Construction of the circRNA-miRNA-mRNA Regulatory Network of an Abdominal Aortic Aneurysm to Explore Its Potential Pathogenesis

Background Abdominal aortic aneurysm (AAA) is a progressive cardiovascular disease, which is a permanent and localized dilatation of the abdominal aorta with potentially fatal consequence of aortic rupture. Dysregulation of circRNAs is correlated with the development of various pathological events in cardiovascular diseases. However, the function of circRNAs in abdominal aortic aneurysm (AAA) is unknown and remains to be explored. This study is aimed at determining the regulatory mechanisms of circRNAs in AAAs. This study was aimed at exploring the underlying molecular mechanisms of abdominal aortic aneurysms based on the competing endogenous RNA (ceRNA) regulatory hypothesis of circRNA, miRNA, and mRNA. Methods The expression profiles of circRNAs (GSE144431), miRNAs (GSE62179), and mRNAs (GSE7084, GSE57691, and GSE47472) in human tissue sample from the aneurysm group and normal group were obtained from the Gene Expression Omnibus database, respectively. The circRNA-miRNA-mRNA network was constructed by using Cytoscape 3.7.2 software; then, the protein-protein interaction (PPI) network was constructed by using the STRING database, and the hub genes were identified by using the cytoHubba plug-in. The circRNA-miRNA-hub gene regulatory subnetwork was formed to understand the regulatory axis of hub genes in AAAs. Results The present study identified 40 differentially expressed circRNAs (DECs) in the GSE144431, 90 differentially expressed miRNAs (DEmiRs) in the GSE62179, and 168 differentially expressed mRNAs (DEGs) with the same direction regulation (130 downregulated and 38 upregulated) in the GSE7084, GSE57691, and GSE47472 datasets identified regarding AAAs. The miRNA response elements (MREs) of three DECs were then predicted. Four overlapping miRNAs were obtained by intersecting the predicted miRNA and DEmiRs. Then, 17 overlapping mRNAs were obtained by intersecting the predicted target mRNAs of 4 miRNAs with 168 DEGs. Furthermore, the circRNA-miRNA-mRNA network was constructed through 3 circRNAs, 4 miRNAs, and 17 mRNAs, and three hub genes (SOD2, CCR7, and PGRMC1) were identified. Simultaneously, functional enrichment and pathway analysis were performed within genes in the circRNA-miRNA-mRNA network. Three of them (SOD2, CCR7, and PGRMC1) were suggested to be crucial based on functional enrichment, protein-protein interaction, and ceRNA network analysis. Furthermore, the expression of SOD2 and CCR7 may be regulated by hsa_circ_0011449/hsa_circ_0081968/hsa-let-7f-5p; the expression of PGRMC1 may be regulated by hsa_circ_0011449/hsa_circ_0081968-hsa-let-7f-5p/hsa-let-7e-5p. Conclusion In conclusion, the ceRNA interaction axis we identified may be an important target for the treatment of abdominal aortic aneurysms. This study provided further understanding of the potential pathogenesis from the perspective of the circRNA-related competitive endogenous RNA network in AAAs.


Introduction
Abdominal aortic aneurysm (AAA) is one of the cardiovascular diseases mainly distributed in Western countries and Asia, which mostly occurs in elderly men [1][2][3]. Local permanent dilation and weakening of the abdominal aorta are important characteristics of AAA [4]. Typically, degeneration of the abdominal aorta is a manifestation of a systemic process characterized by inflammation, apoptosis of smooth muscle cells, and destruction of elastin and collagen in the media and adventitia [5,6]. More specifically, it would arise three clinical symptoms, asymptomatic and ruptured signs, and the characteristics of permanent expansion [7]. Generally, the mortality rate of AAA is up to 85%, which is inseparable from the severe bleeding and unpredictability caused by its rupture, and there is no effective treatment at present [8]. Nevertheless, AAA mortality data based on 1990-2010 from the Global Burden of Disease Study showed that the mortality of AAA decreased by 12.7% [9], and 43 percent of AAAs patients who were not included in the AAA screening criteria died, of which 9% were men and 34% were women [10]. Most of AAA patients were associated with asymptomatic AAA; therefore, the majority of AAA are identified incidentally during an examination for another unrelated pathology [11]. Therefore, the method to prevent the formation of aneurysms and the slow progress of aneurysms has become a growing public concern [12,13].
Recently, several evidences have demonstrated that noncoding RNAs, such as long noncoding RNA (lncRNA), circular RNA (circRNA), and microRNA (miRNA), play an important role in the development of cardiovascular diseases [14][15][16]. CircRNA is spliced by exons at specific splicing sites to form a circular closed structure [17]. CircRNAs act as a competitive endogenous RNA (ceRNA) and compete with miRNA through its miRNA response element. miRNAs negatively regulate mRNA expression of protein-coding genes by binding to complementary sequences [18,19]. Therefore, the circRNA-miRNA-mRNA interaction may be a significant mechanism in the occurrence and development of AAAs. According to our previous study, posttranscriptional modifications play important roles in modulating the functions of RNA involved in abdominal aorta aneurysm [20]. Yue et al. revealed the role of circCBFB/miR-28-5p/GRIA4/LYPD3 in the apoptosis of vascular smooth muscle cells (VSMCs), which conduced to the generation of new thoughts in AAA management [21]. Recently, Yang et al. investigated the role of circRNA CCDC66 in the pathogenesis of AAAs and determine its targeted miRNA and mRNA. The results show that CCDC66 will affect the proliferation of VSMCs through miR-342-3p/CCDC6 through circRNA CCDC66. They suggested that the overexpression of CCDC66 would induce the penetration of AAAs through the circCCDC66/miR-342-3p/CCDC66 pathway [22]. Meanwhile, Zhao et al. demonstrated that the pathogenesis of AAAs can be regulated by the CDR1as/miR-7/CKAP4 axis and affect the proliferation and apoptosis of primary vascular smooth muscle cells [23]. In summary, the circRNA-miRNA-mRNA network might play an important role in the pathogenesis of AAA, and circRNA has different targets and functions in different tissue cells.
Recently, Tian et al. performed microarray sequencing on human and rat abdominal aortic aneurysmal tissue and determined the differentially expressed lncRNA, miRNA, and mRNA by analyzing the public dataset and constructed the ceRNA network of lncRNA [24]. However, the specific targets and mechanisms of circRNA in AAAs have not been reported. Meanwhile, there are few reports on the ceRNA regulation mechanism of circRNA-miRNA-mRNA related to AAAs and on the interaction between ncRNA.
In this study, the microarray data of circRNA, miRNA, and mRNA were downloaded from the Gene Expression Comprehensive (GEO) database, and RStudio was used to perform differential expression analysis of the microarray data to obtain differentially expressed circRNAs (DECs), differentially expressed miRNAs (DEmiRs), and differentially expressed genes (DEGs). Cytoscape 3.7.2 constructs the circRNA-miRNA-mRNA network, and the STRING (search tool for interactive gene retrieval) online analysis tool constructs the PPI network. This study is aimed at exploring novel circRNAs and their mechanisms in human tissues of AAA patients. The purpose of this study is to further screen the ceRNA axis of the key circRNA-miRNA-mRNA in AAAs with microarray data collected from public databases and bioinformatics methods. Besides, the findings of this study promoted the understanding of the molecular mechanism of AAAs, which provided a new direction for targeted therapy of AAAs. The circRNA-miRNA-mRNA regulatory subnetwork was formed to understand the regulatory axis of hub genes in AAA. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) are introduced to reveal the potential impact of key genes in the development of AAA. [25]. The flowchart is shown in Figure 1.

Materials and Methods
2.1. Data Extraction. Microarray data of AAAs were downloaded from the National Center for Biotechnology Information (NCBI) GEO database (https://www.ncbi.nlm.nih .gov/geo/), with keywords searched such as "Abdominal aortic aneurysm" (AAA) and "Noncoding RNA" (circRNA, miRNA, and mRNA). During the retrieving process, "Homo sapiens" was set as the filtering condition; as a result, a total of five datasets were included in this study, namely, GSE144431, GSE62179, GSE7084, GSE47472, and GSE57691. As for circRNA expression data (GSE144431 dataset), the type of data was ncRNA profiling by array and the data of the microarray platform was 074301 Arraystar Human ncRNA microarray V2 (platform: GPL21825). In terms of the GSE62179 dataset, the type of data was miRNA profiling by array and the data of the microarray platform was Agilent-021827 Human miRNA Microarray G4470C (Feature Number version) (platform: GPL14767). Similarly, as for the three datasets (GSE7084, GSE47472, and GSE57691), the type of data was expression profiling by array and the data of the microarray platform was HG-

Differential Expression
Analysis. GEO2R is a GEO database online analysis tool that can directly perform differential expression analysis. We directly exported the analysis results to make differential analysis. GEO2R and R package were employed to identify the differential expression of genes, and the corrected data matrix of each dataset was downloaded directly from the GEO database, with the differentially expressed gene (DEG) analysis method using the "Linear Models for Microarray Data (limma)" R package for the datasets. The annotation information of DEGs, differentially expressed circRNAs (DECs), and differentially expressed miRNAs (DEmiRs) of the three datasets were obtained with the GEO2R online software. The selection criteria for DECs included |log 2FC ðfold changeÞ | >1, and P value < 0.1 was considered to indicate a statistically significant difference. The selection criteria for DEGs included | log 2FC ðfold changeÞ | >2, and P value < 0.05 was considered to indicate a statistically significant difference. There was statistical significance for the selection of this threshold, and those genes that were up-and downregulated can also be selected for the subsequent analysis. The R software pack-age was also adopted to draw a heat map of differentially expressed circRNAs and differentially expressed mRNAs. In this study, these differentially expressed genes in the three mRNA databases were divided into the upregulated and downregulated groups. Subsequently, the Venn diagram was employed to obtain mRNAs that were regulated in the same regulated pattern, and Venny (version 2.1.0) (https:// bioinfogp.cnb.csic.es/tools/venny/), the online mapping software, was utilized to create the Venn diagram. The R software package was used for functional enrichment and pathway analysis of differential genes. Due to the different types of miRNA data and different miRNAs in AAA groups were extracted compared with normal groups with the aim of exploring DEmiRs in different aneurysm cells, and the network was constructed at the end.

Protein-Protein Interaction Network Analysis of DEGs.
STRING protein interaction prediction (version110.b) (STRING: functional protein association networks (stringdb.org)) was used to search for known protein interactions and predict protein interactions. The intersections of the differentially expressed mRNA target genes were taken in a consistent manner from three mRNA datasets and were imported into the database. After the points without interaction were hidden, the data were imported into Cytoscape (version 3.7.2) to visualize the protein-protein interaction (PPI) network.
2.6. CircRNA-miRNA-mRNA ceRNA Network Construction. In this study, the intersection of the three databases was selected as the results. The ceRNA network was constructed based on the commonly interactive differentially expressed mRNAs with differentially expressed miRNAs and differentially expressed circRNAs. The circRNA-miRNA-mRNA ceRNA network was visualized with Cytoscape (version 3.7.2) (https://cytoscape.org/).

Functional Enrichment and Pathway Analysis.
To explore the main functions and pathways of DEGs derived from differentially expressed circRNA target genes, differentially expressed mRNA target genes in a consistent manner, and target genes of the circRNA network, the Database for Annotation, Visualization and Integrated Discovery online tool (version 6.8) (DAVID: https://david.ncifcrf.gov/) was introduced for the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The gene lists were downloaded for the biological function information.

Differential Expression Analysis of Key
Genes. The plugin cytoHubba in the Cytoscape software is visualization software that obtains the dense relationship through the degree, betweenness centrality, and closeness centrality algorithms. To obtain the key genes in the PPI network, we identified the hub genes in the PPI network through cytoHubba. Key genes were obtained through the intersection of Venn diagram PPI network hub genes and ceRNA network genes. To accurately explore the differential expression of key genes in the three datasets, we used the R language software package to draw box plots of key genes.

Differential Expression Analysis.
According to the threshold criteria (P < 0:1 and |log 2FC | >1), the GSE144431 datasets contained circRNA data, and a total of 382 differentially expressed circRNAs were visualized by hierarchical clustering heat map analysis (Figure 2(a)) and volcano plot analysis (Figure 3(a)), with a view to endowing the selection results with much significance. A total of 40 samples ranking for the top 40 with the most significant differences were selected, including 20 upregulated DECs and 20 downregulated DECs ( Table 1).
The mRNA datasets of GSE7084, GSE57691, and GSE47472 contained mRNA microarray data. All differentially expressed mRNAs in GSE7084, GSE57691, and GSE47472 were selected for hierarchical clustering heat Column: an aortic sample. Differentially expressed circRNA molecules were screened under the cut-off criteria |log 2FC | >1 and the P value (P < 0:1). Differentially expressed mRNA molecules were screened under the cut-off criteria |log 2FC | >2 and the P value (P < 0:05).

Disease Markers
common DEGs were obtained at the end. Among all results, the top 20 items with the most significant P values were selected and included in Table 1. As for 215 common DEGs, it could be inferred from that Protein Phosphatase 1 Catalytic Subunit Beta (PPP1CB) and Transmembrane Protein 47 (TMEM47) were upregulated in the GSE47472 and downregulated in the GSE7084 and GSE57691. Similarly, G Protein-Coupled Receptor Class C Group 5 Member C (GPRC5C) was upregulated in the GSE57691 and downregulated in the GSE7084 and GSE47472 (Table 1). Therefore, the adverse regulated DEG results could not be regarded as the content of further analysis. In order to make the research results obtained more accurate, those DEGs with a different regulated trend in the three microarray databases were excluded. Subsequently, the DEGs with the same regulated trend in the three databases were intersected and 168 DEGs were selected. Among them, 130 DEGs were downregulated (Figure 4 Table 1. The GSE62179 dataset contained miRNA data, and there were 90 differentially expressed miRNAs in the control group compared with the AAA groups of M1 macrophages, M2 macrophages, and smooth muscle cells (SMCs) ( Table 2).

PPI Network for DEGs.
When the consistent differentially expressed mRNAs in the GSE7084, GSE47472, and GSE57691 were imported to the STRING website, it could be found that there were 131 interaction relationships for 168 DEGs, which were adopted to construct the PPI network. After the points that are not connected and interacted were hidden, the PPI network file was downloaded. In the present network, AHR could interact with MYOC, PTGS2, and CXCL8; CCR7 could interact with PRKCDBP, IL2RB, ITK, IL21R, PTGS2, and CXCL8; SOD2 could interact with CXCL8, DNM1L, ESD, MRPL33, PTGS2, and TAGLN ( Figure 5). Furthermore, cytoHubba was used to identify hub genes with high accuracy according to the network topology. After the filtering condition was set according to the maximal clique centrality (Mcc), the determined top 30 hub genes could be obtained, including IL2RB, ITK, IL21R, MYLK, PTPN22, CXCL8, PTGS2, ITGB5, CD3D, AGTR1,  Differentially expressed circRNA molecules were screened under the cut-off criteria |log 2FC | >1 and the P value (P < 0:1). Differentially expressed mRNA molecules were screened under the cut-off criteria |log 2FC | >2 and the P value (P < 0:05). 6 Disease Markers   3.3. Prediction of circRNA-miRNA Pairs. The circRNA-miRNA pairs corresponding to 40 DECs were predicted by using the Circular RNA Interactome online software (cir-cBank), which is used for online prediction of miRNA bound to circRNA by combining with circBank. The 2230 miRNA interaction pairs combined with circRNA were predicted by using circBank; meanwhile, only the last five results of miRNA bound to circRNA were shown here (Supplementary Table 2). The predicted results were intersected with the differentially expressed miRNAs of the dataset, and 22 miRNAs could be obtained.

Prediction of Target Genes of miRNAs.
Moreover, there were 957 mRNAs predicted to bind to 22 miRNAs by compilations of the miRTarBase database, TargetScan database, and miRDB database. The results obtained were intersected with 168 DEGs, and as a result, there were 17 potential mRNAs included.
To reveal the biological mechanism or functional pathway of the ceRNA network, DEGs in the ceRNA network were also subjected to GO/KEGG analysis, with the findings of functional enrichment showing that there were six GO biological process terms and two KEGG pathways, e.g., liver development (AK4 and SOD2), COP9 signalosome (NCKIPSD and COPS8), and transport vesicle (PLEKHF2 and FGFRL1) for GO analysis and peroxisome (SOD2) and FoxO signaling pathway (SOD2) for the KEGG pathway (Table 5).

Discussion
In this study on AAAs, it has been confirmed that noncoding RNAs exert significant impacts on the pathogenesis of the disease [27,28]. CircRNA is a stable noncoding RNA that has long been neglected by transcriptomics due to the lack of a 5′ cap and a 3′ polyadenylated tail. As recent studies, there are many endogenous circRNAs in mammalian cells, some of which show high abundances and evolutionary conservation. It has been initially indicated that circRNAs could mediate miRNA functions (e.g., via sponging) and control important events in transcription (e.g., RNA folding and endonuclease protection) [19]. In addition, it has been demonstrated in current evidence that circRNAs contain multiple microRNA response elements (MREs) that can bind to miRNAs, commonly called "miRNA sponges," which could relieve the targeted inhibition of downstream mRNAs by miRNAs [29][30][31]. In this study, the underlying molecular mechanisms of AAAs have been investigated based on ceRNA regulatory hypothesis of circRNAs, miRNA, and mRNA. The expression profiles of circRNAs, miRNAs, and mRNAs in human tissue samples from the aneurysm group and the normal group have been identified.
In the three RNA (circRNA, miRNA, and mRNA) microarray datasets, 17 DEGs have been identified in the circRNA-miRNA-mRNA network, and they form a ceRNA regulatory network. Hub genes, as key genes, could exert decisive impacts on the biological processes. The regulation of other genes in the relevant pathway would be frequently affected by hub genes. Therefore, it is of great significance to find the hub genes for potential targets or research hotspots. CytoHubba is effective software of the Cytoscape plug-in, which is used to identify hub genes with high accuracy according to the network topology. Within bioinformatics analysis, three hub genes (SOD2, CCR7, and PGRMC1) were identified.
Strauss et al. suggested that SOD2 may be related to the formation of abdominal aortic aneurysm through oxidation reaction [32]. However, there are few reports on the effect of SOD2 genes in AAAs. Superoxide dismutase (SOD) is a ubiquitous antioxidant enzyme that catalytically converts the superoxide radical to hydrogen peroxide (H 2 O 2 ) [33,34]. The copper/zinc-(SOD1) and manganese-(MnSOD, SOD2) requiring superoxide dismutases are important and major antioxidant enzymes located in the mitochondria and exert decisive impacts on the progression of tumors [35,36]. For instance, epigenetic silencing of SOD2 in KAS 6/1 human multiple myeloma cells can increase cell proliferation [37]. The blocking of SOD2 expression would significantly inhibit TNF-α-induced cell proliferation in A549 and H1299 cells in vitro. Therefore, TNF-α-mediated lung inflammation can upregulate SOD2 expression in lung adenocarcinoma, and macrophages contribute to SOD2 upregulation by secreting TNF-α [38]. In cardiovascular disease, SOD2 stands at the forefront against mitochondrial ROS in vascular smooth muscle cells (VSMCs) via its preferential 13 Disease Markers localization to the mitochondria, which conduces to the potential modification of the probability of vascular calcification [39] initiation or progression. Moreover, SOD2 augmentation may be a promising therapeutic strategy for the prevention of lesion formation in proliferative vascular diseases such as restenosis [40]. Madamanchi et al. suggested that SOD2 could regulate SMC quiescence by suppressing divergent mitogenic signaling pathways, and the dysregulation of these enzymes under pathophysiological conditions may induce SMC hyperplasia and hypertrophy [41]. The expression level of SOD2 will also have an influence on the migration of VSMCs and the formation of intima. Mechanically, Qu et al. suggested that peroxisome proliferatoractivated receptor-gamma coactivator-1alpha (PGC-1alpha) might upregulate the expression of the mitochondrial antioxidant enzyme SOD2, which would inhibit VSMC migration and neointimal formation after vascular injury in rats [42]. On the contrary, it has been reported in other studies that the loss of heterozygous SOD2 would aggravate chronic intermittent hypoxia-induced lung inflammation and  IL2RB ITK  IL21R MYLK  PTPN22 CXCL8  PTGS2  ITGB5 CD3D   AGTR1  CLEC12A SORBS1  STOM CALD1  PDGFA RAB37  MGAM   FGFRL1  PLEKHF2 AHR  AK4 CCNG1  COPS8  KIAA0930   NCKIPSD  KLF9 MIER1  PAG1 TMEM30B  NFIC  RIMS3   CCR7  14 Disease Markers vascular remodeling through the mtROS-NLRP3 signaling pathway [43]. Finally, the increase in SOD2 expression in genotyped primary human umbilical vein endothelial cells (ECs) induced by fluid shear stress (FSS) would effectively stabilize their antiatherosclerotic phenotype [44]. Besides, it has also been found in this study that SOD2 may exert its impacts through hsa_circ_0011449/hsa_circ_0081968/hsalet-7f-5p. To sum up, it has been indicated in those studies that the dysregulation of SOD2 can cause related arterial diseases or changes in the endothelial cell or smooth muscle cell function and phenotype. Therefore, the research on SOD2 and its functions may become a popular trend for the treatment of AAAs. Considering the important role that SOD2 may play in AAAs, some papers have been found to explain the effects of SOD2 on arteries. At the same time, it is also the most significant among the differentially expressed genes in AAAs. hsa-let-7f-5p can form a regulatory network with many differentially expressed genes, for example, interaction with AHR (Aryl Hydrocarbon Receptor). Besides, Fibroblast Growth Factor Receptor-Like 1 (FGFRL1) and Pleckstrin Homology and FYVE Domain Containing 2 (PLEKHF2) may affect the transportation of intracellular substances to promote aneurysm formation, through the hsa_circ_0005073/hsa_circ_ 0081968/hsa-miR-107 regulated axis. There are relatively few studies on PLEKHF2 and FGFRL1 to indicate that they may exert significant impacts on the occurrence and development of AAAs. E. Shamsara and J. Shamsara have confirmed that the amplification of the PLEKHF2 gene is related to the decreased survival of patients with prostate cancer [45]. FGFRL1 would be significantly upregulated in patients with ovarian cancer, and high FGFRL1 expression is associated with a poor prognosis. The loss of FGFRL1 function significantly affects the proliferation, apoptosis, and migration of ovarian cancer cells in vitro and tumor growth in vivo [46]. For hsa-miR-107, Wang et al. have found that Integral Membrane Protein 2C (ITM2C) would be underexpressed and miR-107-5p overexpressed in acute aortic dissection tissues [47]. Meanwhile, the overexpression of miR-107-5p would promote cell proliferation and inhibit cell apoptosis in rat aortic smooth muscle cells (RASMCs).
CCR7 is a proinflammatory cytokine and is found in human atherosclerotic plaques [48]. It has been found that the expression of CCR7 is dramatically downregulated in human carotid atherosclerotic plaques [49]. Wan et al. have assessed the protein expression of CCR7 in AAAs through immunohistochemistry, with the findings showing that CCR7 is obviously upregulated in AAA compared with the control healthy aorta [50]. Recently, the homeostatic chemokines, CCL19 and CCL21 and their receptor CCR7, have been linked to atherogenesis. The current findings suggest that CCR7 is immunohistochemically localized to macrophages and vascular SMCs via carotid atherosclerosis and highlight that although CLL19 and CCL21 are signaling through CCR7, they may have different effects on macrophages and SMC [51]. In this study, it has also been found that the hsa_circ_0081968/hsa-let-7f-5p/CCR7 regulatory axis may be an important target for the treatment of AAAs. Cai et al. have also revealed that a lncRNA/miRNA/mRNA interaction network may be involved in the pathogenesis of pulmonary arterial hypertension (PAH) and suggested hsalet-7e-5p and CCR7 can be regarded as potential biomarkers for PAH [52].
However, there are some limitations in this study. Firstly, although it has been shown that SOD2 can cause arterial-related diseases, there is no research to prove the relationship between abdominal aortic aneurysm and SOD2. Secondly, the way of action has been predicted based on the measured RNA network, which, however, has not been confirmed (dual-luciferase reporter gene analysis, gene overexpression, or gene knockout). Although several related genes have been screened out in this study for the first time, further in vitro clinical research and in vivo experiments shall be carried out to confirm its expression and functional mechanism in terms of AAAs.
At present, there are relatively few articles on the circRNA-miRNA-mRNA network regulating AAA, and most of them are ceRNA regulatory networks based on lncRNA. Based on Gene Expression Omnibus, we detected six dysregulations of circRNA-miRNA-mRNA axes in the AAA condition compared with healthy aortic condition. The novelty of this study is that the circRNA-miRNA-mRNA network has been constructed for the first time through the GEO database. However, given that these findings are only based on bioinformatics models, it is necessary to conduct a thorough study to verify the possible effects of these 6 axes in AAAs. Further studies are needed to elucidate the biological function of these circRNAs in the pathogenesis of AAA.

Data Availability
The GEO dataset data used to support the findings of this study are available from the corresponding author upon request.