The role of integrin-related genes in atherosclerosis complicated by abdominal aortic aneurysm

The role of integrin-related genes in atherosclerosis complicated by abdominal aortic aneurysm | 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 The role of integrin-related genes in atherosclerosis complicated by abdominal aortic aneurysm Likang Ma, Keyuan Chen, Lele Tang, Liangwan Chen, Zhihuang Qiu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3984086/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 Increasingly, the shared risk factors and pathological processes of atherosclerosis and abdominal aortic aneurysm (AAA) are being recognized. The aim of our study was to identify the hub genes involved in the pathogenesis of atherosclerosis and AAA. Methods The analysis was based on two gene expression profiles for atherosclerosis (GSE28829) and AAA (GSE7084), downloaded from the Gene Expression Omnibus (GEO) database. Common differential genes were identified and an enrichment analysis of differential genes was conducted, with construction of protein-protein interaction networks, and identification of common hub genes and predicted transcription factors. Results The analysis identified 133 differentially expressed genes (116 upregulated and 17 downregulated), with the enrichment analysis identifying a potential important role of integrins and chemokines in the common immune and inflammatory responses of atherosclerosis and AAA. Regulation of the complement and coagulation cascades and regulation of the actin cytoskeleton were associated with both diseases, with 10 important hub genes identified: TYROBP, PTPRC, ITGB2, ITGAM, PLEK, CTSS, LY86, ITGAX, CCL4, and FCER1G. Conclusions Findings identified a common pathogenetic pathway between atherosclerosis and AAA, with integrin-related genes playing a significant role. The common pathways and hub genes identified provide new insights into the shared mechanisms of these two diseases and can contribute to identifying new therapeutic targets and predicting the therapeutic effect of biological agents. atherosclerosis abdominal aortic aneurysm bioinformatics microarray hub genes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Background Abdominal aortic aneurysms (AAA) are considered to have occurred if the local diameter of the abdominal aorta exceeds 50% of the normal diameter and the dilation is irreversible [ 1 ]. Atherosclerosis, characterized by abnormal vascular intima formation due to hyperlipidemia and lipid oxidation, may play a role in AAA formation. Fatty deposits of atherosclerotic plaques in the intima of arterial walls causes a proliferation of fibrous tissue and of the surrounding smooth muscles, leading to arterial stiffening [ 2 ]. Moreover, both atherosclerosis and AAA share common risk factors, such as family history, male sex, advanced age, and smoking [ 3 ], and the pathological processes of chronic inflammation, extracellular matrix degradation, vascular smooth muscle apoptosis, and thrombosis are involved in both AAA and atherosclerotic plaque formation [ 4 , 5 ]. Therefore, atherosclerosis may potentially promote AAA [ 6 , 7 ] by causing a mechanical weakening of the aortic wall, loss of elasticity, and degenerative ischemic changes in the adventitial layer [ 8 ]. However, the exact mechanism linking the pathogenesis of atherosclerosis and to AAA formation remains unclear. Identifying the common transcriptional signatures of atherosclerosis and AAA may clarify the shared pathogenetic pathway. Accordingly, our aim in this study was to identify the hub genes involved in the pathogenesis of atherosclerosis complicated by AAA. 2. Methods 2.1.Data Source The Gene Expression Omnibus (GEO) database ( http://www.ncbi.nlm.nih.gov/geo ) was used [ 9 ], with “atherosclerosis” and “abdominal aortic aneurysms or AAA” used as keywords to search the dataset for related genes. GEO is a public database containing a large number of high-throughput sequencing and microarray datasets, submitted by research institutes worldwide. For our study, we used the following two microarray datasets, GSE28829[ 10 ] and GSE7084[ 11 ]. The GSE28829 dataset, created on the GPL570 (Affymetrix Human Genome U133 Plus 2.0 Array) platform, contains 16 advanced atherosclerotic plaque samples (thin or thick fibrous cap atheroma) (AA) and 13 early atherosclerotic plaque samples ( intimal thickening and intimal xanthoma) as a control (CA), obtained from the human carotid artery. From the GSE7084 dataset, we chose the GPL2507 (Sentrix Human-6 Expression BeadChip) for a larger sample size, which contains seven abdominal aortic aneurysms (AAA) samples and eight control abdominal aorta samples (CO) obtained from autopsy. 2.2.Identification of DEGs Comparison of the gene expression profile between the disease and control groups was performed using the GEO query R package (GEO2R; www.ncbi.nlm.nih.gov/geo/ge2r ), to identify the differentially expressed genes (DEGs), and the Limma R package, to calculate multiple differential expressions [ 12 ]. DEGs were identified by an adjusted P < 0.05 and an absolute fold-change (|logFC|) ≥ 1. Probes that did not contain a corresponding gene were removed. If a gene corresponded to multiple probes, the one with the largest difference in expression was selected. The common set of DEGs between atherosclerosis and AAA was identified using a Venn diagram tool ( http://bioinformatics.psb.ugent.be/webtools/Venn/ ). 2.3.Enrichment Analyses of DEGs Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis results for DEGs were obtained using the Database for Annotation, Visualization and Integrated Discovery ( https://david.ncifcrf.gov/tools.jsp ), which allowed us to investigate the biological functions and signaling pathways involved in a given gene set [ 13 ]. GO includes three independent categories, namely biological processes (BP), molecular functions (MF), and cellular components (CC). Terms with a P < 0.05 were considered significantly enriched. 2.4.PPI Network Construction and Module Analysis The relationship between proteins of interest was obtained using the Search Tool for the Retrieval of Interacting Genes (STRING 11.5; https://cn.string-db.org/ ), which includes both direct binding relationships and coexisting upstream and downstream regulatory pathways [ 14 ]. This information can be used to construct a protein-protein interaction (PPI) network with complex regulatory relationships; interactions having a combined score > 0.4 were considered significant. The PPI network was visualized using Cytoscape (Version 3.9.1 https://cytoscape.org/ ) [ 15 ]. The Cytoscape plug-in molecular complex detection technology (MCODE) was used to analyze the key functional modules, applying the following selection criteria: K-core = 2; degree cutoff = 2; maximum depth = 100; and node score cutoff = 0.2. The GO- and KEGG-based analyses of involved modular genes were then performed using DAVID. 2.5.Selection and Analysis of Hub Genes Hub genes were identified using the CytoHubba plug-in (Cytoscape, version 3.9.1), with the following eight common algorithms used to then evaluate and select hub genes: MCC, MNC, EPC, degree, closeness, radiality, bottleneck, and eccentricity. A co-expression network of these hub genes was then constructed using GeneMANIA ( http://genemania.org/ ), a reliable tool for identifying internal associations within gene sets [ 16 ]. 2.6.Validation of Hub Genes Expression in Other Data Sets The mRNA expression of the hub genes was validated using two additional datasets, GSE100927 [ 17 ] and GSE98278 [ 18 ]. Dataset GSE100927 includes 69 human samples of AA and 35 control artery samples (CA), while GSE98278 includes 31 human AAA samples and 17 peripheral normal aortic samples (CO) that collected during rupture repair of abdominal aortic aneurysm for comparison. Comparison of the two datasets was performed using Student’s t-test, with a P < 0.05 considered significant. 2.7.Prediction and Verification of Transcription Factors (TFs) To more accurately predict the transcription factors (TFs) that regulate the hub genes, the following two databases were used: Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRRUST) ( https://www.grnpedia.org/trrust/ ) and ChIP-X Enrichment Analysis 3 (ChEA3) ( https://maayanlab.cloud/chea3/ ). For the ChEA3 database, the ENCODE library was selected, with the significance level set at P < 0.05. The TRRUST database, used to predict transcriptional regulatory networks, contains the target genes corresponding to TFs and the regulatory relationships between TFs. The TRRUST database currently includes two species, humans and mice, with 8,444 and 6,552 TFs that target regulatory relationships of 800 human TFs and 828 mouse TFs, respectively [ 19 ]. The ChEA3 database is a TF enrichment analysis tool that contains a collection of gene set libraries generated from multiple sources, including TF-gene co-expression from RNA-seq studies, TF-target associations from ChIP-seq experiments, TF-gene co-occurrence computed from crowd-submitted gene lists, and ranks TFs associated with user-submitted gene sets [ 20 ]. In our study, common TFs predicted by both databases were selected. Finally, expression of TFs in datasets GSE28829 and GSE7084 was verified using Student’s t-test. 3. Results 3.1.Identification of DEGs The study process flow chart is shown in Fig. 1 . The data distribution, after standardization of the microarray data, is shown in Fig. 2 A. The volcano plots of DEGs (270 in GSE28829 and 1168 in GSE7084), obtained by difference analysis, are shown in Fig. 2 B. Using the Venn diagram, we identified 134 overlapping DEGs (Fig. 2 C). Verification of these DEGs in the two datasets identified 133 DEGs with the same expression trend, including 116 upregulated and 17 downregulated genes ( Supplementary Table 1 ). 3.2.Analysis of the Functional Characteristics of Common DEGs GO and KEGG pathway analyses were performed on the 133 DEGs to determine the associated biological process (BP), cell component (CC), and molecular function (MF). Results of the GO analysis showed that DEGs were mainly enriched in the immune (P = 1.34E-16) and inflammatory (P = 3.19E-14) responses, antigen processing and presentation of exogenous peptide antigen via MHC class II (P = 1.48E-11), and the innate immune response (P = 2.25E-09) (Fig. 3 B). In the KEGG pathway analysis, the following three significantly enriched pathways were identified: complement and coagulation cascades (P = 2.28E-06), chemokine signaling (P = 3.83E-06), and antigen processing and presentation (P = 1.40E-05) (Fig. 3 C). These results illustrate the important roles of antigen processing and in immune inflammatory responses in both AAA and atherosclerosis. 3.3.PPI Network Construction and Module Analysis The PPI network of common DEGs, constructed in Cytoscape combining STRING scores > 0.4, contained 116 nodes and 1197 edges (Fig. 3 A). Four closely related gene modules, including 43 DEGs and 429 edges, were identified using the MCODE plug-in of Cytoscape (Fig. 4 A). The GO analysis revealed that these genes were related to inflammatory, innate immune, and immune responses (Fig. 4 B). The KEGG pathway analysis showed that these genes were mainly involved in neutrophil extracellular trap formation, Fc gamma R-mediated phagocytosis, viral protein interaction with cytokines and the cytokine receptor, Toll-like receptor signaling, and the Rap1 signaling pathway (Fig. 4 C). 3.4.Selection and Analysis of Hub Genes The top 25 hub genes were obtained using eight plug-in cytoHubba algorithms (Table 1 ). Next, the 10 common hub genes were obtained using Upset diagrams: TYROBP, PTPRC, ITGB2, ITGAM, PLEK, CTSS, LY86, ITGAX, CCL4, and FCER1G (Fig. 5 A; Table 2 ). The co-expression network and related functions of these hub genes, analyzed using the GeneMANIA database, revealed a complex PPI network with a co-expression of 58.88%, physical interactions of 29.53%, prediction of 6.00%, co-localization of 5.08%, and shared protein domains of 0.52% (Fig. 5 B). These hub genes were closely involved in integrin-mediated signaling pathways, positive regulation of superoxide anion generation, cell adhesion mediated by integrin, neutrophil chemotaxis, and cell-matrix adhesion (Fig. 6 A). These findings highlight the important role of integrins in atherosclerosis and AAA. In the KEGG pathway, complement and coagulation cascades, natural killer cell-mediated cytotoxicity, cell adhesion molecules, and regulation of the actin cytoskeleton were identified (Fig. 6 B). This suggests that changes in ITGAM, ITGB2, and ITGAX may affect the actin cytoskeleton and promote formation of arterial aneurysms. Table 1 The top 25 hub genes rank in cytoHubba. MCC MNC EPC Degree Closeness Radiality BottleNeck EcCentricity TYROBP TYROBP TYROBP TYROBP TYROBP TYROBP PTPRC APOE PTPRC PTPRC PTPRC PTPRC PTPRC PTPRC TYROBP ITGAM ITGB2 ITGB2 ITGB2 ITGB2 ITGB2 ITGAM APOE CTSS ITGAM ITGAM CD86 ITGAM ITGAM ITGB2 ITGB2 PTPRC PLEK PLEK LCP2 PLEK PLEK PLEK ITGAM TYROBP CD86 CD86 PLEK CD86 CD86 CTSS HLA-DRA ITGB2 LCP2 LCP2 ITGAM LCP2 LCP2 LCP2 MMP12 HLA-DRA CTSS CTSS FCGR2B CTSS CTSS TLR2 NCF2 MMP12 TLR2 TLR2 CSF1R TLR2 TLR2 CD86 ITGAX NCF2 CSF1R CSF1R CTSS CSF1R CSF1R CSF1R CD48 ITGAX LAPTM5 LAPTM5 LAPTM5 LAPTM5 LAPTM5 LAPTM5 LCP1 CD48 C1QA C1QA IL10RA C1QA C1QA C1QA CORO1A LCP1 CD53 IL10RA TLR2 IL10RA CD53 CD53 PLEK CORO1A IL10RA CD53 HCK CD53 IL10RA AIF1 CTSS PLEK LY86 LY86 CD53 LY86 LY86 LY86 CXCR4 CXCR4 FCGR2B FCGR2B LY86 FCGR2B FCGR2B ITGAX C1QC C1QC HCK HCK AIF1 HCK HCK IL10RA SLAMF8 SCD ITGAX ITGAX C1QA ITGAX ITGAX FCGR2B SCD LY86 CCL4 CCL4 CCL4 CCL4 CCL4 HCK AMPD3 C5AR1 FCER1G FCER1G FCER1G FCER1G FCER1G CCL4 LY86 FCER1G IRF8 IRF8 IRF8 IRF8 IRF8 IRF8 C5AR1 CCL4 AIF1 CCR1 ITGAX CCR1 AIF1 C1QC FCER1G IGSF6 CCR1 CD48 C3AR1 CD48 CCR1 FCER1G CCL4 CD52 CD48 AIF1 CD74 AIF1 CD48 CCR1 IGSF6 CD86 C3AR1 C3AR1 CCR1 C3AR1 C1QC CD48 CD52 BCL2A1 Table 2 The details of the hub genes. NO. Gene Full name Function 1 TYROBP TYRO protein tyrosine kinase binding protein This gene encodes a transmembrane signaling polypeptide which contains an immunoreceptor tyrosine-based activation motif (ITAM) in its cytoplasmic domain. The encoded protein may bind zeta-chain (TCR) associated protein kinase 70kDa (ZAP-70) and spleen tyrosine kinase (SYK) and play a role in signal transduction, bone modeling, brain myelination, and inflammation. 2 PTPRC protein tyrosine phosphatase receptor type C The protein encoded by this gene is a member of the protein tyrosine phosphatase (PTP) family.This PTP has been shown to be an essential regulator of T- and B-cell antigen receptor signaling.This PTP also suppresses JAK kinases. 3 ITGB2 integrin subunit beta 2 This gene encodes an integrin beta chain, which combines with multiple different alpha chains to form different integrin heterodimers. Integrins are integral cell-surface proteins that participate in cell adhesion as well as cell-surface mediated signalling. The encoded protein plays an important role in immune response and defects in this gene cause leukocyte adhesion deficiency. 4 ITGAM integrin subunit alpha M This gene encodes the integrin alpha M chain.This I-domain containing alpha integrin combines with the beta 2 chain (ITGB2) to form a leukocyte-specific integrin referred to as macrophage receptor 1 ('Mac-1'), or inactivated-C3b (iC3b) receptor 3 ('CR3').The alpha M beta 2 integrin is important in the adherence of neutrophils and monocytes to stimulated endothelium, and also in the phagocytosis of complement coated particles. 5 PLEK pleckstrin Involved in several processes, including G protein-coupled receptor signaling pathway; actin cytoskeleton organization; and positive regulation of supramolecular fiber organization. 6 CTSS cathepsin S This gene participates in the degradation of antigenic proteins to peptides for presentation on MHC class II molecules. This gene is implicated in the pathology of many inflammatory and autoimmune diseases 7 LY86 lymphocyte antigen 86 Acts upstream of or within positive regulation of lipopolysaccharide-mediated signaling pathway. 8 ITGAX integrin subunit alpha X This gene encodes the integrin alpha X chain protein.The alpha X beta 2 complex seems to overlap the properties of the alpha M beta 2 integrin in the adherence of neutrophils and monocytes to stimulated endothelium cells, and in the phagocytosis of complement coated particles. 9 CCL4 C-C motif chemokine ligand 4 It is one of the major HIV-suppressive factors produced by CD8 + T-cells. The encoded protein is secreted and has chemokinetic and inflammatory functions. 10 FCER1G Fc epsilon receptor Ig The high affinity IgE receptor is a key molecule involved in allergic reactions. 3.5.Validation of Hub Genes Expression Two other datasets containing atherosclerotic plaques and AAAs were selected to confirm the reliability of these gene expression levels. In the GSE100927 dataset, the expression values of PLEK were missing, whereas the expression of other genes was upregulated in atherosclerotic plaques (Fig. 7 ). In the GSE98278 dataset, expression values of PTPRC were missing. In addition, expressions of ITGAM and CCL4 were not statistically significant. Expression of other genes was upregulated in AAA (Fig. 8 ). Combining the above results, TYROBP, ITGB2, CTSS, LY86, ITGAX, and FCER1G were expressed in these datasets with the same tendency as in the original datasets. 3.6.Prediction and Verification of TFs Using the ChEA3 database, nine TFs were predicted to regulate the expression of these hub genes, with four TFs predicted to regulate the expression of these hub genes, using the TRRUST database. Of these, only one transcription factor (SPI1) was common to both databases (Fig. 9 A) and shown to be highly expressed in both diseases (Fig. 9 B, C), being involved in the regulation of eight hub genes (PTPRC, ITGAM, ITGB2, ITGAX, PLEK, CCL4, LY86, and CTSS). 4. Discussion Our identification of common hub genes for atherosclerosis and AAA provides new insights into the shared biological mechanisms of these two diseases. Our findings of an association between atherosclerosis and AAA is consistent with previous studies [ 21 , 22 ]. Identification of the common DEGs for atherosclerosis and AAA will help to explore their common pathogenesis, identify new therapeutic targets, and predict the therapeutic effect of biological agents. Our study identified 133 overlapping DEGs between atherosclerosis and AAA, including 10 hub genes (TYROBP, PTPRC, ITGB2, ITGAM, PLEK, CTSS, LY86, ITGAX, CCL4, and FCER1G). GO and KEGG pathway enrichment analyses showed that these genes were involved in integrin-mediated signaling pathways, integrin-mediated cell adhesion, neutrophil chemotaxis, regulation of the actin cytoskeleton, chemokine signaling pathways, and antigen processing and presentation. These results demonstrate the important role of integrins, chemokines, and immune and inflammatory responses in both diseases. The GO analysis identified that integrin-mediated signaling pathways play an important role in both diseases. Furthermore, that leukocyte integrin αxβ2 was upregulated under hypercholesterolemic conditions with reduced atherogenesis after its deletion suggests that αxβ2 may be particularly important in atherogenicity [ 23 ]. Deposition of matrix proteins in atherosclerotic plaques creates a permissive environment for cell proliferation, migration, differentiation, and inflammatory responses, primarily via integrin α5β1 and αvβ3 signaling [ 24 ]. Fibroblast growth factor 18 and integrin β1 can improve the repair of AAA by increasing elastin expression, enhancing the migration and proliferation of smooth muscle cells, and improving aortic remodeling [ 25 ]. Therefore, integrins may be the link between atherosclerosis and AAA. In our study, we further identified nine TFs in the TRRSUT database and four TFs in the ChEA3 database which may regulate the expression of the identified hub genes. By combining these results, the high reliability of expression of one TF (SPI1) in atherosclerosis and AAA was confirmed. SPI1 is involved in the regulation of eight hub genes (PTPRC, ITGAM, ITGB2, ITGAX, PLEK, CCL4, LY86, and CTSS). Of these, after gene expression verification, only ITGB2, CTSS, LY86, and ITGAX were found to be highly expressed in both atherosclerosis and AAA. Integrin subunit beta 2 (ITGB2) encodes the integrin beta chain. The protein encoded by this gene plays an important role in immune responses, with a defect of this gene leading to defective leukocyte adhesion. ICAM1 and endothelial cells recruit circulating ITGB2, also known as CD18, and immune cells contribute to atherosclerosis; therefore, inhibition of ITGB2 can alleviate or even prevent the development of atherosclerosis [ 26 ]. Animal experiments have shown that treatment of mice with AAA using an anti-CD18 monoclonal antibody alleviates AAA expansion and reduces the inflammatory response [ 27 ], indicative of the potential benefit of ITGB2 downregulation in patients with AAA. Cathepsin S (CTSS) is a lysosomal cysteine proteinase that participates in the degradation of antigenic proteins into peptides for presentation on MHC class II molecules. CTSS is involved in the pathogenesis of cardiovascular diseases via its effect on extracellular matrix protein degradation, protein transport, and cell signaling [ 28 ]. CTSS can be secreted into the extracellular matrix via lysosomes, increasing collagen and elastin degradation, promoting vascular smooth muscle migration, and ultimately causing atherosclerosis [ 29 ]. Apoptosis of the medial smooth muscle cells of the arterial wall is an important marker of AAA, with an increase in apoptosis during aneurysm formation. Reduction of CTSS has been shown to attenuate smooth muscle cell apoptosis in the aorta, in vitro . and reduce smooth muscle cell loss in AAA lesions [ 30 ]. Lymphocyte antigen 86 (LY86), also known as MD-1, can form a complex with radioprotective 105 (PR105) to block the TLR4/MD-2 complex and, thus, attenuate inflammation via the NF-KB signaling pathway [ 31 ]. Therefore, an RP105 deficiency can lead to a slower progression of early atherosclerotic plaques [ 32 ]. Divanovic et al. showed that RP105 can suppress TLR4 signaling only when MD-1 is fully present [ 33 ]. Therefore, the detailed mechanism by which the specific RP105/MD-1 complex leads to atherosclerosis needs to be further elucidated. The expression of LY86 was not limited to immune cells but was also highly expressed in cardiovascular tissues. LY86 plays an important role in cardiac remodeling, myocardial hypertrophy, fibrosis, arrhythmia, and heart failure [ 34 ]. Although the effect of LY86 on AAA is still unclear, we believe that LY86 also plays an important role in the pathogenesis of AAA. Integrin subunit alpha X (ITGAX), known as CD11C, encodes an integrin X-chain protein that binds to ITGB2 to form a leukocyte-specific integrin called inactivated-C3b (iC3b) receptor 4 (CR4). ITGAX is a fibrinogen receptor that is important for monocyte adhesion and chemotaxis, which mediates cell-to-cell interactions during inflammatory responses. Monocytes are among the main cells involved in atherosclerosis. ITGAX can mediate the adhesion of monocytes to endothelial cells and then infiltrate the arterial wall through endothelial cells [ 35 ], which is an important link in the formation of atherosclerosis [ 36 ]. In addition, CD11C expression in macrophages is regulated by interferon regulatory factor-5, promoting the presence of CD11C-expressing macrophages within atheromatous plaques [ 37 ]. Previous studies have shown that CD4 T cells and CD8 + T cells decrease significantly after CD11C deletion, which can further down-regulate activity of neutrophil elastase, thus decreasing elastase degradation and increasing collagen content and, overall, inhibiting degradation of the abdominal aortic matrix [ 38 ]. However, few studies have explored the relationship between atherosclerosis and AAA. Our study focused on the common hub genes and related transcription factors in atherosclerosis and AAA. Hub genes were identified using a complex network of interactions and key nodes. This bioinformatics approach has proven to be reliable for other diseases [ 39 – 41 ]. Moreover, we verified the expression levels of the hub genes and transcription factors, which made our results more credible. We believe that our results provide a new research direction for the molecular mechanism of atherosclerosis complicated by AAA. The limitations of our study need to be acknowledged. The datasets we selected were from different platforms and, therefore, the detection methods and algorithms for the platforms are bound to be different. In the future, we plan to use a microarray from the same platform to test our patients to eliminate this difference. The function of hub genes also needs to be further verified in cell and animal models, which will be the focus of our future studies. In future studies, we suspected that it may be possible to detect hub genes expression levels in the blood of patients with two diseases to predict the trend of disease occurrence. 5. Conclusions Common DEGs associated with atherosclerosis and AAA were identified and subjected to enrichment analysis and PPI network analysis, identifying a common pathogenetic pathway which may be mediated by specific hub genes. Specifically, we identified that integrin-related genes may play significant roles in both diseases. Our findings may provide new directions for research on the molecular mechanisms of atherosclerosis complicated by AAA. Abbreviations AAA, abdominal aortic aneurysm BP, biological processes CC, cellular components ChEA3, ChIP-X enrichment analysis 3 CTSS, cathepsin S DAVID, Database for Annotation, Visualization and Integrated Discovery DEG, differentially expressed genes GEO, Gene Expression Omnibus (GEO) database GO, gene ontology ITGB2, integrin subunit beta 2 KEGG, Kyoto Encyclopedia of Genes and Genomes LY86, lymphocyte antigen 86 MCODE, molecular complex detection technology MF, molecular functions PPI, protein-protein interaction PR105, radioprotective 105 STRING, Search Tool for the Retrieval of Interacting Genes TF, transcription factor TRRUST, Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining Declarations Ethics approval and Consent to participate: Not applicable in ethics approval and consent to participant. (No human or human tissue samples participated in this study, and all data were from public databases) Consent for publication: Not applicable. Availability of data and materials: The datasets (GSE28829 and GSE7084) used in this study were all from the GEO database (https://www.ncbi.nlm.nih.gov/geo/) and were publicly available, no payment or login was required. Conflicting Interests: The authors declare that there is no conflict of interest. Funding: This work was supported by the National Natural Science Foundation of China [82370470], and the Fujian Provincial Special Reserve Talents Fund [2021-25]. Authors’ Contributions: LK.Ma: designing, collecting literature, editing figures, and drafting manuscripts. KY.Chen: collecting literature and designing. LK.Ma and LL.Tang: data analysis. Liangwan Chen and Zhihuang Qiu: editing the manuscript. Acknowledgments: Not applicable. References Johnston KW, et al. Suggested standards for reporting on arterial aneurysms. 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Interferon Regulatory Factor 5 Controls Necrotic Core Formation in Atherosclerotic Lesions by Impairing Efferocytosis. Circulation. 2017;136(12):1140–54. Krishna SM, et al. Depletion of CD11c + dendritic cells in apolipoprotein E-deficient mice limits angiotensin II-induced abdominal aortic aneurysm formation and growth. Clin Sci (Lond). 2019;133(21):2203–15. Fang X, et al. Identification of key genes associated with changes in the host response to severe burn shock: a bioinformatics analysis with data from the gene expression omnibus (GEO) database. J Inflamm Res. 2020;13:1029. Yang S et al. Analysis of potential hub genes involved in the pathogenesis of Chinese type 1 diabetic patients . Annals Translational Med, 2020. 8(6). Su W et al. Exploring the pathogenesis of psoriasis complicated with atherosclerosis via microarray data analysis . Front Immunol, 2021: p. 2045. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterialTable1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3984086","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":274872760,"identity":"66368127-04e2-4067-9419-d2aea47c76d6","order_by":0,"name":"Likang Ma","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Likang","middleName":"","lastName":"Ma","suffix":""},{"id":274872761,"identity":"bab87584-77bd-4899-aa82-7946f67b1a6a","order_by":1,"name":"Keyuan Chen","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Keyuan","middleName":"","lastName":"Chen","suffix":""},{"id":274872762,"identity":"5f83f7de-c7a4-4822-9406-e4d0feee97d6","order_by":2,"name":"Lele Tang","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lele","middleName":"","lastName":"Tang","suffix":""},{"id":274872763,"identity":"2cf74ed0-a65f-4578-9c6c-173662739c8b","order_by":3,"name":"Liangwan Chen","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Liangwan","middleName":"","lastName":"Chen","suffix":""},{"id":274872764,"identity":"a3f64cb6-fecb-4090-b924-d66fc60b3c47","order_by":4,"name":"Zhihuang Qiu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAnUlEQVRIiWNgGAWjYFACHoYPDxhsSNPCOCOBIU2CZC2HSdDCd/7swYbEnPN1/GIHGD98zCFCi+SBc4kNidtuS0jOTmCWnLmNCC0GB3vMH4C0GNxOYGPmJUrLYR5DoC3nSNFyDKzlAAlaJM+AtSRLzpyd2EycX/jOnzFs+LjNjp9fOvngh4/EaGE4AGcxNhCjHkXLKBgFo2AUjAIcAACXlzhACS4EKgAAAABJRU5ErkJggg==","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":true,"prefix":"","firstName":"Zhihuang","middleName":"","lastName":"Qiu","suffix":""}],"badges":[],"createdAt":"2024-02-24 06:19:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3984086/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3984086/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51764009,"identity":"1b5f6eab-4dd1-4c23-aea4-67475d3fa511","added_by":"auto","created_at":"2024-02-28 17:45:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":139144,"visible":true,"origin":"","legend":"\u003cp\u003eThe study flow chart.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3984086/v1/a92886104b17246ab09ba9cf.png"},{"id":51763815,"identity":"4dbc9b5e-5ac9-4b5e-b507-d9c604931b10","added_by":"auto","created_at":"2024-02-28 17:37:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":90781,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of common differentially expressed genes (DEGs).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003eBox plot after normalization of the GSE28829 and GSE7084 datasets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.\u003c/strong\u003eVolcano plot of GSE28829 and GSE7084, with red data points indicating up-regulated genes and blue indicating down-regulated genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC.\u003c/strong\u003eVenn diagram of the GSE28829 and GSE7084 datasets, identifying an overlap of 134 DEGs\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3984086/v1/f3d055f0eb1d511e96413f2f.png"},{"id":51763818,"identity":"a4c56c1c-1402-49dc-a1ec-49cbc3b69122","added_by":"auto","created_at":"2024-02-28 17:37:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":122767,"visible":true,"origin":"","legend":"\u003cp\u003eProtein-protein network and the enrichment analysis of DEGs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. \u003c/strong\u003eThe PPI network constructed by differential genes is shown; the darker the color, the higher the degree of interaction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. \u003c/strong\u003eResults of the GO enrichment analysis of differentially expressed genes (DEGs).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC.\u003c/strong\u003eResults of the KEGG Pathway enrichment analysis of differentially expressed genes (DEGs).\u003c/p\u003e\n\u003cp\u003eGO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3984086/v1/ef2e37f844d3777ab997e61f.png"},{"id":51763821,"identity":"4fcffee6-d925-436e-886b-4dba3b23a2cc","added_by":"auto","created_at":"2024-02-28 17:37:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":86053,"visible":true,"origin":"","legend":"\u003cp\u003eGene enrichment analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003eFour key gene modules obtained using the MCODE plug-in of Cytoscape.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.\u003c/strong\u003eGO enrichment analysis of the modular genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC.\u003c/strong\u003eKEGG Pathway enrichment analysis of the modular genes.\u003c/p\u003e\n\u003cp\u003eGO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; MCODE, molecular complex detection technology\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3984086/v1/d9731208c63bc9a454f99d68.png"},{"id":51763822,"identity":"5b3a5ecc-6c8c-4453-ab7d-df0dea0ac823","added_by":"auto","created_at":"2024-02-28 17:37:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":124644,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of hub genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003eThe upset plot showed that eight algorithms have screened out 10 overlapping hub genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.\u003c/strong\u003eHub genes and their co-expression genes were analysis by GeneMANIA.\u003c/p\u003e","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3984086/v1/72d147bdcf1162fc59f392b6.png"},{"id":51763824,"identity":"1a510f0e-3bec-4e45-8c56-b015eed6f90d","added_by":"auto","created_at":"2024-02-28 17:37:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":70537,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment analysis of the hub genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003eGO enrichment analysis of the hub genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.\u003c/strong\u003eKEGG Pathway enrichment analysis of the hub genes.\u003c/p\u003e\n\u003cp\u003eGO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e","description":"","filename":"OnlineFigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3984086/v1/68d07c1cdbaa7de15e18dfe0.png"},{"id":51763819,"identity":"9a22d8c5-5698-46d5-a334-004987467d64","added_by":"auto","created_at":"2024-02-28 17:37:10","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":99227,"visible":true,"origin":"","legend":"\u003cp\u003eVerification of the expression level of hub genes in GSE100927.\u003c/p\u003e\n\u003cp\u003eAA, atherosclerosis; CA, control artery.\u003c/p\u003e","description":"","filename":"OnlineFigure7.png","url":"https://assets-eu.researchsquare.com/files/rs-3984086/v1/5055ed5ddb8441ceb99f9179.png"},{"id":51763825,"identity":"3a78484e-9e36-4d5a-bea6-2111cb573cca","added_by":"auto","created_at":"2024-02-28 17:37:11","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":98956,"visible":true,"origin":"","legend":"\u003cp\u003eVerification of the expression level of hub genes in GSE98278.\u003c/p\u003e\n\u003cp\u003eAAA, abdominal aortic aneurysms; CO, compared ruptured abdominal aortic aneurysms.\u003c/p\u003e","description":"","filename":"OnlineFigure8.png","url":"https://assets-eu.researchsquare.com/files/rs-3984086/v1/099f49beea71652fa8b637c6.png"},{"id":51763820,"identity":"df109e7a-ceca-4fe8-b1bb-00147ce9a353","added_by":"auto","created_at":"2024-02-28 17:37:10","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":76661,"visible":true,"origin":"","legend":"\u003cp\u003ePredicting the results and the expression level of TFs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003ePredicting the results of TFs in ChEA3 database and TRRUST database respectively and the intersection of two databases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.\u003c/strong\u003eThe expression level of TF expression was verifiedin GSE28829.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC.\u003c/strong\u003eThe expression level of TF expression was verified in GSE7084.\u003c/p\u003e\n\u003cp\u003eTF, transcription factor\u003c/p\u003e","description":"","filename":"OnlineFigure9.png","url":"https://assets-eu.researchsquare.com/files/rs-3984086/v1/c45cc6b6b90e62e8f38a6c03.png"},{"id":56240314,"identity":"d2aa28a6-7b2a-4045-925f-ae7b9ed9f1b5","added_by":"auto","created_at":"2024-05-10 09:44:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4139561,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3984086/v1/9af3e38d-26e5-4132-b264-7d123547c52b.pdf"},{"id":51763817,"identity":"b855d0b7-3e0e-446d-a50e-79dff74ee16b","added_by":"auto","created_at":"2024-02-28 17:37:10","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":25521,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3984086/v1/205ec5945459feb7822486c0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The role of integrin-related genes in atherosclerosis complicated by abdominal aortic aneurysm","fulltext":[{"header":"1. Background","content":"\u003cp\u003eAbdominal aortic aneurysms (AAA) are considered to have occurred if the local diameter of the abdominal aorta exceeds 50% of the normal diameter and the dilation is irreversible [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Atherosclerosis, characterized by abnormal vascular intima formation due to hyperlipidemia and lipid oxidation, may play a role in AAA formation. Fatty deposits of atherosclerotic plaques in the intima of arterial walls causes a proliferation of fibrous tissue and of the surrounding smooth muscles, leading to arterial stiffening [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Moreover, both atherosclerosis and AAA share common risk factors, such as family history, male sex, advanced age, and smoking [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and the pathological processes of chronic inflammation, extracellular matrix degradation, vascular smooth muscle apoptosis, and thrombosis are involved in both AAA and atherosclerotic plaque formation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, atherosclerosis may potentially promote AAA [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] by causing a mechanical weakening of the aortic wall, loss of elasticity, and degenerative ischemic changes in the adventitial layer [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, the exact mechanism linking the pathogenesis of atherosclerosis and to AAA formation remains unclear. Identifying the common transcriptional signatures of atherosclerosis and AAA may clarify the shared pathogenetic pathway. Accordingly, our aim in this study was to identify the hub genes involved in the pathogenesis of atherosclerosis complicated by AAA.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1.Data Source\u003c/h2\u003e \u003cp\u003eThe Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], with \u0026ldquo;atherosclerosis\u0026rdquo; and \u0026ldquo;abdominal aortic aneurysms or AAA\u0026rdquo; used as keywords to search the dataset for related genes. GEO is a public database containing a large number of high-throughput sequencing and microarray datasets, submitted by research institutes worldwide. For our study, we used the following two microarray datasets, GSE28829[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and GSE7084[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The GSE28829 dataset, created on the GPL570 (Affymetrix Human Genome U133 Plus 2.0 Array) platform, contains 16 advanced atherosclerotic plaque samples (thin or thick fibrous cap atheroma) (AA) and 13 early atherosclerotic plaque samples ( intimal thickening and intimal xanthoma) as a control (CA), obtained from the human carotid artery. From the GSE7084 dataset, we chose the GPL2507 (Sentrix Human-6 Expression BeadChip) for a larger sample size, which contains seven abdominal aortic aneurysms (AAA) samples and eight control abdominal aorta samples (CO) obtained from autopsy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2.Identification of DEGs\u003c/h2\u003e \u003cp\u003eComparison of the gene expression profile between the disease and control groups was performed using the GEO query R package (GEO2R; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.ncbi.nlm.nih.gov/geo\" target=\"_blank\"\u003ewww.ncbi.nlm.nih.gov/geo/ge2r\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo/ge2r\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), to identify the differentially expressed genes (DEGs), and the Limma R package, to calculate multiple differential expressions [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. DEGs were identified by an adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and an absolute fold-change (|logFC|)\u0026thinsp;\u0026ge;\u0026thinsp;1. Probes that did not contain a corresponding gene were removed. If a gene corresponded to multiple probes, the one with the largest difference in expression was selected. The common set of DEGs between atherosclerosis and AAA was identified using a Venn diagram tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinformatics.psb.ugent.be/webtools/Venn/\u003c/span\u003e\u003cspan address=\"http://bioinformatics.psb.ugent.be/webtools/Venn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3.Enrichment Analyses of DEGs\u003c/h2\u003e \u003cp\u003eGene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis results for DEGs were obtained using the Database for Annotation, Visualization and Integrated Discovery (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/tools.jsp\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/tools.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which allowed us to investigate the biological functions and signaling pathways involved in a given gene set [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. GO includes three independent categories, namely biological processes (BP), molecular functions (MF), and cellular components (CC). Terms with a P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significantly enriched.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4.PPI Network Construction and Module Analysis\u003c/h2\u003e \u003cp\u003eThe relationship between proteins of interest was obtained using the Search Tool for the Retrieval of Interacting Genes (STRING 11.5; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org/\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which includes both direct binding relationships and coexisting upstream and downstream regulatory pathways [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This information can be used to construct a protein-protein interaction (PPI) network with complex regulatory relationships; interactions having a combined score\u0026thinsp;\u0026gt;\u0026thinsp;0.4 were considered significant. The PPI network was visualized using Cytoscape (Version 3.9.1 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cytoscape.org/\u003c/span\u003e\u003cspan address=\"https://cytoscape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The Cytoscape plug-in molecular complex detection technology (MCODE) was used to analyze the key functional modules, applying the following selection criteria: K-core\u0026thinsp;=\u0026thinsp;2; degree cutoff\u0026thinsp;=\u0026thinsp;2; maximum depth\u0026thinsp;=\u0026thinsp;100; and node score cutoff\u0026thinsp;=\u0026thinsp;0.2. The GO- and KEGG-based analyses of involved modular genes were then performed using DAVID.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5.Selection and Analysis of Hub Genes\u003c/h2\u003e \u003cp\u003eHub genes were identified using the CytoHubba plug-in (Cytoscape, version 3.9.1), with the following eight common algorithms used to then evaluate and select hub genes: MCC, MNC, EPC, degree, closeness, radiality, bottleneck, and eccentricity. A co-expression network of these hub genes was then constructed using GeneMANIA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://genemania.org/\u003c/span\u003e\u003cspan address=\"http://genemania.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a reliable tool for identifying internal associations within gene sets [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6.Validation of Hub Genes Expression in Other Data Sets\u003c/h2\u003e \u003cp\u003eThe mRNA expression of the hub genes was validated using two additional datasets, GSE100927 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and GSE98278 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Dataset GSE100927 includes 69 human samples of AA and 35 control artery samples (CA), while GSE98278 includes 31 human AAA samples and 17 peripheral normal aortic samples (CO) that collected during rupture repair of abdominal aortic aneurysm for comparison. Comparison of the two datasets was performed using Student\u0026rsquo;s t-test, with a P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7.Prediction and Verification of Transcription Factors (TFs)\u003c/h2\u003e \u003cp\u003eTo more accurately predict the transcription factors (TFs) that regulate the hub genes, the following two databases were used: Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRRUST) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.grnpedia.org/trrust/\u003c/span\u003e\u003cspan address=\"https://www.grnpedia.org/trrust/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and ChIP-X Enrichment Analysis 3 (ChEA3) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://maayanlab.cloud/chea3/\u003c/span\u003e\u003cspan address=\"https://maayanlab.cloud/chea3/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). For the ChEA3 database, the ENCODE library was selected, with the significance level set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The TRRUST database, used to predict transcriptional regulatory networks, contains the target genes corresponding to TFs and the regulatory relationships between TFs. The TRRUST database currently includes two species, humans and mice, with 8,444 and 6,552 TFs that target regulatory relationships of 800 human TFs and 828 mouse TFs, respectively [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The ChEA3 database is a TF enrichment analysis tool that contains a collection of gene set libraries generated from multiple sources, including TF-gene co-expression from RNA-seq studies, TF-target associations from ChIP-seq experiments, TF-gene co-occurrence computed from crowd-submitted gene lists, and ranks TFs associated with user-submitted gene sets [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In our study, common TFs predicted by both databases were selected. Finally, expression of TFs in datasets GSE28829 and GSE7084 was verified using Student\u0026rsquo;s t-test.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1.Identification of DEGs\u003c/h2\u003e \u003cp\u003eThe study process flow chart is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The data distribution, after standardization of the microarray data, is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA. The volcano plots of DEGs (270 in GSE28829 and 1168 in GSE7084), obtained by difference analysis, are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB. Using the Venn diagram, we identified 134 overlapping DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Verification of these DEGs in the two datasets identified 133 DEGs with the same expression trend, including 116 upregulated and 17 downregulated genes (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2.Analysis of the Functional Characteristics of Common DEGs\u003c/h2\u003e \u003cp\u003eGO and KEGG pathway analyses were performed on the 133 DEGs to determine the associated biological process (BP), cell component (CC), and molecular function (MF). Results of the GO analysis showed that DEGs were mainly enriched in the immune (P\u0026thinsp;=\u0026thinsp;1.34E-16) and inflammatory (P\u0026thinsp;=\u0026thinsp;3.19E-14) responses, antigen processing and presentation of exogenous peptide antigen via MHC class II (P\u0026thinsp;=\u0026thinsp;1.48E-11), and the innate immune response (P\u0026thinsp;=\u0026thinsp;2.25E-09) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). In the KEGG pathway analysis, the following three significantly enriched pathways were identified: complement and coagulation cascades (P\u0026thinsp;=\u0026thinsp;2.28E-06), chemokine signaling (P\u0026thinsp;=\u0026thinsp;3.83E-06), and antigen processing and presentation (P\u0026thinsp;=\u0026thinsp;1.40E-05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). These results illustrate the important roles of antigen processing and in immune inflammatory responses in both AAA and atherosclerosis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3.PPI Network Construction and Module Analysis\u003c/h2\u003e \u003cp\u003eThe PPI network of common DEGs, constructed in Cytoscape combining STRING scores\u0026thinsp;\u0026gt;\u0026thinsp;0.4, contained 116 nodes and 1197 edges (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Four closely related gene modules, including 43 DEGs and 429 edges, were identified using the MCODE plug-in of Cytoscape (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The GO analysis revealed that these genes were related to inflammatory, innate immune, and immune responses (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The KEGG pathway analysis showed that these genes were mainly involved in neutrophil extracellular trap formation, Fc gamma R-mediated phagocytosis, viral protein interaction with cytokines and the cytokine receptor, Toll-like receptor signaling, and the Rap1 signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4.Selection and Analysis of Hub Genes\u003c/h2\u003e \u003cp\u003eThe top 25 hub genes were obtained using eight plug-in cytoHubba algorithms (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Next, the 10 common hub genes were obtained using Upset diagrams: TYROBP, PTPRC, ITGB2, ITGAM, PLEK, CTSS, LY86, ITGAX, CCL4, and FCER1G (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The co-expression network and related functions of these hub genes, analyzed using the GeneMANIA database, revealed a complex PPI network with a co-expression of 58.88%, physical interactions of 29.53%, prediction of 6.00%, co-localization of 5.08%, and shared protein domains of 0.52% (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). These hub genes were closely involved in integrin-mediated signaling pathways, positive regulation of superoxide anion generation, cell adhesion mediated by integrin, neutrophil chemotaxis, and cell-matrix adhesion (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). These findings highlight the important role of integrins in atherosclerosis and AAA. In the KEGG pathway, complement and coagulation cascades, natural killer cell-mediated cytotoxicity, cell adhesion molecules, and regulation of the actin cytoskeleton were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). This suggests that changes in ITGAM, ITGB2, and ITGAX may affect the actin cytoskeleton and promote formation of arterial aneurysms.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe top 25 hub genes rank in cytoHubba.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEPC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCloseness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRadiality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBottleNeck\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEcCentricity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTYROBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTYROBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTYROBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTYROBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTYROBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTYROBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePTPRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAPOE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTPRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePTPRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePTPRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePTPRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePTPRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePTPRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTYROBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eITGAM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eITGB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eITGB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eITGB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eITGB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eITGB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eITGAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAPOE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCTSS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eITGAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eITGAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCD86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eITGAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eITGAM\u003c/p\u003e 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align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTYROBP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePLEK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCD86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCD86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCTSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHLA-DRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eITGB2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLCP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLCP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eITGAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLCP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLCP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLCP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMMP12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHLA-DRA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCTSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFCGR2B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCTSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCTSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTLR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNCF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMMP12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTLR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCSF1R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTLR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTLR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e 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align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCTSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePLEK\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLY86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLY86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCD53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLY86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLY86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLY86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCXCR4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCXCR4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCGR2B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFCGR2B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLY86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFCGR2B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFCGR2B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eITGAX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC1QC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eC1QC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAIF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIL10RA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSLAMF8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSCD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eITGAX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eITGAX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC1QA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eITGAX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e 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align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFCER1G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCCL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCD52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAIF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCD74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAIF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCD48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCCR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIGSF6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCD86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC3AR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC3AR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCCR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC3AR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC1QC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCD48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCD52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBCL2A1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe details of the hub genes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFull name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFunction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTYROBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTYRO protein tyrosine kinase binding protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThis gene encodes a transmembrane signaling polypeptide which contains an immunoreceptor tyrosine-based activation motif (ITAM) in its cytoplasmic domain. The encoded protein may bind zeta-chain (TCR) associated protein kinase 70kDa (ZAP-70) and spleen tyrosine kinase (SYK) and play a role in signal transduction, bone modeling, brain myelination, and inflammation.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePTPRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eprotein tyrosine phosphatase receptor type C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe protein encoded by this gene is a member of the protein tyrosine phosphatase (PTP) family.This PTP has been shown to be an essential regulator of T- and B-cell antigen receptor signaling.This PTP also suppresses JAK kinases.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eITGB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eintegrin subunit beta 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThis gene encodes an integrin beta chain, which combines with multiple different alpha chains to form different integrin heterodimers. Integrins are integral cell-surface proteins that participate in cell adhesion as well as cell-surface mediated signalling. The encoded protein plays an important role in immune response and defects in this gene cause leukocyte adhesion deficiency.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eITGAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eintegrin subunit alpha M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThis gene encodes the integrin alpha M chain.This I-domain containing alpha integrin combines with the beta 2 chain (ITGB2) to form a leukocyte-specific integrin referred to as macrophage receptor 1 ('Mac-1'), or inactivated-C3b (iC3b) receptor 3 ('CR3').The alpha M beta 2 integrin is important in the adherence of neutrophils and monocytes to stimulated endothelium, and also in the phagocytosis of complement coated particles.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePLEK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003epleckstrin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInvolved in several processes, including G protein-coupled receptor signaling pathway; actin cytoskeleton organization; and positive regulation of supramolecular fiber organization.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecathepsin S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThis gene participates in the degradation of antigenic proteins to peptides for presentation on MHC class II molecules. This gene is implicated in the pathology of many inflammatory and autoimmune diseases\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLY86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elymphocyte antigen 86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eActs upstream of or within positive regulation of lipopolysaccharide-mediated signaling pathway.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eITGAX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eintegrin subunit alpha X\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThis gene encodes the integrin alpha X chain protein.The alpha X beta 2 complex seems to overlap the properties of the alpha M beta 2 integrin in the adherence of neutrophils and monocytes to stimulated endothelium cells, and in the phagocytosis of complement coated particles.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC-C motif chemokine ligand 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIt is one of the major HIV-suppressive factors produced by CD8\u0026thinsp;+\u0026thinsp;T-cells. The encoded protein is secreted and has chemokinetic and inflammatory functions.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFCER1G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFc epsilon receptor Ig\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe high affinity IgE receptor is a key molecule involved in allergic reactions.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5.Validation of Hub Genes Expression\u003c/h2\u003e \u003cp\u003eTwo other datasets containing atherosclerotic plaques and AAAs were selected to confirm the reliability of these gene expression levels. In the GSE100927 dataset, the expression values of PLEK were missing, whereas the expression of other genes was upregulated in atherosclerotic plaques (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). In the GSE98278 dataset, expression values of PTPRC were missing. In addition, expressions of ITGAM and CCL4 were not statistically significant. Expression of other genes was upregulated in AAA (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Combining the above results, TYROBP, ITGB2, CTSS, LY86, ITGAX, and FCER1G were expressed in these datasets with the same tendency as in the original datasets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6.Prediction and Verification of TFs\u003c/h2\u003e \u003cp\u003eUsing the ChEA3 database, nine TFs were predicted to regulate the expression of these hub genes, with four TFs predicted to regulate the expression of these hub genes, using the TRRUST database. Of these, only one transcription factor (SPI1) was common to both databases (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA) and shown to be highly expressed in both diseases (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB, C), being involved in the regulation of eight hub genes (PTPRC, ITGAM, ITGB2, ITGAX, PLEK, CCL4, LY86, and CTSS).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur identification of common hub genes for atherosclerosis and AAA provides new insights into the shared biological mechanisms of these two diseases. Our findings of an association between atherosclerosis and AAA is consistent with previous studies [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Identification of the common DEGs for atherosclerosis and AAA will help to explore their common pathogenesis, identify new therapeutic targets, and predict the therapeutic effect of biological agents.\u003c/p\u003e \u003cp\u003eOur study identified 133 overlapping DEGs between atherosclerosis and AAA, including 10 hub genes (TYROBP, PTPRC, ITGB2, ITGAM, PLEK, CTSS, LY86, ITGAX, CCL4, and FCER1G). GO and KEGG pathway enrichment analyses showed that these genes were involved in integrin-mediated signaling pathways, integrin-mediated cell adhesion, neutrophil chemotaxis, regulation of the actin cytoskeleton, chemokine signaling pathways, and antigen processing and presentation. These results demonstrate the important role of integrins, chemokines, and immune and inflammatory responses in both diseases. The GO analysis identified that integrin-mediated signaling pathways play an important role in both diseases. Furthermore, that leukocyte integrin αxβ2 was upregulated under hypercholesterolemic conditions with reduced atherogenesis after its deletion suggests that αxβ2 may be particularly important in atherogenicity [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Deposition of matrix proteins in atherosclerotic plaques creates a permissive environment for cell proliferation, migration, differentiation, and inflammatory responses, primarily via integrin α5β1 and αvβ3 signaling [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Fibroblast growth factor 18 and integrin β1 can improve the repair of AAA by increasing elastin expression, enhancing the migration and proliferation of smooth muscle cells, and improving aortic remodeling [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Therefore, integrins may be the link between atherosclerosis and AAA.\u003c/p\u003e \u003cp\u003eIn our study, we further identified nine TFs in the TRRSUT database and four TFs in the ChEA3 database which may regulate the expression of the identified hub genes. By combining these results, the high reliability of expression of one TF (SPI1) in atherosclerosis and AAA was confirmed. SPI1 is involved in the regulation of eight hub genes (PTPRC, ITGAM, ITGB2, ITGAX, PLEK, CCL4, LY86, and CTSS). Of these, after gene expression verification, only ITGB2, CTSS, LY86, and ITGAX were found to be highly expressed in both atherosclerosis and AAA.\u003c/p\u003e \u003cp\u003eIntegrin subunit beta 2 (ITGB2) encodes the integrin beta chain. The protein encoded by this gene plays an important role in immune responses, with a defect of this gene leading to defective leukocyte adhesion. ICAM1 and endothelial cells recruit circulating ITGB2, also known as CD18, and immune cells contribute to atherosclerosis; therefore, inhibition of ITGB2 can alleviate or even prevent the development of atherosclerosis [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Animal experiments have shown that treatment of mice with AAA using an anti-CD18 monoclonal antibody alleviates AAA expansion and reduces the inflammatory response [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], indicative of the potential benefit of ITGB2 downregulation in patients with AAA.\u003c/p\u003e \u003cp\u003eCathepsin S (CTSS) is a lysosomal cysteine proteinase that participates in the degradation of antigenic proteins into peptides for presentation on MHC class II molecules. CTSS is involved in the pathogenesis of cardiovascular diseases via its effect on extracellular matrix protein degradation, protein transport, and cell signaling [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. CTSS can be secreted into the extracellular matrix via lysosomes, increasing collagen and elastin degradation, promoting vascular smooth muscle migration, and ultimately causing atherosclerosis [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Apoptosis of the medial smooth muscle cells of the arterial wall is an important marker of AAA, with an increase in apoptosis during aneurysm formation. Reduction of CTSS has been shown to attenuate smooth muscle cell apoptosis in the aorta, \u003cem\u003ein vitro\u003c/em\u003e. and reduce smooth muscle cell loss in AAA lesions [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLymphocyte antigen 86 (LY86), also known as MD-1, can form a complex with radioprotective 105 (PR105) to block the TLR4/MD-2 complex and, thus, attenuate inflammation via the NF-KB signaling pathway [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Therefore, an RP105 deficiency can lead to a slower progression of early atherosclerotic plaques [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Divanovic et al. showed that RP105 can suppress TLR4 signaling only when MD-1 is fully present [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Therefore, the detailed mechanism by which the specific RP105/MD-1 complex leads to atherosclerosis needs to be further elucidated. The expression of LY86 was not limited to immune cells but was also highly expressed in cardiovascular tissues. LY86 plays an important role in cardiac remodeling, myocardial hypertrophy, fibrosis, arrhythmia, and heart failure [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Although the effect of LY86 on AAA is still unclear, we believe that LY86 also plays an important role in the pathogenesis of AAA.\u003c/p\u003e \u003cp\u003eIntegrin subunit alpha X (ITGAX), known as CD11C, encodes an integrin X-chain protein that binds to ITGB2 to form a leukocyte-specific integrin called inactivated-C3b (iC3b) receptor 4 (CR4). ITGAX is a fibrinogen receptor that is important for monocyte adhesion and chemotaxis, which mediates cell-to-cell interactions during inflammatory responses. Monocytes are among the main cells involved in atherosclerosis. ITGAX can mediate the adhesion of monocytes to endothelial cells and then infiltrate the arterial wall through endothelial cells [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], which is an important link in the formation of atherosclerosis [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In addition, CD11C expression in macrophages is regulated by interferon regulatory factor-5, promoting the presence of CD11C-expressing macrophages within atheromatous plaques [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Previous studies have shown that CD4 T cells and CD8\u0026thinsp;+\u0026thinsp;T cells decrease significantly after CD11C deletion, which can further down-regulate activity of neutrophil elastase, thus decreasing elastase degradation and increasing collagen content and, overall, inhibiting degradation of the abdominal aortic matrix [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. However, few studies have explored the relationship between atherosclerosis and AAA.\u003c/p\u003e \u003cp\u003eOur study focused on the common hub genes and related transcription factors in atherosclerosis and AAA. Hub genes were identified using a complex network of interactions and key nodes. This bioinformatics approach has proven to be reliable for other diseases [\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Moreover, we verified the expression levels of the hub genes and transcription factors, which made our results more credible. We believe that our results provide a new research direction for the molecular mechanism of atherosclerosis complicated by AAA.\u003c/p\u003e \u003cp\u003eThe limitations of our study need to be acknowledged. The datasets we selected were from different platforms and, therefore, the detection methods and algorithms for the platforms are bound to be different. In the future, we plan to use a microarray from the same platform to test our patients to eliminate this difference. The function of hub genes also needs to be further verified in cell and animal models, which will be the focus of our future studies. In future studies, we suspected that it may be possible to detect hub genes expression levels in the blood of patients with two diseases to predict the trend of disease occurrence.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eCommon DEGs associated with atherosclerosis and AAA were identified and subjected to enrichment analysis and PPI network analysis, identifying a common pathogenetic pathway which may be mediated by specific hub genes. Specifically, we identified that integrin-related genes may play significant roles in both diseases. Our findings may provide new directions for research on the molecular mechanisms of atherosclerosis complicated by AAA.\u003c/p\u003e"},{"header":"Abbreviations","content":" \u003cp\u003eAAA, abdominal aortic aneurysm\u003c/p\u003e \u003cp\u003eBP, biological processes\u003c/p\u003e \u003cp\u003eCC, cellular components\u003c/p\u003e \u003cp\u003eChEA3, ChIP-X enrichment analysis 3\u003c/p\u003e \u003cp\u003eCTSS, cathepsin S\u003c/p\u003e \u003cp\u003eDAVID, Database for Annotation, Visualization and Integrated Discovery\u003c/p\u003e \u003cp\u003eDEG, differentially expressed genes\u003c/p\u003e \u003cp\u003eGEO, Gene Expression Omnibus (GEO) database\u003c/p\u003e \u003cp\u003eGO, gene ontology\u003c/p\u003e \u003cp\u003eITGB2, integrin subunit beta 2\u003c/p\u003e \u003cp\u003eKEGG, Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e \u003cp\u003eLY86, lymphocyte antigen 86\u003c/p\u003e \u003cp\u003eMCODE, molecular complex detection technology\u003c/p\u003e \u003cp\u003eMF, molecular functions\u003c/p\u003e \u003cp\u003ePPI, protein-protein interaction\u003c/p\u003e \u003cp\u003ePR105, radioprotective 105\u003c/p\u003e \u003cp\u003eSTRING, Search Tool for the Retrieval of Interacting Genes\u003c/p\u003e \u003cp\u003eTF, transcription factor\u003c/p\u003e \u003cp\u003eTRRUST, Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and Consent to participate:\u003c/strong\u003e Not applicable in ethics approval and consent to participant. (No human or human tissue samples participated in this study, and all data were from public databases)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe datasets (GSE28829 and GSE7084) used in this study were all from the GEO database (https://www.ncbi.nlm.nih.gov/geo/) and were publicly available, no payment or login was required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicting Interests:\u003c/strong\u003e The\u0026nbsp;authors declare that there is no conflict of interest.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This work was supported by the National Natural Science Foundation of China\u0026nbsp;[82370470], and the Fujian Provincial Special Reserve Talents Fund [2021-25].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions:\u0026nbsp;\u003c/strong\u003eLK.Ma: designing, collecting literature, editing figures, and drafting manuscripts. KY.Chen: collecting literature and designing. LK.Ma and LL.Tang: data analysis. Liangwan Chen and Zhihuang Qiu: editing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJohnston KW, et al. Suggested standards for reporting on arterial aneurysms. J Vasc Surg. 1991;13(3):452\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRafieian-Kopaei M, et al. Atherosclerosis: process, indicators, risk factors and new hopes. Int J Prev Med. 2014;5(8):927\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIto S, et al. Differences in atherosclerotic profiles between patients with thoracic and abdominal aortic aneurysms. Am J Cardiol. 2008;101(5):696\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWassef M, et al. 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Front Immunol, 2021: p. 2045.\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":"atherosclerosis, abdominal aortic aneurysm, bioinformatics, microarray, hub genes","lastPublishedDoi":"10.21203/rs.3.rs-3984086/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3984086/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIncreasingly, the shared risk factors and pathological processes of atherosclerosis and abdominal aortic aneurysm (AAA) are being recognized. The aim of our study was to identify the hub genes involved in the pathogenesis of atherosclerosis and AAA.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe analysis was based on two gene expression profiles for atherosclerosis (GSE28829) and AAA (GSE7084), downloaded from the Gene Expression Omnibus (GEO) database. Common differential genes were identified and an enrichment analysis of differential genes was conducted, with construction of protein-protein interaction networks, and identification of common hub genes and predicted transcription factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe analysis identified 133 differentially expressed genes (116 upregulated and 17 downregulated), with the enrichment analysis identifying a potential important role of integrins and chemokines in the common immune and inflammatory responses of atherosclerosis and AAA. Regulation of the complement and coagulation cascades and regulation of the actin cytoskeleton were associated with both diseases, with 10 important hub genes identified: TYROBP, PTPRC, ITGB2, ITGAM, PLEK, CTSS, LY86, ITGAX, CCL4, and FCER1G.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eFindings identified a common pathogenetic pathway between atherosclerosis and AAA, with integrin-related genes playing a significant role. The common pathways and hub genes identified provide new insights into the shared mechanisms of these two diseases and can contribute to identifying new therapeutic targets and predicting the therapeutic effect of biological agents.\u003c/p\u003e","manuscriptTitle":"The role of integrin-related genes in atherosclerosis complicated by abdominal aortic aneurysm","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-28 17:37:05","doi":"10.21203/rs.3.rs-3984086/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":"01508f10-fb6c-4f3b-baad-04b243a1f834","owner":[],"postedDate":"February 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-10T09:36:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-28 17:37:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3984086","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3984086","identity":"rs-3984086","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}