regulatory T cells and myeloid-derived suppressor cells (MDSC, r = 0.96, p \u0026lt; 0.001). Key genes (CR1 and F13A1) also exhibit strong positive correlations with DIICs, including CR1 with gamma delta T cells (r = 0.90, p \u0026lt; 0.001) and F13A1 with T follicular helper cells (r = 0.88, p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7912284/v1/7e6d75d973d737f91fa26da3.png"},{"id":95323831,"identity":"6c0cdc3e-0f8e-42b0-859a-46f496d363ca","added_by":"auto","created_at":"2025-11-06 17:08:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2354181,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePathway enrichment and regulatory network analysis of key genes. \u003c/strong\u003eA Gene Set Enrichment Analysis (GSEA) results for CR1. The top 5 significantly enriched signaling pathways are shown, including \"cytokine-cytokine receptor interaction\", \"natural killer cell mediated cytotoxicity\", \"hematopoietic cell lineage\", \"chemokine signaling pathway\", and \"systemic lupus erythematosus\". Each pathway is represented by a plot with the x-axis indicating the rank of genes sorted by correlation with CR1, and the y-axis representing the running enrichment score (RES). The vertical black lines mark the positions of genes in the pathway within the sorted list. B GSEA results for F13A1. Similar to CR1, the top 5 enriched pathways include \"cytokine-cytokine receptor interaction\", \"natural killer cell mediated cytotoxicity\", \"hematopoietic cell lineage\", \"chemokine signaling pathway\", and \"systemic lupus erythematosus\", demonstrating overlapping functional associations with CR1. C Gene-gene interaction (GGI) network of key genes and their functionally related genes. Nodes represent genes, and edges indicate functional interactions (mainly physical interactions). Key genes (CR1 and F13A1) are highlighted, and they interact with genes such as F13B, FGB, and CD46. The network is associated with shared functions including \"complement activation\" and \"protein activation cascade\". D TF-miRNA-mRNA regulatory network. The network includes key genes (CR1 and F13A1), their predicted upstream transcription factors (TFs) and microRNAs (miRNAs). TFs such as SCL, SOX2, P63, MYC, and PBX1 are shared regulators of both CR1 and F13A1. miRNAs targeting CR1 (e.g., hsa-miR-6867-3p, hsa-miR-578) and F13A1 (e.g., hsa-miR-3682-3p, hsa-miR-7853-5p) are also shown. Nodes are color-coded by enti\u003cstrong\u003ety \u003c/strong\u003etype (TFs, miRNAs, mRNAs), and edges represent regulatory relationships.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7912284/v1/2ca535a127ea6b387a485023.png"},{"id":95524498,"identity":"f33bebf8-b809-49c7-9f5e-0dc07e11f969","added_by":"auto","created_at":"2025-11-10 10:02:49","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":945260,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003escRNA-seq data processing and cell type annotation. \u003c/strong\u003eA Top 10 highly variable genes in the scRNA-seq dataset (GSE143921) after quality control. The genes are ranked by their variance, with HBA2 and HBB among the most variable. The plot displays the average expression (x-axis) and dispersion (y-axis) of these genes, highlighting their high variability across cells. B PCA variance drop-off plot. The x-axis represents the principal components (PCs), and the y-axis shows the percentage of variance explained by each PC. The inflection point occurs at the 15th PC, indicating that the first 15 PCs capture the majority of the variability in the data. Thus, dims = 15 was selected for subsequent clustering analysis. C Uniform Manifold Approximation and Projection (UMAP) plot of cell clustering. Cells are clustered into 12 distinct clusters (resolution = 0.4) based on the first 15 PCs. Each cluster is represented by a different color, reflecting the transcriptional heterogeneity in the dataset. D UMAP plot of cell type annotations. The 12 clusters were annotated as 8 major cell types—smooth muscle cells, erythrocyte cells, monocytes, fibroblasts, endothelial cells, macrophages, CD8 T cells, and plasma cells—using marker genes from the CellMarker database and literature. Each cell type is labeled with a specific color, illustrating the distribution of different cell populations in the dataset.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7912284/v1/d4fcdf962553575719f33c9c.png"},{"id":95323835,"identity":"7729d7f0-5660-47ac-9a0b-e485d109875b","added_by":"auto","created_at":"2025-11-06 17:08:44","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":980094,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of macrophages as key cells and dynamic expression of F13A1 during macrophage development. \u003c/strong\u003eA Violin plot showing the expression levels of key genes (CR1 and F13A1) across different cell types in the scRNA-seq dataset. Macrophages exhibit high expression of both genes compared to other cell types, indicating their potential association with key biological processes in TAA. B Dot plot displaying the expression abundance and percentage of key genes in macrophages. The size of the dots represents the percentage of cells expressing the gene, and the color intensity indicates the average expression level. Macrophages show high expression percentages and abundances for both CR1 and F13A1. C Box plot comparing the expression of key genes in macrophages between TAA and control groups. Both CR1 and F13A1 are significantly upregulated in TAA macrophages (p \u0026lt; 0.05), confirming macrophages as key cells involved in TAA pathogenesis. D UMAP plot of re-clustered macrophages, showing 2 distinct subclusters. Each subcluster is represented by a different color, reflecting transcriptional heterogeneity within macrophages. E Pseudotime trajectory analysis of macrophage development. The trajectory is visualized using a tree-like structure, with each node representing a differentiation state. Macrophages progress through 5 distinct states, with state1 as the initial stage and state4 as the final stage of development. F Distribution of macrophage states along the pseudotime axis. Each state is color-coded, illustrating the sequential progression of macrophages from the initial to the final developmental stage. G Heatmap showing the dynamic expression of F13A1 during macrophage pseudotime development. F13A1 expression increases from the initial stage (state1) to the middle stage, then decreases, and is absent in the later stage (state4), suggesting its role in regulating macrophage differentiation.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-7912284/v1/85577239a2a5e2726cb11f14.png"},{"id":95323834,"identity":"6d13dd02-bc3f-40ff-961b-93fc33a77ab2","added_by":"auto","created_at":"2025-11-06 17:08:44","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1799424,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e \u003cstrong\u003eCell-cell communication changes involving macrophages in TAA. \u003c/strong\u003eA-B Quantitative comparison of cell-cell communication intensity between macrophages and other cell types in TAA and control groups. The x-axis represents different cell types interacting with macrophages, and the y-axis indicates the communication intensity. Compared with the control, TAA shows decreased communication intensity between macrophages and fibroblasts/endothelial cells, while communication with monocytes is enhanced. C-D Number of significant ligand-receptor pairs between macrophages and other cell types in TAA and control groups. The bar plot demonstrates that TAA has fewer interactions between macrophages and fibroblasts/endothelial cells but more interactions with monocytes compared to the control. E Bubble plot of key ligand-receptor pairs in the control group. The size of bubbles represents the significance of the interaction, and the color intensity indicates the average expression level of the ligand-receptor pair. The dominant interactions include C3-(ITGAX + ITGB2) between fibroblasts and macrophages, and CXCL8-ACKR1 between macrophages and endothelial cells. F Bubble plot of key ligand-receptor pairs in the TAA group. The interaction between fibroblasts and macrophages is weakened, while the interaction between macrophages and monocytes is strengthened via th\u003cstrong\u003ee \u003c/strong\u003eANXA1-FPR1 ligand-receptor pair.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-7912284/v1/f4903cb292101d36c2457e29.png"},{"id":95524324,"identity":"00561f5f-c44c-48cf-896a-49a31af656ea","added_by":"auto","created_at":"2025-11-10 10:02:37","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":851481,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of key gene expression in clinical TAA samples via RT-qPCR.\u003c/strong\u003e Bar plots showing the relative expression levels of CR1 A and F13A1 B in clinical thoracic aortic aneurysm (TAA) tissues and normal control tissues.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-7912284/v1/96360591610bbbd0fc8bbbe1.png"},{"id":98622031,"identity":"13da271d-7544-4c73-83c9-fa00a57f2765","added_by":"auto","created_at":"2025-12-19 16:42:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":15526064,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7912284/v1/3f647bcb-178b-4a04-b853-9a6406a8a391.pdf"},{"id":95323828,"identity":"33b4c42b-02b8-4812-87ef-c491040688af","added_by":"auto","created_at":"2025-11-06 17:08:44","extension":"rar","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1597286,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.rar","url":"https://assets-eu.researchsquare.com/files/rs-7912284/v1/1247f76341068465305e82d6.rar"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated analysis of the diagnostic value and mechanism of action of the senescence-associated secretory phenotype in thoracic aortic aneurysm relying on bulk RNA-Seq and scRNA-Seq","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThoracic aortic aneurysm (TAA) is a life-threatening cardiovascular disease with complex pathogenesis. Genetic predisposition accounts for approximately 20% of cases, involving mutations in genes such as FBN1 and TGFBR1, while key environmental risk factors include hypertension and smoking\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Pathologically, TAA is characterized by progressive dilation of the thoracic aorta exceeding 50% of the normal diameter. In Western countries, TAA-related mortality constitutes 1\u0026ndash;2% of total mortality. Acute aortic dissection (AAD) may lead to sudden death, and survivors often suffer from multi-organ dysfunction\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Current therapeutic strategies for TAA primarily include open surgical repair and thoracic endovascular aortic repair (TEVAR). However, both approaches are associated with high perioperative mortality, and TEVAR carries a notable risk of endoleak. Small TAAs are typically managed with watchful waiting, while pharmacological interventions remain limited\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. TAA is often asymptomatic in early stages, yet poses a high risk of rupture and fatality. For instance, approximately 50% of patients with AAD die before hospital evaluation\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Therefore, early diagnosis and intervention are critical for TAA patients to reduce mortality and improve quality of life.\u003c/p\u003e\u003cp\u003eThe senescence-associated secretory phenotype (SASP) refers to the phenomenon wherein senescent cells secrete inflammatory cytokines, chemokines, and matrix metalloproteinases (MMPs). During organismal aging, SASP reinforces senescence through paracrine/autocrine mechanisms, driving chronic inflammation and accelerating aging, while paradoxically participating in tissue repair and embryonic development. Accumulating evidence indicates that SASP is closely linked to the pathogenesis of cancer, cardiovascular diseases, and neurodegenerative disorders\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Although the role of SASP in the pathogenesis and diagnostic value of TAA remains incompletely defined, emerging evidence suggests its potential involvement in TAA progression. Inflammation plays a pivotal role in TAA development, and SASP-derived inflammatory mediators may exacerbate local inflammatory responses, thereby disrupting the normal structure and function of the vascular wall. Additionally, vascular smooth muscle cell (VSMC) senescence is critically implicated in TAA pathogenesis, and SASP likely promotes VSMC senescence, further compromising vascular wall stability. Furthermore, apoptosis has been associated with TAA, and SASP may modulate apoptotic pathways, perturbing cellular homeostasis within the vascular wall and ultimately contributing to TAA initiation and progression\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSingle-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing are pivotal technologies for gene expression profiling. scRNA-seq employs optimized next-generation sequencing (NGS) to resolve cellular heterogeneity at the single-cell level, enabling the identification of gene expression signatures across distinct cell subpopulations. For instance, scRNA-seq has identified 20 distinct cell clusters in ovarian cancer and delineated 15 pancreatic cell subtypes in acute pancreatitis\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. In contrast, bulk RNA sequencing analyzes gene expression profiles at the population level, providing an integrated overview of transcriptional activity, as demonstrated in studies of bladder cancer and hepatocellular carcinoma\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The combined application of scRNA-seq and bulk RNA sequencing allows multi-scale investigation of gene expression regulation\u0026mdash;from global patterns to single-cell resolution\u0026mdash;thereby overcoming limitations inherent to either approach alone. In cancer research, this integrative strategy facilitates exploration of tumor microenvironment dynamics, including cell-cell interactions and regulatory mechanisms. For example, studies on cancer-associated fibroblasts (CAFs) have elucidated their transcriptional heterogeneity and subpopulation-specific roles\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. In the context of SASP research, integrated sequencing enables comprehensive analysis of SASP-related gene expression and intercellular signaling, offering novel insights into SASP-driven mechanisms in TAA pathogenesis and accelerating therapeutic discovery.\u003c/p\u003e\u003cp\u003eThis study aims to employ bulk RNA sequencing and scRNA-seq technologies to investigate the diagnostic value and mechanistic roles of SASP-related genes in TAA. By systematically integrating data obtained from both approaches, we seek to identify and validate novel TAA diagnostic biomarkers, thereby providing more precise tools for early disease detection. Concurrently, this research will elucidate the molecular mechanisms by which SASP drives TAA pathogenesis, pinpoint potential therapeutic targets, and lay a robust theoretical foundation for developing innovative therapeutic strategies. From a clinical perspective, our findings are expected to improve clinical outcomes for TAA patients, offering critical translational implications for disease management.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data source\u003c/h2\u003e\u003cp\u003eIn Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the datasets GSE26155 (platform: GPL5175), GSE219204 (GPL24539), and GSE143921 (GPL18573) were selected (Access time: January 7, 2025). In training set GSE26155, only 43 thoracic aortic aneurysm (TAA) tissue samples and 43 normal thoracic aortic tissue samples (control) in GSE26155 were retained, while the rest of the samples were excluded. Meanwhile, 8 smooth muscle cell samples from TAA tissues and 3 smooth muscle samples from normal thoracic artery tissues in GSE219204 were selected as validation set. The remaining samples in GSE219204 were excluded. Both of these datasets were mRNA microarray datasets. GSE143921 was a single-cell RNA sequencing (scRNA-seq) dataset, encompassing 3 TAA ascending aortic tissue samples and 3 normal ascending aortic tissue samples.Relying on the records in literature\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, all 83 senescence-associated secretory phenotype-related genes (SASP-RGs) were listed in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Weighted gene co-expression network analysis (WGCNA)\u003c/h2\u003e\u003cp\u003eThe ssGSEA scores of SASP-RGs in training set samples were computed via the ssGSEA algorithm in \"GSVA\" package (v 1.42.0)\u003csup\u003e14\u003c/sup\u003e. The magnitude of the scores could reflect the degree of interrelation within the samples and SASP-RGs. In TAA and control, Wilcoxon test was employed to determine whether there was a marked difference in this score between groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). If a difference was detected, SASP-RGs were considered to be associated with TAA. Subsequently, WGCNA was employed to identify the module genes in training set that were most correlated with ssGSEA scores of SASP-RGs.\u003c/p\u003e\u003cp\u003eIn \"WGCNA\" package (v 1.71)\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, hierarchical clustering was first executed on all samples in training set to detect and remove outliers. Subsequently, to ensure that the constructed network was better conformed to a scale-free distribution, the scale-free network evaluation coefficient R\u003csup\u003e2\u003c/sup\u003e was set to 0.80. The soft threshold with a value exceeding 0.80 and connectivity close to 0 was selected as the optimal soft threshold. Then, with minModuleSize set to 100 and mergeCutHeight set to 0.25, co-expression modules were gained by merging modules with high similarity. The Pearson correlation between these co-expression modules and ssGSEA scores of SASP-RGs was examined (|correlation (cor)| \u0026gt;0.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Genes in the module with the highest interrelation were further restricted (|Module Membership (MM)| \u0026gt;0.8, |Gene Significance (GS)| \u0026gt;0.7) to acquire WGCNA genes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Screening of candidate genes\u003c/h2\u003e\u003cp\u003eDifferentially expressed genes (DEGs) between TAA and control in training set were examined through \"limma\" package (v 3.54.1)\u003csup\u003e16\u003c/sup\u003e(|log\u003csub\u003e2\u003c/sub\u003efold change (FC)| \u0026gt;0.5, adj.p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A volcano plot was generated with the use of \"ggplot2\" package (v 3.3.6) \u003csup\u003e17\u003c/sup\u003eto display expression of DEGs. DEGs were sorted on the basis of log\u003csub\u003e2\u003c/sub\u003eFC, and the names of the top 10 DEGs with the highest and lowest log\u003csub\u003e2\u003c/sub\u003eFC values were labeled. Subsequently, a heatmap was diagrammed via \"ComplexHeatmap\" package (v 2.14.0) \u003csup\u003e18\u003c/sup\u003eto visually present the expression amounts of these 20 labeled DEGs. Subsequently, DEGs were intersected with SASP-RGs, and the overlapping genes were taken as candidate genes. These candidate genes were presented via \"ggvenn\" package (v 0.1.9) \u003csup\u003e19\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Functional and interaction analysis of candidate genes\u003c/h2\u003e\u003cp\u003eFunctional analysis, including Gene Ontology (GO) and Kyoto EncyclDMedia of Genes and Genomes (KEGG) analyses, was implemented by \"clusterProfiler\" package (v 4.6.2) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003csup\u003e20\u003c/sup\u003e. Among them, GO annotated functions of genes, covering biological processes (BPs) at a macroscopic level, molecular functions (MFs) at a molecular level, and cellular components (CCs) where the genes were located. KEGG studied the pathways in which genes were involved and revealed the relevant biological mechanisms of genes. Relying on results gained from the analysis, the number of genes replete with each functional part or pathway was sorted in descending order. The top 5 entries of each part in GO and top 10 pathways in KEGG were presented.\u003c/p\u003e\u003cp\u003eSubsequently, in STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with a confidence level set to be \u0026ge;\u0026thinsp;0.4, the protein-level interactions among candidate genes were examined. The candidate genes with interactions were incorporated into Cytoscape software (v 3.9.1) for the construction of a protein-protein interaction (PPI) network\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Meanwhile, within CytoHubba plugin of Cytoscape software (v 3.9.1), \u0026lsquo;Degree of Maximum Neighborhood Component (DMNC)\u0026rsquo;, \u0026lsquo;Stress\u0026rsquo;, and \u0026lsquo;ClusteringCoefficient\u0026rsquo; algorithms were selected to score interactions of candidate genes in PPI network. The candidate genes ranked among the top 100 scores in each algorithm were intersected to acquire candidate key genes, which were then presented via \"ggvenn\" package (v 0.1.9).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Confirmation of key genes\u003c/h2\u003e\u003cp\u003eMachine learning was conducted in training set. First, least absolute shrinkage and selection operator (LASSO) regression analysis was executed on candidate key genes in \"glmnet\" package (v 4.1-4) to construct a LASSO model. The model introduced a penalty term (regularization coefficient, lambda) to achieve variable selection. Genes that had a marked impact on disease were screened out, and unimportant genes were excluded. When the lambda value reached the minimum, LASSO model achieved optimality. We denoted candidate key genes in optimal LASSO model as LASSO genes. Subsequently, in both training set and validation set, the expression of LASSO genes was verified through Wilcoxon test. LASSO genes that presented differential expression and had consistent expression trends in two datasets were denoted as key genes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Meanwhile, box plots were diagrammed in \"ggplot2\" package (v 3.3.6) to visually present this difference.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Diagnostic value of key genes\u003c/h2\u003e\u003cp\u003eTo explore the diagnostic value of key genes for TAA, a nomogram was developed. Specifically, in training set, key genes were incorporated into \"rms\" package (v6.5.0) to create the nomogram\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The scores of each key gene were aggregated to form a total score, which corresponded to the probability of a sample in training set having TAA. Relying on nomogram, a calibration curve was diagrammed again in \"rms\" package (v6.5.0), and bias correction was implemented with 1,000 repetitions via \"Bootstrapping\". In addition, the goodness-of-fit of the model was evaluated by Hosmer-Lemeshow (HL) test (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Meanwhile, decision curve analysis (DCA) was conducted in \"rmda\" package (v 1.6)\u003csup\u003e23\u003c/sup\u003e, and a DCA curve was generated to determine clinical benefit of the nomogram.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Immune cell infiltration analysis\u003c/h2\u003e\u003cp\u003eIn training set, 28 types of immune cells\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e in ssGSEA algorithm of \"GSVA\" package (v 1.42.0)\u003csup\u003e14\u003c/sup\u003e were selected for analysis. The enrichment levels of these immune cells in TAA and control were observed. Meanwhile, Wilcoxon test was employed to analyze whether there were differences in the enrichment levels of immune cells between groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and a box plot was created in \"ggplot2\" package (v 3.3.6) for presentation. Next, in \"psych\" package (v 2.3.12)\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, Spearman correlations were examined among differentially infiltrated immune cells (DIICs), as well as between DIICs and key genes (|cor| \u0026gt;0.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Meanwhile, a interrelation heatmap was diagrammed in \"corrplot\" package (v 0.92)\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Gene set enrichment analysis (GSEA)\u003c/h2\u003e\u003cp\u003eRelying on default gene sets in MSigDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), GSEA was executed on key genes in training set. First, Spearman correlations between key genes and remaining genes were examined in \"psych\" package (v 2.3.12), and all genes were sorted in descending order on the basis of interrelation coefficients. Utilizing the sorting results, GSEA was implemented in \"clusterProfiler\" package (v 4.6.2) (|normalized enrichment score (NES)|\u0026gt;1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For the top 5 signaling pathways with the smallest p-values, plots were generated in \"enrichplot\" package (v 1.18.0)\u003csup\u003e26\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Network construction\u003c/h2\u003e\u003cp\u003eGeneMANIA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://genemania.org/\u003c/span\u003e\u003cspan address=\"https://genemania.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) could explore genes functionally connected with key genes. Thus, a gene-gene interaction network was generated via this database, and the top 7 shared functions of these genes were presented. In addition, the upstream microRNAs (miRNAs) and transcription factors (TFs) of key genes were predicted, and a TF-miRNA-mRNA network was created respectively in Cytoscape software (v 3.9.1). Specifically, miRNAs targeting key genes were identified in microcosm database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ebi.ac.uk/enright-srv/microcosm/\u003c/span\u003e\u003cspan address=\"http://www.ebi.ac.uk/enright-srv/microcosm/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the TFs targeting key genes were identified in NetworkAnalyst database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.networkanalyst.ca/NetworkAnalyst/\u003c/span\u003e\u003cspan address=\"https://www.networkanalyst.ca/NetworkAnalyst/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.10 Processing and annotation of scRNA-seq data\u003c/h2\u003e\u003cp\u003eIn \"Seurat\" package (v 5.0.1)\u003csup\u003e27\u003c/sup\u003e, scRNA-seq dataset was pre-processed and annotated. Specifically, first, quality control was executed on the data. Standards of 200\u0026thinsp;\u0026lt;\u0026thinsp;nFeature_RNA\u0026thinsp;\u0026lt;\u0026thinsp;4,000 and nCount_RNA\u0026thinsp;\u0026lt;\u0026thinsp;6,0000 were set to acquire qualified cells and genes. Subsequently, vst method was adopted to extract the top 2,000 genes with the greatest variance among the qualified genes, and the names of the top 10 genes with the greatest variance were presented. Next, principal component analysis (PCA) was carried out on samples in the data. Thereafter, JackStrawPlot function was employed to compare distribution of p-values of each principal component (PC) and sort the percentage of each PC. Meanwhile, a PCA variance drop-off plot was diagrammed. In the variance drop-off plot, PCs at the inflection point were retained for subsequent clustering analysis. In the clustering analysis, FindNeighbors and FindClusters functions were employed to cluster cells into different clusters (resolution\u0026thinsp;=\u0026thinsp;0.4). Simultaneously, Uniform Manifold Approximation and Projection (UMAP) clustering method was incorporated for dimensionality reduction and presentation. Subsequently, by integrating FindAllMarkers function (logFC\u0026thinsp;=\u0026thinsp;0.5, Minpct\u0026thinsp;=\u0026thinsp;0.2, only.pos\u0026thinsp;=\u0026thinsp;TRUE), the literature\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, and the marker genes in CellMarker database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bio-bigdata.hrbmu.edu.cn/cellmarker/\u003c/span\u003e\u003cspan address=\"http://bio-bigdata.hrbmu.edu.cn/cellmarker/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), different clusters were annotated as different cell types.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.11 Confirmation of key cells\u003c/h2\u003e\u003cp\u003eInitially, the proportion of key gene expression in different cell types was observed. Subsequently, the difference in expression abundances of key cells between TAA and the control was examined via Wilcoxon test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There were differential expressions of key genes, and the cells with high expression of key genes were recorded as key cells.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.12 Pseudo-time analysis and cell-cell communication\u003c/h2\u003e\u003cp\u003eHere, key cells were re-clustered. High-variable genes were filtered on the basis of the criteria of \"average expression amount\u0026thinsp;\u0026ge;\u0026thinsp;0.1 and empirical_dispersion\u0026thinsp;\u0026ge;\u0026thinsp;1 * fit_dispersion\", and dimensionality reduction was executed by virtue of \"DDRTree\" package (v 0.1.5) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/package\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/package\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e = DDRTree). After key cells were clustered into different clusters, the developmental trajectories of key cells were examined in \"Monocle\" package (v 2.26.0)\u003csup\u003e29\u003c/sup\u003e. Subsequently, Branched Expression Analysis Modeling (BEAM) method in \"Monocle\" package (v 2.26.0) was employed to analyze key nodes, and expression heatmap of key genes in different pseudo-time sequences of key cells was plotted by virtue of plot_pseudo-time_heatmap function. For all the annotated cells, the inter-cellular communication networks in TAA and the control were examined in \"CellChat\" package (v 1.6.1)\u003csup\u003e30\u003c/sup\u003e. Meanwhile, bubble plots were generated to observe receptor-ligand interactions between different cell types.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.13 Reverse transcription-quantitative polymerase chain reaction (RT-qPCR)\u003c/h2\u003e\u003cp\u003eFrom June 1, 2024 to December 30, 2025, five thoracic aortic aneurysm (TAA) samples and five control samples were collected to validate the expression of key genes. The TAA group consisted of surgically resected thoracic aortic aneurysm tissue specimens (from patients who underwent thoracic aortic aneurysm resection), while the control group included normal aortic wall tissues obtained by aortic wall punch during coronary artery bypass grafting (CABG). All specimens were secondary utilization of routinely resected/discarded tissues during surgery, eliminating the need for additional sampling for the study. This research has been approved by the Ethics Committee of the Second Affiliated Hospital of Hebei Medical University, The study has obtained informed consent from all participants and/or their legal guardians. Research involving human research participants was conducted in accordance with the Helsinki Declaration. Inclusion criteria for TAA patients:1. Aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years; 2. Diagnosed with thoracic aortic aneurysm via whole-aorta computed tomography angiography (CTA) and underwent thoracic aortic aneurysm resection; 3. Complete medical records and comprehensive auxiliary examinations.Exclusion criteria for TAA patients: 1. Receiving other surgeries such as aortic valve replacement simultaneously with aortic valve replacement; 2. Ehlers-Danlos syndrome; 3. Marfan syndrome; 4. History of endovascular aortic repair for aortic dissection or abdominal aortic aneurysm; 5. Other known hereditary vascular or connective tissue diseases;6. Cancer; 7. Infection; 8. Inflammatory aortic aneurysm; 9. Comorbid immune system diseases or receiving immunosuppressants or modulators (e.g., psoriasis, systemic lupus erythematosus, Beh\u0026ccedil;et's disease, atopic dermatitis, ulcerative colitis, etc.).. Total RNA was extracted from tissue samples after lysis with 1 ml of TRIzol reagent. After the total RNA was air - dried naturally, it was redissolved and its concentration was measured again. Subsequently, 2 \u0026micro;g of total RNA was taken for reverse transcription into cDNA. The reverse transcription kit used was Hifair\u0026reg; Ⅲ 1st Strand cDNA Synthesis SuperMix for qPCR (Yeasen, China), and the reverse transcription was carried out in an S1000\u0026trade; Thermal Cycler ordinary PCR instrument (BIO - RAD, USA) (\u003cb\u003eTable S2\u003c/b\u003e). Then, the cDNA was diluted 5\u0026ndash;20 times and amplified using Servicebio\u0026reg; 2\u0026times;Universal Blue SYBR Green qPCR Master Mix (Servicebio, China). The amplification instrument was a CFX Connect real - time quantitative fluorescence PCR instrument (BIO - RAD, USA) (40 cycles, \u003cb\u003eTable S3\u003c/b\u003e). The primer sequences were shown in \u003cb\u003eTable S4\u003c/b\u003e. GAPDH was used as the internal reference gene for the relative quantification of key genes. The relative expression abundances of key genes were computed by 2\u003csup\u003e\u0026minus;△△CT\u003c/sup\u003e method. The graphs were plotted by virtue of Graphpad Prism 10, and the t-test was employed to compare the expression differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e2.14 Statistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses in this study were conducted in R language (v 4.2.3). Wilcoxon test was applied to analyze differences between two groups. The inter-group test for RT-qPCR was executed by virtue of t-test. A p-value under 0.05 was considered as criterion for statistical significance.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Identification of WGCNA modules associated with SASP-RGs in TAA\u003c/h2\u003e\u003cp\u003eThe ssGSEA scores of SASP-RGs were higher in TAA (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), enabling subsequent WGCNA analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Additionally, no outlier samples were present in training set (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). When the soft-threshold was set to 6, the connectivity was close to 0 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Subsequently, genes were clustered into 10 modules (excluding un-clusterable gray module) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Among them, the yellow module had the highest interrelation with ssGSEA scores of SASP-RGs, reaching 0.88 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Furthermore, after restricting |MM| and |GS| of the yellow module, all 330 module genes connected with SASP-RGs were gained (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF, \u003cb\u003eTable S5\u003c/b\u003e), denoted as WGCNA genes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Identification and functional annotation of candidate genes in TAA\u003c/h2\u003e\u003cp\u003eThere were 593 DEGs between TAA and the control. Among them, 470 DEGs presented an upward expression trend in TAA, while 123 DEGs presented a downward trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B, \u003cb\u003eTable S6\u003c/b\u003e). These 593 DEGs had 229 overlapping candidate genes with 330 WGCNA genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). In terms of GO-BP, candidate genes were mainly involved in 950 processes such as \"leukocyte mediated immunity\", \"leukocyte cell-cell adhesion\", and \"positive regulation of cell activation\". Regarding GO-MF, candidate genes were primarily connected with 90 functions like \"immune receptor activity\", \"GTPase regulator activity\", and \"nucleoside-triphosphatase regulator activity\". In terms of GO-CC, candidate genes were mostly located in 85 components including \"external side of plasma membrane\" and \"secretory granule membrane\" (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, \u003cb\u003eTable S7\u003c/b\u003e). In KEGG, candidate genes were associated with 65 signaling pathways, such as \"tuberculosis\", \"staphylococcus aureus infection\", and \"cell adhesion molecules\" (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, \u003cb\u003eTable S8\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAmong 229 candidate genes, 205 had interactions. The three genes with the highest Degree were PTPRC, CD4, and ITGB2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). Relying on algorithms of DMNC, Stress, and Clustering Coefficient, the genes among these 205 candidate genes that ranked in the top 100 in terms of scores were shown as in figures (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG-I). After taking intersection, 36 overlapping candidate key genes were gained (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Identification of key genes via LASSO regression and expression verification\u003c/h2\u003e\u003cp\u003eWhen lambda.min was set at 0.02239849, LASSO model reached its optimal state (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-B). LASSO genes included in model were F13A1, FERMT3, AIF1, CD300A, TLR7, CD28, CD69, CR1, CCR1, and IL7R. Among them, CR1 and F13A1 had high expression abundances in TAA samples of both training set and validation set (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-D).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Diagnostic value of the nomogram and immune cell infiltration characteristics in TAA\u003c/h2\u003e\u003cp\u003eThe nomogram constructed via key genes presented that each key gene corresponded to a score, and the combined total score was directly proportional to the disease risk of TAA (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Subsequently, on the basis of the evaluation of calibration curve, the prediction curve of the nomogram had a high degree of overlap with the actual curve. HL test indicated that there was no marked deviation between predicted values and true values (p\u0026thinsp;=\u0026thinsp;0.215) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). In addition, the clinical benefit of the nomogram was exceed that of that of extreme values \"none\" and \"all\", demonstrating a good benefit (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). This nomogram - based diagnostic model could serve as a potentially developable tool for TAA diagnosis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOverall, infiltration level of neutrophils was low in all samples, while that of central memory CD4 T cells was high in all samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). With exception of CD56dim natural killer cells, effector memory CD4 T cells, memory B cells, neutrophils, monocytes, and type 17 T helper cells, the expression of the remaining immune cells was higher in TAA (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), such as macrophages, eosinophils, mast cells, and regulatory T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). There were positive interrelations among all DIICs. Specifically, the interrelation between regulatory T cells and myeloid-derived suppressor cells (MDSC) was as high as 0.96 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Moreover, there were also marked positive interrelations between key genes and DIICs (cor\u0026thinsp;\u0026gt;\u0026thinsp;0.40, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The interrelation between F13A1 and T follicular helper cells reached 0.88, and the interrelation between CR1 and gamma delta T cells was as high as 0.90 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). This suggested that these DIICs might play important roles in the progression of TAA.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Pathway enrichment and regulatory network analysis of key genes\u003c/h2\u003e\u003cp\u003eIn GSEA, CR1 was replete with 64 pathways, with the most marked ones being \"cytokine-cytokine receptor interaction\", \"natural killer cell mediated cytotoxicity\", \"hematopoietic cell lineage\", \"chemokine signaling pathway\", and \"systemic lupus erythematosus\" (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, \u003cb\u003eTable S9\u003c/b\u003e). F13A1 was markedly replete with 64 pathways such as \"cytokine-cytokine receptor interaction\", \"natural killer cell mediated cytotoxicity\", \"hematopoietic cell lineage\", \"chemokine signaling pathway\", and \"systemic lupus erythematosus\" (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, \u003cb\u003eTable S10\u003c/b\u003e). Evidently, key genes were all markedly associated with \"cytokine-cytokine receptor interaction\" and \"chemokine signaling pathway\". This indicated that these key genes might have participated in multiple biological processes connected with immune response and cell-cell communication, and were likely to have been closely involved in the pathophysiological mechanisms of TAA.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn GGI network, the interaction relationships between key genes and functionally similar genes (such as F13B, FGB, CD46) were mainly physical interactions. They jointly participated in functions such as \"complement activation\" and \"protein activation cascade\" (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). CR1 predicted 26 miRNAs, such as hsa-miR-6867-3p and hsa-miR-578; F13A1 predicted 18 miRNAs, such as hsa-miR-3682-3p and hsa-miR-7853-5p (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). In addition, CR1 and F13A1 predicted 23 and 18 TFs respectively, and they jointly targeted SCL, SOX2, P63, MYC, and PBX1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). This interrelation and regulatory relationship reflected the complex mechanism of action of key genes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e3.6 scRNA-seq data processing and cell type annotation\u003c/h2\u003e\u003cp\u003eRelying on screening criteria, 5,236 cells and 31,845 genes remained in the dataset (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA-B\u003c/b\u003e). Among them, the top 2,000 highly variable genes were shown in the figure, such as HBA2 and HBB (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). In addition, cluster analysis indicated a high degree of differentiation between TAA and control samples (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC\u003c/b\u003e). In the elbow plot of variance, the first 15 PCs contributed the most to the variation and were at the inflection point. Therefore, dims\u0026thinsp;=\u0026thinsp;15 was selected for cluster analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). After clustering, the cells were allocated into 12 clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). These 12 clusters were annotated as smooth muscle cells, erythrocyte cells, monocytes, fibroblasts, endothelial cells, macrophages, CD8 T cells, and plasma cells (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Identification of macrophages as key cells and dynamic expression of F13A1 during macrophage development\u003c/h2\u003e\u003cp\u003eRelying on expression abundances and expression differences of key genes, macrophages were marked as key cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-C). Key cells were clustered into 2 clusters and had 5 differentiation states. During the development states of macrophages, state1 was in initial stage of development, and state4 was in final stage of development (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD-F). Among them, the expression amount of F13A1 gradually rose from the initial to the middle stage of development, then declined, and there was no expression in later stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG). This implied that F13A1 was connected with macrophage development.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e3.8 Cell-cell communication changes involving macrophages in TAA\u003c/h2\u003e\u003cp\u003eCompared with the control, the quantity and intensity of communication between macrophages and fibroblasts as well as endothelial cells in TAA declined, while the communication effect with monocytes rose (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-D). In addition, in the control, the connection between fibroblasts and macrophages was mainly through C3\u0026minus;(ITGAX\u0026thinsp;+\u0026thinsp;ITGB2), and the main ligand - receptor pairs between macrophages and endothelial cells were CXCL8\u0026thinsp;\u0026minus;\u0026thinsp;ACKR1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE). In TAA, the communication between fibroblasts and macrophages was weakened, while the communication between macrophages and monocytes was enhanced, and its ligand-receptor pair was ANXA1\u0026thinsp;\u0026minus;\u0026thinsp;FPR1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eF). These findings suggested that the communication patterns among different cell types changed in TAA, which might have played a crucial role in the pathological process of TAA, potentially providing new insights into the underlying mechanisms and possible therapeutic targets for this disease.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e3.9 Validation of key gene expression in clinical TAA samples via RT-qPCR\u003c/h2\u003e\u003cp\u003eCompared with the control, the key genes CR1 and F13A1 were both significantly highly expressed in the TAA samples (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01)(Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA-B). This finding was consistent with the expression trends observed in the training and validation sets. This concordance not only strengthens the reliability of our data analysis but also suggests that CR1 and F13A1 may play crucial and consistent roles in the biological processes underlying TAA. Their elevated expression in TAA samples across different datasets implies that these genes could potentially serve as stable biomarkers for the diagnosis, prognosis assessment, or even as potential therapeutic targets for TAA.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTranscriptomic and single-cell sequencing data for TAA were retrieved from GEO. Eighty-three senescence-associated secretory phenotype-related genes (SASP-RGs) were curated from literature and expanded via WGCNA. CR1and F13A1 were identified via intersecting differential expression and WGCNA results, refined through LASSO regression, and validated for TAA diagnostic utility in a nomogram model. Immune infiltration analysis revealed significant immune cell-TAA correlations with these genes, while GSEA highlighted enriched pathways (e.g., cytokine-cytokine receptor interactions). Regulatory miRNAs and shared transcription factors (SCL, SOX2) were predicted for both genes. Single-cell analysis implicated macrophages in TAA progression, with F13A1 showing differentiation-dependent expression. Clinical specimens confirmed dysregulation of both genes.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTAA is an insidious disease with nonspecific early symptoms, such as chest tightness, chest pain, shortness of breath, and cough, which often lead to misdiagnosis\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. The pathogenesis of TAA involves a complex multifactorial process, demonstrating strong associations with genetic predisposition and age-related degenerative changes. In this study, we integrated TAA transcriptomic data with SASP-related genes and identified key candidates such as CR1 and F13A1(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA、B). A predictive nomogram was constructed, followed by comprehensive analyses of immune infiltration, pathway enrichment, and regulatory mechanisms (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Furthermore, single-cell resolution analysis revealed macrophages as pivotal cellular players in TAA progression. These findings provide a theoretical foundation for early diagnosis and mechanistic exploration of TAA\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Cellular senescence contributes to TAA pathogenesis through mitochondrial dysfunction, elevated oxidative stress, and increased SASP\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. In this study, we integrated transcriptomic data from TAA with SAPA-associated genes to identify key diagnostic genes for TAA within the SAPA framework using bioinformatics approaches. Further investigations revealed the mechanisms underlying immune infiltration, pathway enrichment, and regulatory networks mediated by these key genes in TAA. Additionally, single-cell data analysis identified critical cellular players driving TAA progression, providing a theoretical foundation for elucidating the role of SAPA in TAA pathogenesis.\u003c/p\u003e\u003cp\u003eThrough rigorous screening, CR1 and F13A1 were identified as pivotal genes in TAA. Notably, these critical genes exhibited significant upregulation in TAA specimens, a finding subsequently validated by PCR experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).CR1as a regulatory component of the complement cascade, has been implicated in the pathogenesis, progression, and prognosis of various diseases through its genetic polymorphisms. Three distinct CR1 polymorphic patterns demonstrate disease-specific effects: 1) Length and density polymorphisms predominantly influence Alzheimer's disease (AD) pathogenesis \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e; 2) Knops blood group antigen polymorphisms and erythrocytic CR1 density variations modulate malaria susceptibility\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e; 3) Erythrocytic CR1 density polymorphisms exhibit geographic and ethnic variations in their associations with neoplastic disorders\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, cardiovascular diseases\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, and leprosy. CR1 demonstrates a significant association with inflammatory cytokines/chemokines, which play pivotal roles in orchestrating immune responses through their regulation of immune cell migration and activation. CR1 dysfunction may induce dysregulated expression of these inflammatory mediators, consequently impairing normal immune cell functionality. This pathophysiological alteration manifests as diminished pathogen clearance capacity and persistent inflammatory activation. Notably, such chronic inflammatory states exhibit a close correlation with the SASP, suggesting a potential mechanistic link between CR1-mediated immune regulation and aging-related inflammatory processes\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. F13A1 encodes the A subunit of coagulation factor XIII, the final zymogen activated in the coagulation cascade. It plays a critical role in multiple physiological processes including blood coagulation, fibrinolysis, and platelet activation. Emerging evidence further associates F13A1 with inflammatory responses\u003csup\u003e\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eGiven the potential pivotal role of CR1 in TAA, this study employed GSEA to reveal that CR1 and F13A1 are primarily associated with the chemokine signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Activation of this pathway induces cytoskeletal rearrangement, directing immune cell migration toward inflammatory sites, injured tissues, or specific organs, thereby playing crucial roles in immune defense, inflammatory responses, and tissue repair processes\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. During aging, altered expression of chemokines and their receptors impairs immune cell trafficking and tissue regeneration, leading to diminished host responsiveness to infections and injuries\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Notably, chemokines facilitate inflammatory cell recruitment to vascular walls, contributing to inflammatory reactions in TAA pathogenesis. This process promotes vascular wall damage and remodeling, ultimately shaping the inflammatory microenvironment critical for TAA progression\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThrough the analysis of critical genes CR1 and F13A1, this study successfully constructed a nomogram, a graphical computational tool that integrates multiple variables into a comprehensive predictive index for intuitive disease risk assessment. Notably, this represents the first diagnostic model for TAA established using SASP-related genes. The positive correlation between key gene expression scores and TAA disease risk provides clinicians with a user-friendly tool for visual risk stratification. Validation through calibration curves and Hosmer-Lemeshow tests confirmed the nomogram's satisfactory predictive accuracy and reliability in TAA risk evaluation. Compared with conventional diagnostic approaches, this model demonstrates superior performance in risk quantification and synergistic integration of polygenic information(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), offering enhanced clinical applicability for comprehensive risk assessment\u003csup\u003e\u003cspan additionalcitationids=\"CR52 CR53\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eEmerging evidence indicates that immune cell infiltration plays a pivotal role in the pathogenesis and progression of TAA, with macrophages and T lymphocytes serving as key contributors in this process. As critical mediators of signal transduction and intercellular communication, macrophages demonstrate significant regulatory functions in both physiological homeostasis and pathological remodeling of the aortic wall. Notably, specific subsets of natural killer T (NKT) cells and CD8⁺ T lymphocytes have been identified to exert potential protective effects against TAA development(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD-F). These mechanistic insights substantially advance our understanding of TAA pathogenesis and provide a scientific foundation for the development of targeted immunomodulatory therapeutic strategies\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. The immune infiltration analysis revealed significant infiltration of multiple immune cell populations in TAA, including macrophages, eosinophils, mast cells, and regulatory T cells (Tregs), demonstrating a complex immune microenvironment. During the pathological progression of thoracic aortic aneurysm (TAA), massive macrophage accumulation occurs with pronounced infiltration from the adventitia into the media, a process regulated by multiple factors including the CCL2/CCR2 axis. These macrophages robustly activate inflammatory responses through secretion of various pro-inflammatory cytokines such as tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), and interleukin-6 (IL-6). These cytokines not only promote self-amplification through macrophage activation, proliferation, and sustained infiltration, but also recruit additional immune cells including neutrophils, collectively establishing a potent inflammatory microenvironment. Within this milieu, escalating inflammation induces vascular smooth muscle cell (VSMC) dysfunction and phenotypic switching, accompanied by severe extracellular matrix (ECM) degradation characterized by elastic fiber fragmentation and collagen breakdown. The persistent inflammatory cascade and progressive ECM deterioration synergistically compromise aortic wall structural integrity and biomechanical properties, ultimately driving TAA formation and progression\u003csup\u003e\u003cspan additionalcitationids=\"CR58 CR59\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Our single-cell analysis revealed macrophages as the predominant expressors of the F13A1 gene, identifying them as key cellular players in TAA pathogenesis. Subsequent clustering analysis further delineated two distinct F13A1-expressing macrophage subtypes exhibiting functional heterogeneity. This finding underscores the critical involvement of macrophage heterogeneity in TAA progression, reinforcing the pivotal role of macrophage subpopulations in disease dynamics.\u003c/p\u003e\u003cp\u003eTranscription factors (TFs) are a class of protein molecules that regulate gene transcription, the transcription factors SCL, SOX2, p63, MYC, and PBX1 play pivotal roles in modulating the expression of CR1 and F13A1(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). These transcription factors may bind to cis-acting elements within the promoter regions of CR1 and F13A1 genes, he progression of thoracic aortic aneurysm may involve modulation of mRNA transcription in CR1 and F13A1, thereby influencing critical biological processes in vascular smooth muscle cells (VSMCs), including proliferation, apoptosis, migration, as well as the synthesis and degradation of extracellular matrix components. For instance, MYC, a pivotal proto-oncogene\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e, exhibits dysregulated expression that potentially drives abnormal cellular proliferation and disrupts normal physiological functions of thoracic aortic wall cells\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e, ultimately contributing to aneurysm pathogenesis\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e.Systematic monitoring of these transcription factors and their associated mRNA levels could provide novel biomarkers for early diagnosis and dynamic assessment of thoracic aortic aneurysm progression. This molecular surveillance approach may establish a foundation for developing targeted therapeutic strategies to intervene in aneurysm development.\u003c/p\u003e\u003cp\u003eIn conclusion, this study investigated the mechanistic role of SASP in TAA pathogenesis through transcriptomic profiling and single-cell resolution analyses. We identified CR1 and F13A1 as pivotal genes and subsequently developed a nomogram model demonstrating potential diagnostic value. Furthermore, single-cell characterization revealed macrophages as critical cellular components interacting with these key genes during TAA progression. However, several limitations warrant consideration: the experimental validation cohort exhibited restricted sample size, and comprehensive exploration of molecular mechanisms underlying these genetic determinants remains imperative. Additionally, while the nomogram demonstrates theoretical diagnostic potential, future iterations should incorporate additional multidimensional features to enhance predictive accuracy for TAA risk assessment. Further mechanistic studies are required to elucidate the precise functional contributions of these identified genes in TAA pathophysiology.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee of The Second Hospital of Hebei Medical University (2025-R557). The study has obtained informed consent from all participants and/or their legal guardians. Research involving human research participants was conducted in accordance with the Helsinki Declaration. And written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors approved the publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Hebei Provincial Department of Finance Government Funding or Public hospital reform and high-quality development demonstration project (No.36); Hebei Provincial Department of Finance Government Funding or Specialty Capacity Building and Specialty Leader Train (No. 303-2022-27-07).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZTY and WBJ wrote the manuscript; FC and LY completed the experimental data collection; YH, HKC and LY were responsible for experimental data collation and statistical analysis. ZTY and WBJ guided the revision of the manuscript; LY and MQL provided manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZhou Z, Cecchi AC, Prakash SK, Milewicz DM. Risk Factors for Thoracic Aortic Dissection. Genes (Basel). 2022;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/genes13101814\u003c/span\u003e\u003cspan address=\"10.3390/genes13101814\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRodrigues Bento J, et al. The Genetics and Typical Traits of Thoracic Aortic Aneurysm and Dissection. Annu Rev Genomics Hum Genet. 2022;23:223\u0026ndash;53. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1146/annurev-genom-111521-104455\u003c/span\u003e\u003cspan address=\"10.1146/annurev-genom-111521-104455\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGouveia EMR, et al. Incidence and Prevalence of Thoracic Aortic Aneurysms: A Systematic Review and Meta-analysis of Population-Based Studies. Semin Thorac Cardiovasc Surg. 2022;34:1\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1053/j.semtcvs.2021.02.029\u003c/span\u003e\u003cspan address=\"10.1053/j.semtcvs.2021.02.029\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWatkins AC, Dalal A, Lee JT, Dake MD. Current Status of Endoluminal Treatment of Descending Thoracic Aortic Aneurysms. Cardiovasc Intervent Radiol. 2020;43:1770\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00270-020-02526-1\u003c/span\u003e\u003cspan address=\"10.1007/s00270-020-02526-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Y, et al. Animal Models, Pathogenesis, and Potential Treatment of Thoracic Aortic Aneurysm. Int J Mol Sci. 2024;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms25020901\u003c/span\u003e\u003cspan address=\"10.3390/ijms25020901\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaus M, et al. Iron accumulation drives fibrosis, senescence and the senescence-associated secretory phenotype. Nat Metab. 2023;5:2111\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s42255-023-00928-2\u003c/span\u003e\u003cspan address=\"10.1038/s42255-023-00928-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKumari R, Jat P. Mechanisms of Cellular Senescence: Cell Cycle Arrest and Senescence Associated Secretory Phenotype. Front Cell Dev Biol. 2021;9:645593. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcell.2021.645593\u003c/span\u003e\u003cspan address=\"10.3389/fcell.2021.645593\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAschacher T, et al. Impacts of Telomeric Length, Chronic Hypoxia, Senescence, and Senescence-Associated Secretory Phenotype on the Development of Thoracic Aortic Aneurysm. Int J Mol Sci. 2022;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms232415498\u003c/span\u003e\u003cspan address=\"10.3390/ijms232415498\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFang Z, Li J, Cao F, Li F. Integration of scRNA-Seq and Bulk RNA-Seq Reveals Molecular Characterization of the Immune Microenvironment in Acute Pancreatitis. Biomolecules. 2022;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/biom13010078\u003c/span\u003e\u003cspan address=\"10.3390/biom13010078\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTan Z, et al. Comprehensive analysis of scRNA-Seq and bulk RNA-Seq reveals dynamic changes in the tumor immune microenvironment of bladder cancer and establishes a prognostic model. J Transl Med. 2023;21:223. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12967-023-04056-z\u003c/span\u003e\u003cspan address=\"10.1186/s12967-023-04056-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiang L, et al. Integration of scRNA-Seq and Bulk RNA-Seq to Analyse the Heterogeneity of Ovarian Cancer Immune Cells and Establish a Molecular Risk Model. Front Oncol. 2021;11:711020. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fonc.2021.711020\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2021.711020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChi H, et al. T-cell exhaustion signatures characterize the immune landscape and predict HCC prognosis via integrating single-cell RNA-seq and bulk RNA-sequencing. Front Immunol. 2023;14:1137025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2023.1137025\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2023.1137025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaul D, et al. A new gene set identifies senescent cells and predicts senescence-associated pathways across tissues. Nat Commun. 2022;13:4827. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-022-32552-1\u003c/span\u003e\u003cspan address=\"10.1038/s41467-022-32552-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eH\u0026auml;nzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1471-2105-14-7\u003c/span\u003e\u003cspan address=\"10.1186/1471-2105-14-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLangfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1471-2105-9-559\u003c/span\u003e\u003cspan address=\"10.1186/1471-2105-9-559\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRitchie ME, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkv007\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkv007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGustavsson EK, Zhang D, Reynolds RH, Garcia-Ruiz S, Ryten M. ggtranscript: an R package for the visualization and interpretation of transcript isoforms using ggplot2. \u003cem\u003eBioinformatics\u003c/em\u003e 38, 3844\u0026ndash;3846. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bioinformatics/btac409\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btac409\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGu Z, H\u0026uuml;bschmann D. Make Interactive Complex Heatmaps in R. Bioinformatics. 2022;38:1460\u0026ndash;2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bioinformatics/btab806\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btab806\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZheng Y, et al. Ferroptosis and Autophagy-Related Genes in the Pathogenesis of Ischemic Cardiomyopathy. Front Cardiovasc Med. 2022;9:906753. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcvm.2022.906753\u003c/span\u003e\u003cspan address=\"10.3389/fcvm.2022.906753\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics. 2012;16:284\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/omi.2011.0118\u003c/span\u003e\u003cspan address=\"10.1089/omi.2011.0118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu P, Xu H, Shi Y, Deng L, Chen X. Potential Molecular Mechanisms of Plantain in the Treatment of Gout and Hyperuricemia Based on Network Pharmacology. \u003cem\u003eEvid Based Complement Alternat Med\u003c/em\u003e 2020, 3023127. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1155/2020/3023127\u003c/span\u003e\u003cspan address=\"10.1155/2020/3023127\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSachs MC, plotROC. A Tool for Plotting ROC Curves. J Stat Softw. 2017;79. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18637/jss.v079.c02\u003c/span\u003e\u003cspan address=\"10.18637/jss.v079.c02\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu C, He Y, Luo J. Application of Chest CT Imaging Feature Model in Distinguishing Squamous Cell Carcinoma and Adenocarcinoma of the Lung. Cancer Manag Res. 2024;16:547\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2147/cmar.S462951\u003c/span\u003e\u003cspan address=\"10.2147/cmar.S462951\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou J, et al. Identification of aging-related biomarkers and immune infiltration characteristics in osteoarthritis based on bioinformatics analysis and machine learning. Front Immunol. 2023;14:1168780. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2023.1168780\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2023.1168780\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCorrection to Lancet Psych. 2022; 9: 938. \u003cem\u003eLancet Psychiatry\u003c/em\u003e 10, e10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s2215-0366(22)00411-4\u003c/span\u003e\u003cspan address=\"10.1016/s2215-0366(22)00411-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang L, et al. Cuproptosis related genes associated with Jab1 shapes tumor microenvironment and pharmacological profile in nasopharyngeal carcinoma. Front Immunol. 2022;13:989286. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2022.989286\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2022.989286\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSatija R, Farrell JA, Gennert D, Schier AF, Regev A. Spatial reconstruction of single-cell gene expression data. Nat Biotechnol. 2015;33:495\u0026ndash;502. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nbt.3192\u003c/span\u003e\u003cspan address=\"10.1038/nbt.3192\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Y, et al. Single-Cell Transcriptome Analysis Reveals Dynamic Cell Populations and Differential Gene Expression Patterns in Control and Aneurysmal Human Aortic Tissue. Circulation. 2020;142:1374\u0026ndash;88. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/circulationaha.120.046528\u003c/span\u003e\u003cspan address=\"10.1161/circulationaha.120.046528\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTrapnell C, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014;32:381\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nbt.2859\u003c/span\u003e\u003cspan address=\"10.1038/nbt.2859\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAzadian S, et al. Reconstructing the cell-cell interaction network among mouse immune cells. Biotechnol Bioeng. 2023;120:2756\u0026ndash;64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/bit.28431\u003c/span\u003e\u003cspan address=\"10.1002/bit.28431\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArnaoutakis GJ, et al. Misdiagnosis of Thoracic Aortic Emergencies Occurs Frequently Among Transfers to Aortic Referral Centers: An Analysis of Over 3700 Patients. J Am Heart Assoc. 2022;11:e025026. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/jaha.121.025026\u003c/span\u003e\u003cspan address=\"10.1161/jaha.121.025026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eElefteriades JA, Ziganshin BA. Familial Legacy of Thoracic Aortic Aneurysm. Circulation. 2020;142:929\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/circulationaha.120.049124\u003c/span\u003e\u003cspan address=\"10.1161/circulationaha.120.049124\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTyrrell DJ, et al. Aging Alters the Aortic Proteome in Health and Thoracic Aortic Aneurysm. Arterioscler Thromb Vasc Biol. 2022;42:1060\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/atvbaha.122.317643\u003c/span\u003e\u003cspan address=\"10.1161/atvbaha.122.317643\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWatson AMD, Chen YC, Peter K. Vascular Aging and Vascular Disease Have Much in Common! Arterioscler Thromb Vasc Biol. 2022;42:1077\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/atvbaha.122.317892\u003c/span\u003e\u003cspan address=\"10.1161/atvbaha.122.317892\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWan H, et al. Bioinformatics analysis of aging-related genes in thoracic aortic aneurysm and dissection. Front Cardiovasc Med. 2023;10:1089312. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcvm.2023.1089312\u003c/span\u003e\u003cspan address=\"10.3389/fcvm.2023.1089312\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVeteleanu A, et al. Alzheimer's disease-associated complement gene variants influence plasma complement protein levels. J Neuroinflammation. 2023;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12974-023-02850-6\u003c/span\u003e\u003cspan address=\"10.1186/s12974-023-02850-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLorenzini PA, et al. Population-specific positive selection on low CR1 expression in malaria-endemic regions. PLoS ONE. 2023;18:e0280282. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0280282\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0280282\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJeong JM, et al. CX 3 CR1\u0026thinsp;+\u0026thinsp;macrophages interact with HSCs to promote HCC through CD8\u0026thinsp;+\u0026thinsp;T-cell suppression. Hepatology. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/hep.0000000000001021\u003c/span\u003e\u003cspan address=\"10.1097/hep.0000000000001021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJeyalan V, Austin D, Loh SX, Wangsaputra VK, Spyridopoulos I. Fractalkine/CX(3)CR1 in Dilated Cardiomyopathy: A Potential Future Target for Immunomodulatory Therapy? \u003cem\u003eCells\u003c/em\u003e 12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cells12192377\u003c/span\u003e\u003cspan address=\"10.3390/cells12192377\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Y, et al. Genomics of Alzheimer's disease implicates the innate and adaptive immune systems. Cell Mol Life Sci. 2021;78:7397\u0026ndash;426. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00018-021-03986-5\u003c/span\u003e\u003cspan address=\"10.1007/s00018-021-03986-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang YH, et al. Identification of highly reliable risk genes for Alzheimer's disease through joint-tissue integrative analysis. Front Aging Neurosci. 2023;15:1183119. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnagi.2023.1183119\u003c/span\u003e\u003cspan address=\"10.3389/fnagi.2023.1183119\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDull K, Fazekas F, T\u0026ouml;rőcsik D, Factor. XIII-A in Diseases: Role Beyond Blood Coagulation. Int J Mol Sci. 2021;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms22031459\u003c/span\u003e\u003cspan address=\"10.3390/ijms22031459\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eErcan H, et al. Alterations of the Platelet Proteome in Lung Cancer: Accelerated F13A1 and ER Processing as New Actors in Hypercoagulability. Cancers (Basel). 2021;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cancers13092260\u003c/span\u003e\u003cspan address=\"10.3390/cancers13092260\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaartinen MT, et al. F13A1 transglutaminase expression in human adipose tissue increases in acquired excess weight and associates with inflammatory status of adipocytes. Int J Obes (Lond). 2021;45:577\u0026ndash;87. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41366-020-00722-0\u003c/span\u003e\u003cspan address=\"10.1038/s41366-020-00722-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eComerford I, McColl SR. Atypical chemokine receptors in the immune system. Nat Rev Immunol. 2024;24:753\u0026ndash;69. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41577-024-01025-5\u003c/span\u003e\u003cspan address=\"10.1038/s41577-024-01025-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDu Y, et al. Chemokines form nanoparticles with DNA and can superinduce TLR-driven immune inflammation. J Exp Med. 2022;219. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1084/jem.20212142\u003c/span\u003e\u003cspan address=\"10.1084/jem.20212142\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHan X, et al. Potential Regulators of the Senescence-Associated Secretory Phenotype During Senescence and Aging. J Gerontol Biol Sci Med Sci. 2022;77:2207\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/gerona/glac097\u003c/span\u003e\u003cspan address=\"10.1093/gerona/glac097\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOhtani N. Deciphering the mechanism for induction of senescence-associated secretory phenotype (SASP) and its role in ageing and cancer development. J Biochem. 2019;166:289\u0026ndash;95. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/jb/mvz055\u003c/span\u003e\u003cspan address=\"10.1093/jb/mvz055\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu J, Meganathan I, MacAruthur R, Kassiri Z. Inflammation in Abdominal Aortic Aneurysm: Cause or Comorbidity? Can J Cardiol. 2024;40:2378\u0026ndash;91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cjca.2024.08.274\u003c/span\u003e\u003cspan address=\"10.1016/j.cjca.2024.08.274\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao H, Xu Y, Cui J. CXCR2 (rs3890158 and rs2230054) and CXCL4 (rs352008 and rs1801572) gene polymorphisms in patients with thoracic aortic aneurysm. Biotechnol Genet Eng Rev. 2024;40:4300\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/02648725.2023.2210011\u003c/span\u003e\u003cspan address=\"10.1080/02648725.2023.2210011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao P, Zhen H, Zhao H, Huang Y, Cao B. Identification of hub genes and potential molecular mechanisms related to radiotherapy sensitivity in rectal cancer based on multiple datasets. J Transl Med. 2023;21:176. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12967-023-04029-2\u003c/span\u003e\u003cspan address=\"10.1186/s12967-023-04029-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu F, Huang Y, Liu F, Wang H. Identification of immune-related genes in diagnosing atherosclerosis with rheumatoid arthritis through bioinformatics analysis and machine learning. Front Immunol. 2023;14:1126647. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2023.1126647\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2023.1126647\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang B, Xie L, Liu J, Liu A, He M. Construction and validation of a cuproptosis-related prognostic model for glioblastoma. Front Immunol. 2023;14:1082974. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2023.1082974\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2023.1082974\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang J, et al. NET-related gene signature for predicting AML prognosis. Sci Rep. 2024;14:9115. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-024-59464-y\u003c/span\u003e\u003cspan address=\"10.1038/s41598-024-59464-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Q, et al. Integrating Bulk Transcriptome and Single-Cell RNA Sequencing Data Reveals the Landscape of the Immune Microenvironment in Thoracic Aortic Aneurysms. Front Cardiovasc Med. 2022;9:846421. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcvm.2022.846421\u003c/span\u003e\u003cspan address=\"10.3389/fcvm.2022.846421\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGao H, et al. Deciphering the circulating immunological landscape of thoracic aortic aneurysm: Insights from a two-sample Mendelian randomization study. Heliyon. 2024;10:e31198. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.heliyon.2024.e31198\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2024.e31198\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSong W, Tu G, Qin L, Wei L, Chen J. Macrophage in Sporadic Thoracic Aortic Aneurysm and Dissection: Potential Therapeutic and Preventing Target. Rev Cardiovasc Med. 2023;24:340. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.31083/j.rcm2412340\u003c/span\u003e\u003cspan address=\"10.31083/j.rcm2412340\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang X, et al. Dexamethasone reduces the formation of thoracic aortic aneurysm and dissection in a murine model. Exp Cell Res. 2021;405:112703. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.yexcr.2021.112703\u003c/span\u003e\u003cspan address=\"10.1016/j.yexcr.2021.112703\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu C, et al. Interleukin-3 stimulates matrix metalloproteinase 12 production from macrophages promoting thoracic aortic aneurysm/dissection. Clin Sci (Lond). 2018;132:655\u0026ndash;68. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1042/cs20171529\u003c/span\u003e\u003cspan address=\"10.1042/cs20171529\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeng J, et al. Murine model of elastase-induced proximal thoracic aortic aneurysm through a midline incision in the anterior neck. Front Cardiovasc Med. 2023;10:953514. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcvm.2023.953514\u003c/span\u003e\u003cspan address=\"10.3389/fcvm.2023.953514\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDesi N, et al. Global analysis of RNA-binding proteins identifies a positive feedback loop between LARP1 and MYC that promotes tumorigenesis. Cell Mol Life Sci. 2022;79:147. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00018-021-04093-1\u003c/span\u003e\u003cspan address=\"10.1007/s00018-021-04093-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Y, et al. c-Myc inhibits LAPTM5 expression in B-cell lymphomas. Ann Hematol. 2023;102:3499\u0026ndash;513. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00277-023-05434-9\u003c/span\u003e\u003cspan address=\"10.1007/s00277-023-05434-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOeller M, et al. Transcription Factors STAT3 and MYC Are Key Players of Human Platelet Lysate-Induced Cell Proliferation. Int J Mol Sci. 2022;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms232415782\u003c/span\u003e\u003cspan address=\"10.3390/ijms232415782\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang J, et al. Transcription factor AP-2α activates RNA polymerase III-directed transcription and tumor cell proliferation by controlling expression of c-MYC and p53. J Biol Chem. 2023;299:102945. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jbc.2023.102945\u003c/span\u003e\u003cspan address=\"10.1016/j.jbc.2023.102945\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi J, et al. Circ-LAMP2 regulates aortic smooth muscle cell proliferation and apoptosis in thoracic aortic aneurysm via modulation of autophagy and NF-κB pathway. Hum Pathol. 2021;WITHDRAWN. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.humpath.2021.09.002\u003c/span\u003e\u003cspan address=\"10.1016/j.humpath.2021.09.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Thoracic aortic aneurysm, Senescence-associated secretory phenotype, Key genes, Diagnosis, Aging","lastPublishedDoi":"10.21203/rs.3.rs-7912284/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7912284/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThoracic aortic aneurysm (TAA) eventually causes aortic intima rupture, and the senescence-associated secretory phenotype (SASP) has been found to promote TAA. Thus, identifying TAA early via SASP-related genes (SASP-RGs) is highly significant.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eDifferential analysis was performed on TAA datasets from GEO; SASP-RGs were expanded via weighted gene co-expression network analysis. Candidate key genes were obtained by combining these two analyses with protein-protein interaction, then identified via least absolute shrinkage and selection operator and expression verification. A nomogram was built using key genes, and immune cell infiltration in TAA was examined. Meanwhile, pathway enrichment and regulatory networks of key genes were explored. Key TAA-related cells in single-cell datasets and key gene expression in them were determined, with key gene expression validated in clinical samples.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 36 candidates, CR1 and F13A1 were key genes; the nomogram had good diagnostic value. In TAA, infiltration of immune cells like macrophages and natural killer cells increased, showing positive correlation with key genes. Key genes were associated with the \"chemokine signaling pathway\" and regulated by transcription factors (e.g., SCL, SOX2). Macrophages were key cells; F13A1 expression fluctuated during macrophage development. Key gene expression was verified by PCR in clinical samples.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eSASP-RGs play important roles in TAA and have diagnostic value, providing a basis for TAA early diagnosis and prevention.\u003c/p\u003e","manuscriptTitle":"Integrated analysis of the diagnostic value and mechanism of action of the senescence-associated secretory phenotype in thoracic aortic aneurysm relying on bulk RNA-Seq and scRNA-Seq","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-06 17:08:39","doi":"10.21203/rs.3.rs-7912284/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"df13f356-c3fe-4418-82a8-df7164d50872","owner":[],"postedDate":"November 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-03T20:23:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-06 17:08:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7912284","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7912284","identity":"rs-7912284","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}