Identification of diagnostic biomarkers and therapeutic targets for abdominal aortic aneurysm via transcriptome sequencing and integrated bioinformatics

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Methods RNA sequencing was performed on abdominal aortic tissues from AAA-induced rats and healthy controls. Differentially expressed genes (DEGs) were identified through bioinformatic analysis, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Protein–protein interaction (PPI) networks were constructed to identify central regulatory genes. Additional analyses included tissue-specific gene expression profiling, Gene Set Enrichment Analysis (GSEA), and molecular docking to predict candidate therapeutic compounds. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) was conducted to validate key gene expression. Results A total of 400 DEGs were identified in AAA tissues, including 314 upregulated and 86 downregulated genes. Functional enrichment indicated significant involvement in biological processes such as response to external stimuli, plasma membrane localization, and cell adhesion. KEGG analysis highlighted the PI3K-Akt signaling pathway as prominently associated with AAA. PPI network analysis identified five hub genes— Fcgr2b , Tlr7 , Clec7a , Tlr9 , and Cd53 —which were significantly upregulated in AAA tissues. Tissue-specific expression analysis revealed that these genes were predominantly expressed in immune-related organs such as the spleen and bone marrow. GSEA showed enrichment of Cd53 , Fcgr2b , and Tlr9 in leukocyte transendothelial migration and actin cytoskeleton regulation pathways, while Clec7a and Tlr7 were linked to cell cycle progression and DNA replication. Molecular docking identified diphenylpyraline as a potential therapeutic compound targeting AAA-related pathways. RT-qPCR validation confirmed the differential expression of the five hub genes. Conclusion This integrative transcriptomic and bioinformatic analysis provides novel insights into the molecular mechanisms underlying AAA and identifies promising diagnostic biomarkers and therapeutic targets. abdominal aortic aneurysm diagnostic biomarkers therapeutic targets transcriptome sequencing bioinformatics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction Abdominal aortic aneurysm (AAA) is a life-threatening vascular disorder characterized by the localized dilation of the abdominal aorta, posing a major risk to cardiovascular health [ 1 ]. Clinically, AAA is defined as an aortic diameter at least 1.5 times greater than normal at the level of the renal arteries, typically exceeding 3.0 cm [ 2 ]. AAAs are generally classified as either saccular (localized) or fusiform (circumferential), with the fusiform type accounting for more than 90% of cases. The clinical consequences of AAA are often severe, including compression of surrounding organs, abdominal pain, and, most critically, aortic rupture, which leads to hemorrhagic shock and high mortality [ 3 ]. Despite advances in open surgical repair and endovascular aneurysm repair (EVAR), the mortality rate following AAA rupture remains alarmingly high. Furthermore, current therapeutic strategies do not adequately prevent AAA recurrence or progression [ 4 ]. As a result, increasing attention has shifted toward elucidating the molecular mechanisms of AAA to uncover reliable diagnostic biomarkers and novel therapeutic targets [ 5 ]. Inflammation has emerged as a central contributor to AAA pathogenesis, with immune cell infiltration—particularly by T cells, macrophages, and B cells—promoting structural weakening of the aortic wall [ 6 , 7 ]. Although inflammation and immune dysregulation are well-established features of AAA, the underlying gene regulatory networks and their interactions remain insufficiently characterized. Animal models have played a critical role in elucidating AAA pathophysiology [ 8 , 9 ]. Refinement of these models is ongoing, aiming to better recapitulate the complexity of human AAA and enhance translational relevance [ 10 ]. Commonly used experimental models in mice, rats, and rabbits employ methods such as angiotensin II (AngII) infusion, elastase perfusion, and calcium chloride application to induce aneurysm formation [ 11 , 12 ]. These approaches have not only advanced AAA research but also contributed to investigations into various diseases, including diabetes, obesity, neurological disorders, and cancer [ 13 ]. Complementing in vivo studies, bioinformatics has become an essential tool for analyzing high-throughput omics data, including those generated from transcriptomic studies[ 14 ]. Bioinformatic analyses enable the identification of key genes, signaling pathways, and molecular signatures implicated in AAA, offering new opportunities for mechanistic insight and therapeutic development [ 15 ]. The integration of RNA sequencing (RNA-seq) with computational analysis has proven especially effective for detecting novel exons, quantifying gene expression, and characterizing alternative splicing events [ 16 ]. These technologies have opened new avenues for understanding AAA at the molecular level and for identifying actionable targets for diagnosis and treatment. Building on these advancements, the present study aims to identify key regulatory genes involved in AAA pathogenesis through a comprehensive approach integrating RNA-seq with differential gene expression and protein–protein interaction network analyses [ 17 ]. To further elucidate the biological roles of these genes, we employed subcellular and chromosomal localization profiling, as well as Gene Set Enrichment Analysis (GSEA). In addition, we constructed lncRNA-miRNA-mRNA and TF-miRNA-mRNA regulatory networks to explore upstream modulators of these key targets. Finally, drug prediction analysis was performed to identify potential small-molecule therapeutics that interact with the identified targets. Through this multidimensional framework, we aim to provide a detailed understanding of AAA molecular pathology and to identify novel candidates for clinical application in diagnosis and therapy. 2 Materials and methods 2.1 Establishment of the Abdominal Aortic Aneurysm Animal Model and Tissue Collection Male Sprague–Dawley (SD) rats (4–6 weeks old, 280–310 g) were obtained from Kunming Medical University and housed under controlled environmental conditions (22 ± 1°C, 60% relative humidity, 12-hour light/dark cycle) with free access to food and water. All animal procedures were conducted in accordance with the International Guiding Principles for Biomedical Research Involving Animals and were approved by the Ethics Committee for Animal Experiments at Kunming Medical University (Approval No. 2014YYGJ116). A total of six rats were used to induce abdominal aortic aneurysm (AAA) via intra-aortic perfusion of porcine pancreatic elastase (0.4 U/µL; Sigma-Aldrich, Søborg, Denmark). Under general anesthesia induced with isoflurane (3–5% in oxygen) and maintained at ≥ 5% via a nose cone, a midline laparotomy was performed to expose the abdominal aorta. The targeted segment was temporarily clamped and perfused with elastase for 30 minutes to degrade elastin and promote aneurysmal dilation. One rat died during the perioperative period, while the remaining five were monitored postoperatively under standard care conditions. On day 14, all surviving animals were humanely euthanized under deep isoflurane anesthesia. Depth of anesthesia was confirmed by loss of the righting reflex and absence of pedal reflex before continuing high-concentration isoflurane exposure (≥ 5%) until respiratory arrest occurred. Death was verified by the absence of respiration and heartbeat. AAA formation was confirmed by abdominal ultrasonography and defined as a ≥ 50% increase in aortic diameter relative to baseline. Aneurysmal aortic tissues were collected from the five surviving rats, along with abdominal aortic tissues from five healthy controls [ 18 ], and subsequently subjected to transcriptome sequencing. 2.2 RNA Extraction and library construction Total RNA was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) following the manufacturer’s instructions. RNA quantity and purity were assessed using a NanoDrop ND-1000 spectrophotometer (NanoDrop, Wilmington, DE, USA). Reverse transcription of cleaved RNA fragments into cDNA was performed using SuperScript™ II Reverse Transcriptase (Invitrogen, Cat. No. 1896649, USA). After adapter ligation and PCR amplification, sequencing libraries were constructed, and 2 × 150 bp paired-end reads (PE150) were generated using the Illumina NovaSeq™ 6000 platform (LC-Bio Technology Co., Ltd., Hangzhou, China). 2.3 Data processing Initial quality control of raw sequencing reads was performed using FastQC (v0.11.5), and base error rates were calculated with Phred. Clean reads were aligned to the rat reference genome using HISAT2 (Supplementary Fig. 1). The genome and annotation files were retrieved from the Ensembl database ( https://asia.ensembl.org/info/about/species.html ). Transcript assembly and gene quantification were carried out using Cufflinks (v2.2.1), yielding count data for downstream analysis (Supplementary Table 1). To assess data dispersion and inter-group consistency, principal component analysis (PCA) was performed using the scatterplot3d package (v0.3-44) [ 19 ] (Supplementary Fig. 2). 2.4 Differential expression analysis Differentially expressed genes (DEGs) between AAA and control groups were identified using DESeq2 (v3.4.1) [ 20 ], with thresholds of P 2. The top 10 DEGs with the most significant variation were visualized using a volcano plot generated by the ggpubr package (v3.3.6) [ 21 ], and a heatmap was constructed using the ComplexHeatmap package (v2.14.0) [ 22 ]. 2.5 Enrichment analysis and protein-protein interaction (PPI) network construction To explore the biological relevance of DEGs, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using the ClusterProfiler package (v4.2.2) [ 23 ], with significance set at P < 0.05. A protein–protein interaction (PPI) network was constructed using STRING ( https://string-db.org ) with a minimum interaction score of 0.4 and visualized in Cytoscape (v3.9.1) [ 24 ]. Candidate hub genes were identified by intersecting the top 40 genes ranked by five algorithms—degree, maximum neighborhood component (DMNC), maximum clique centrality (MCC), neighborhood component centrality (MNC), and edge percolated component (EPC) [ 25 ]. The intersecting genes were considered key candidates for further analysis, and their expression levels were assessed using the Wilcoxon test. 2.6 Chromosomal distribution and subcellular localization To further investigate the genomic and spatial context of the identified key genes, RCircos (v1.2.0) [ 26 ] was used to map their chromosomal locations. Organ- and tissue-specific expression patterns were explored via the BioGPS database ( https://biogps.org ), and a tissue–gene expression network was visualized in Cytoscape (v3.9.1). The ggplot2 package (v3.4.1) [ 27 ] was employed to visualize the results. 2.7 Gene Set Enrichment Analysis (GSEA) Gene Set Enrichment Analysis (GSEA) is a statistical approach used to determine whether predefined gene sets exhibit significant, coordinated differences between two biological states. By evaluating gene set enrichment, GSEA reveals the biological pathways through which key genes may exert their function, offering valuable insight into the underlying mechanisms of disease. In this study, GSEA was applied to elucidate the functional roles of key genes in AAA. Gene sets corresponding to rat KEGG C2 pathways were retrieved from the Molecular Signatures Database (MSigDB, https://www.gsea-msigdb.org/gsea/msigdb ). Spearman correlation analysis was performed between key genes and all other genes using the psych package (v2.2.9) [ 28 ], and genes were ranked by correlation coefficients (|NES| >1, FDR 0.3, P adj < 0.05). GSEA was performed using the ClusterProfiler package (v4.2.2) ( P < 0.05), and results were visualized with the enrichplot package (v3.18.1) [ 29 ]. 2.8 Regulatory network construction To elucidate the upstream regulatory mechanisms governing key gene expression, integrated lncRNA-miRNA-mRNA and TF-miRNA-mRNA networks were constructed. Predicted interactions between key genes and associated miRNAs and lncRNAs were retrieved from the miRDB database ( http://www.mirdb.org/ ) using the multiMiR package (v1.16.0) [ 30 ]. Transcription factors (TFs) targeting key genes were predicted via the NetworkAnalyst platform ( https://www.networkanalyst.ca/ ) using data from the JASPAR database ( https://jaspar.elixir.no/ ). Regulatory networks were visualized using Cytoscape (v3.9.1). 2.9 Drug Prediction and molecular docking To identify potential therapeutic compounds targeting key AAA-related genes, the Drug Signatures Database (DSigDB) was queried using the enrichR package (v3.2) [ 31 ]. Structural information on candidate compounds was obtained from PubChem ( https://pubchem.ncbi.nlm.nih.gov ). Molecular docking simulations were performed to assess binding interactions between key target proteins and candidate compounds. Three-dimensional structures of key proteins were downloaded from the RCSB Protein Data Bank ( https://www.rcsb.org ), while ligand structures were retrieved from PubChem. Docking simulations were used to calculate binding affinities, with a total score |>7.0| considered indicative of strong binding interactions. This approach allowed the prediction of stable drug-target complexes. Additionally, to explore the broader disease relevance of key genes, associations with other diseases were assessed using the disgenet2r package (v0.0.9) [ 32 ], with data sourced from the DisGeNET database ( https://disgenet.com/ ). 2.10 Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) Total RNA was extracted from five pairs of tissue samples (50 mg each) using TRIzol reagent (Ambion, 15596-018CN). Samples were thoroughly homogenized in 1 mL TRIzol and incubated on ice for 10 minutes to ensure complete cell lysis. Following this, 300 µL of chloroform (Chengdu Guearda Adhesive Co., Ltd.) was added, and the mixture was vigorously shaken for 30 seconds, then allowed to stand at room temperature for 10 minutes to facilitate phase separation. The samples were centrifuged at 12,000 × g for 15 minutes at 4°C, and the upper aqueous phase containing RNA was carefully collected. In case of over-aspiration, the solution was gently expelled to preserve RNA integrity. An equal volume of ice-cold isopropanol (Chengdu Kelong Chemicals Co., Ltd., 67-63-0) was added to the aqueous phase, followed by gentle inversion to mix and incubation at room temperature for 10 minutes. RNA was pelleted by centrifugation at 12,000 × g for 10 minutes at 4°C. The pellet was then washed twice with 75% ethanol (Chengdu Kelong Chemicals Co., Ltd., 64-17-5) to remove impurities. After discarding the supernatant, the tubes were inverted on blotting paper, and residual ethanol was carefully removed using a 10 µL pipette tip without disturbing the pellet. The RNA was air-dried until residual moisture evaporated and then dissolved in RNase-free water. RNA concentration was quantified using a NanoPhotometer N50, and samples were adjusted accordingly for subsequent reverse transcription. For cDNA synthesis, a SureScript First-Strand cDNA Synthesis Kit (Tlr9, Xavier Co., Ltd.) was used. Reagents were combined according to the manufacturer’s instructions and briefly centrifuged. The reverse transcription reaction was carried out under the following conditions: 25°C for 5 minutes, 50°C for 15 minutes, 85°C for 5 seconds, followed by a hold at 4°C. For qPCR, the resulting cDNA was diluted 5- to 20-fold with RNase/DNase-free ddH₂O. The qPCR reaction mixture was prepared and amplified using a CFX96 Real-Time PCR Detection System (Bio-Rad, XLFZ006) under the following thermal cycling conditions: initial denaturation at 95°C for 1 minute, followed by 40 cycles of denaturation at 95°C for 20 seconds, annealing at 55°C for 20 seconds, and extension at 72°C for 30 seconds. Melting curves were generated, and Ct values were recorded for downstream analysis. Primer sequences used in this study are provided in Supplementary Table 1. 2.11 Statistical analysis of data All statistical analyses were performed using Cytoscape 3.9.1 and R software version 4.1.0. Statistical significance was determined using either Student’s t -test or the Wilcoxon test, as appropriate. A P -value < 0.05 was considered statistically significant. 3 Results 3.1 Functional Enrichment and PPI analysis of 400 DEGs Following transcriptome assembly using Cufflinks, four AAA and five control samples with optimal alignment rates (70%–90%) were selected for downstream analysis (Supplementary Table 2). Differential expression analysis identified 400 differentially expressed genes (DEGs) between the AAA and control groups, with 314 upregulated and 86 downregulated in the AAA group ( P 2) (Fig. 1 A, 1 B). Gene Ontology (GO) enrichment analysis of the DEGs revealed 1,291 biological process (BP) terms, 87 cellular component (CC) terms, and 97 molecular function (MF) terms. DEGs were predominantly involved in the BP of "positive regulation of response to external stimulus ", enriched in the CC term "external side of plasma membrane", and associated with the MF term "cell adhesion molecule binding" (Fig. 1 C). Additionally, 61 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were significantly enriched, with the PI3K-Akt signaling pathway showing the most notable enrichment ( P < 0.05) (Fig. 1 D). PPI network analysis of the 400 DEGs yielded 324 nodes and 1,638 edges (Fig. 1 E). Through integration of five topological algorithms—degree, DMNC, MCC, MNC, and EPC—five hub genes were identified: Fcgr2b , Tlr7 , Clec7a , Tlr9 , and Cd53 (Fig. 1 F). The Wilcoxon test confirmed that all five genes were significantly upregulated in the AAA group ( P < 0.05), with Cd53 exhibiting the highest expression level (Fig. 1 G). 3.2 Chromosomal distribution and subcellular localization of key genes Chromosomal mapping showed that Fcgr2b was located on chromosome 13, Tlr7 on chromosome X, Clec7a on chromosome 4, Tlr9 on chromosome 8, and Cd53 on chromosome 2 (Fig. 2 A). Analysis of tissue-specific expression patterns revealed that Fcgr2b , Tlr7 , and Cd53 were highly expressed in spleen tissue, while Cd53 and Fcgr2b were also prominently expressed in bone marrow (Fig. 2 B). Subcellular localization data indicated that Fcgr2b , Tlr7 , Clec7a , and Tlr9 were primarily cytoplasmic, whereas Cd53 was mainly localized in the nucleus (Fig. 2 C). 3.3 GSEA analysis of hub genes To further elucidate the biological functions of the identified hub genes, gene set enrichment analysis (GSEA) was conducted. The results demonstrated that Cd53 , Fcgr2b , and Tlr9 were significantly enriched in pathways related to leukocyte transendothelial migration and regulation of the actin cytoskeleton. Meanwhile, Clec7a and Tlr7 were associated with pathways involving the cell cycle, DNA replication, and glycan degradation (Fig. 3 ). 3.4 Molecular regulatory networks of key genes To explore the molecular regulatory mechanisms of Clec7a and Cd53 , both lncRNA–miRNA–mRNA and transcription factor (TF)–miRNA–mRNA interaction networks were constructed. Clec7a was predicted to interact with nine miRNAs, including rno-miR-377-5p, and seven corresponding lncRNAs, such as Fndc1 . In contrast, Cd53 was associated with only two miRNAs: rno-miR-325-3p and rno-miR-124-5p (Fig. 4 A). In the TF–miRNA–mRNA regulatory network, 5, 12, 13, 9, and 12 transcription factors were identified for Fcgr2b , Tlr7 , Clec7a , Tlr9 , and Cd53 , respectively. Notably, JUN was a shared regulator among Tlr7 , Fcgr2b , and Clec7a , while GATA2 was predicted to regulate Tlr9 , Cd53 , and Clec7a (Fig. 4 B). 3.5 Drug and disease association of hub genes To identify candidate drugs targeting AAA, drug–gene interaction predictions were performed using the DSigDB database. A total of 131 compounds were predicted to interact with the five key genes (Table 1 ). Among them, AGN-PC-0JHFVD was predicted to target Cd53 , Tlr9 , Tlr7 , and Fcgr2b , while diphenylpyraline was predicted to target Cd53 , Tlr9 , and Tlr7 . Diphenylpyraline was selected as the most promising candidate based on its statistically significant P -value (Fig. 5 A). Table 1 Key genes are predicted to be involved in the development of drugs for AAA therapy. Term P -value Genes AGN-PC-0JHFVD 4.00005E-08 Cd53;Tlr9;Tlr7;Fcgr2b Diphenylpyraline 5.75026E-06 Cd53;Tlr9;Tlr7 Isoguanine 8.58731E-06 Cd53;Tlr9;Tlr7 IMIQUIMOD 2.02451E-05 Tlr9;Tlr7 Chloroquine 2.47256E-05 Tlr9;Tlr7 Inosinic acid 5.15228E-05 Tlr9;Tlr7 Beta-D-allopyranose 0.0002306 Tlr9;Tlr7 Chloroquine 0.00023538 Tlr9;Tlr7 Trimethoprim 0.000890245 Cd53;Tlr9 Leukotriene 0.004243144 Clec7a Molecular docking simulations indicated that diphenylpyraline formed stable binding conformations with Cd53 , Tlr9 , and Tlr7 , with binding scores of − 9.2, − 7.1, and − 7.4, respectively (Table 2 ). Key amino acid interactions are illustrated in Figs. 5 B– 5 D. Table 2 The total scores of diphenylpyraline with Cd53 , Tlr9 , and Tlr7 . Key Genes - Key Active Ingredients Total Score Center Diphenylpyraline- Cd53 -9.2 29, 53, 86 Diphenylpyraline- Tlr9 -7.1 78, 53, 20 Diphenylpyraline- Tlr7 -7.4 -26, -11, -11 Disease prediction analysis revealed that Fcgr2b , Tlr7 , Clec7a , Tlr9 , and Cd53 were associated with 130, 276, 126, 457, and 17 diseases, respectively. Notably, both Fcgr2b and Cd53 were linked to neoplastic diseases, indicating their potential roles as diagnostic biomarkers for neoplasms (Fig. 5 E). 3.6 Experimental validation of gene expression RT-qPCR was employed to validate the expression levels of the five hub genes in AAA and control tissue samples. The results confirmed that Fcgr2b , Clec7a , Tlr9 , and Cd53 were significantly upregulated in the AAA group ( P 0.05), its expression level remained elevated compared to the control group. These results were largely consistent with transcriptomic findings (Fig. 6 A–E). 4 Discussion In this study, we employed comprehensive transcriptomic and bioinformatics analyses to elucidate the molecular underpinnings of AAA, identifying five key genes— Fcgr2b, Tlr7, Clec7a, Tlr9 , and Cd53 —that may serve as critical regulators in AAA pathogenesis. These findings offer a novel theoretical foundation for advancing our understanding of AAA and may guide future diagnostic and therapeutic strategies. Fcgr2b, a low-affinity inhibitory receptor for the Fc region of IgG, has been previously identified as an independent prognostic marker in glioma via computational analyses [ 33 ]. Beyond its role in oncology, Fcgr2b deficiency is associated with exacerbated vascular inflammation and increased vulnerability to immune-mediated vascular damage, as shown in models of atherosclerosis and systemic lupus erythematosus [ 34 ]. These studies underscore its role in immune homeostasis and vascular integrity. Although Fcgr2b has been implicated in vascular inflammation [ 34 ], a direct link to leukocyte transendothelial migration in AAA has not been established. Notably, our enrichment analysis revealed that Fcgr2b is significantly associated with the leukocyte transendothelial migration pathway, offering new insights into its potential function in modulating immune infiltration and disease progression in AAA. TLR7, a Toll-like receptor that detects single-stranded RNA, initiates robust innate and adaptive immune responses via downstream signaling cascades. TLR7 overactivation has been linked to autoimmune conditions by enhancing B cell longevity and type I interferon production [ 35 ]. In vascular diseases, TLR7 contributes to endothelial dysfunction and immune cell infiltration, fostering chronic inflammation [ 36 ]. These immune responses mirror those observed in AAA, particularly regarding leukocyte recruitment, cytokine release, and extracellular matrix degradation, suggesting that TLR7 may act as a molecular bridge between innate immune sensing and vascular remodeling in aneurysmal development. Clec7a (Dectin-1), a C-type lectin receptor predominantly expressed in myeloid cells, plays a pivotal role in fungal recognition and macrophage modulation. It has been shown to influence macrophage polarization and the resolution of inflammation in renal injury models [ 37 ]. More recently, Clec7a has been implicated in atherosclerosis through its regulation of foam cell formation and proinflammatory macrophage phenotypes [38]. Given the overlap in inflammatory mechanisms between atherosclerosis and AAA, Clec7a may similarly contribute to AAA by modulating immune cell behavior and cytokine secretion in the aortic wall microenvironment. TLR9, another endosomal Toll-like receptor, recognizes unmethylated CpG motifs and promotes inflammatory responses upon activation [ 39 ]. Its role in vascular pathology is gaining recognition, with evidence showing that TLR9 activation in endothelial and smooth muscle cells heightens inflammatory signaling [ 40 ]. Given the sustained immune activation and vascular stress characteristic of AAA, TLR9 may exacerbate pathologic remodeling via inflammasome activation and proinflammatory cytokine expression. Thus, therapeutic targeting of TLR9 could present a viable approach to mitigating aneurysmal progression. CD53, a member of the tetraspanin family, is widely expressed in hematopoietic cells and plays a crucial role in immune cell signaling. Cd53 deficiency compromises lymphocyte function and increases susceptibility to infection, underscoring its immunoregulatory role [ 41 – 42 ]. While its role in vascular pathology remains underexplored, Cd53 may influence AAA development through its effects on leukocyte adhesion, migration, and antigen presentation—processes known to be dysregulated in aneurysmal tissue. Interestingly, Clec7a and Tlr7 were also enriched in pathways related to DNA replication and cell cycle regulation, suggesting a potential role in genomic stability within vascular cells. Replication stress is increasingly recognized as a contributor to vascular dysfunction, particularly under conditions of oxidative stress and chronic inflammation. This stress activates the ATR kinase, which orchestrates DNA damage responses by coordinating repair pathways through mediators such as TopBP1 and ETAA1 [ 43 – 45 ]. ETAA1, in particular, serves as a critical ATR activator in response to stalled replication forks [ 46 ]. Together with ATM, ATR governs homologous recombination and suppresses deleterious DNA structures [ 47 ]. These mechanisms may be relevant in AAA, where VSMCs exhibit phenotypic modulation, senescence, and apoptosis—hallmarks often driven by DNA damage responses. In silico drug prediction identified diphenylpyraline, an antihistamine with anticholinergic properties [ 48 ], as a promising therapeutic agent due to its high binding affinity with Cd53 , Tlr9 , and Tlr7 . Given that AAA is driven by chronic inflammation [ 49 ], and that inflammatory cell infiltration promotes aortic wall degradation through the release of cytokines such as TNF-α and IL-6 [ 50 – 51 ], diphenylpyraline may exert protective effects by modulating histamine receptors and suppressing inflammatory cell activity. Nevertheless, this study has several limitations. The relatively small sample size may limit the generalizability and statistical robustness of our findings. Additionally, we did not assess the correlation between gene expression levels and aortic diameter, a key clinical parameter. Furthermore, the mechanistic roles of the identified genes and diphenylpyraline remain to be experimentally validated. To address these gaps, future studies will expand the sample size to enhance statistical power and incorporate aortic imaging data to correlate gene expression with aneurysm dimensions. We also plan to conduct cellular assays to investigate the specific roles of key genes (e.g., Fcgr2b ) in inflammation and cell migration, and to explore their functions through gene knockout or overexpression in animal models. Moreover, in vivo pharmacodynamic studies of diphenylpyraline will be performed to evaluate its efficacy in suppressing AAA progression and to elucidate its molecular interactions with the identified gene targets, thereby laying the groundwork for clinical translation. 5 Conclusion This study is the first to systematically identify and characterize the roles of Fcgr2b, Tlr7, Clec7a, Tlr9 , and Cd53 in AAA. Through integrative functional enrichment and network analyses, we reveal their involvement in immune cell infiltration, DNA replication stress, and inflammatory regulation—mechanisms that are central to AAA pathogenesis. These findings provide a novel molecular framework for understanding AAA and open new avenues for biomarker discovery and targeted therapy. Declarations Data availability statement The datasets generated and/or analysed during the current study are available in the Genome Sequence Archive (GSA) repository, accession number CRA033718 (https://ngdc.cncb.ac.cn/gsa). Ethics approval and consent to participate All animal-related procedures were conducted in accordance with the International Guiding Principles for Biomedical Research Involving Animals and were reviewed and approved by the Ethics Committee for Animal Experiments of Kunming Medical University (Approval No. 2014YYGJ116). Author contributions RK: Data curation, Formal Analysis, Methodology, Writing original draft, Writing review & editing. JT: Data curation, Formal Analysis, Methodology, Writing original draft, Writing review & editing. HY: Investigation, Software, Validation, Writing original draft. XL: Investigation, Software, Validation, Writing original draft. YJ:Conceptualization, Formal Analysis, Methodology, Writing original draft. RL: Supervision, Validation, Writing review & editing. YW: Supervision, Validation, Writing review & editing. KG:Supervision, Visualization, Writing review & editing. XC: Project administration, Resources, Validation, Writing review & editing. PZ: Project administration, Resources, Validation, Writing review & editing. Funding This work was supported by grants from the Open Project of the Clinical Medical Center of the First People's Hospital of Yunnan Province (Grant Nos. 2022LCZXKF-XG02). Acknowledgments We would like to acknowledge GEO database for providing data. We also express our gratitude to researchers for their generous contribution of microarray datasets and to the creators of the web resources and data processing tools employed in this research. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Supplementary material The Supplementary Material for this article can be found online at: XXX. Consent for publication Not applicable. References Golledge J, Thanigaimani S, Powell JT, Tsao PS. Pathogenesis and management of abdominal aortic aneurysm. Eur Heart J. 2023;44(29):2682–97. 10.1093/eurheartj/ehad386 . Czerny M, Beyersdorf F. Abdominal Aortic Aneurysm. Dtsch Arztebl Int. 2020;117(48):811–2. 10.3238/arztebl.2020.0811 . Cho MJ, Lee MR, Park JG. Aortic aneurysms: current pathogenesis and therapeutic targets. Exp Mol Med. 2023;55(12):2519–30. 10.1038/s12276-023-01130-w . 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Li J, Krishna SM, Golledge J. The Potential Role of Kallistatin in the Development of Abdominal Aortic Aneurysm. Int J Mol Sci. 2016;17(8):1312. 10.3390/ijms17081312 . Additional Declarations No competing interests reported. Supplementary Files supplementaryTable3.xlsx SupplementaryMaterial02.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. 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16:05:24","extension":"xml","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":148875,"visible":true,"origin":"","legend":"","description":"","filename":"9e86c7bceee746d5a0960ffb521febbe1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7886883/v1/14a641b3910441282b188849.xml"},{"id":97259735,"identity":"1e6dfbf3-d426-4719-8aea-cea18cf315da","added_by":"auto","created_at":"2025-12-02 13:54:04","extension":"html","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":163605,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7886883/v1/06639954877793d76e45185d.html"},{"id":97367884,"identity":"a52efbbc-1811-479a-8936-17fcce28f208","added_by":"auto","created_at":"2025-12-03 16:20:58","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2003069,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification and analysis of DEGs in AAA.\u003c/strong\u003e (A) Volcano plot of differentially expressed genes (DEGs) between abdominal aortic aneurysm (AAA) and control tissues, highlighting significantly upregulated (orange) and downregulated (blue) genes. (B) Heatmap and distribution plot of representative DEGs illustrating distinct expression patterns across all samples. (C) Gene Ontology (GO) enrichment analysis of DEGs classified into biological processes (BP), cellular components (CC), and molecular functions (MF), with up- and downregulated genes distinctly color-coded. (D) KEGG pathway enrichment analysis identifies significantly altered signaling pathways. (E) Protein–protein interaction (PPI) network constructed via STRING and visualized in Cytoscape, with core hub genes marked in magenta. (F) Venn diagram showing the intersection of five hub genes identified through five topological algorithms (MCC, DMNC, MNC, Degree, EPC). (G) Box plots comparing the expression levels of \u003cem\u003eCd53, Clec7a, Fcgr2b, Tlr7, \u003c/em\u003eand\u003cem\u003e Tlr9\u003c/em\u003ebetween AAA and control groups. *\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7886883/v1/b7ec9722a2eb95cbbab67879.jpg"},{"id":97259716,"identity":"c5792f71-0be2-4b74-86f6-ab37ba8f5055","added_by":"auto","created_at":"2025-12-02 13:54:04","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":479238,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenomic localization, tissue-specific expression, and subcellular distribution of hub genes.\u003c/strong\u003e (A) Circular plot indicating the chromosomal positions of \u003cem\u003eCd53, Clec7a, Fcgr2b, Tlr7, \u003c/em\u003eand\u003cem\u003e Tlr9\u003c/em\u003e in the rat genome. (B) Gene–tissue interaction network depicting the expression patterns of hub genes across various rat tissues, including immune and neural regions such as the spleen, thymus, hippocampus, and striatum. Hub genes are shown as pink triangles, and tissue types are labeled with green rectangles. (C) Bar plot summarizing subcellular localization predictions for each hub gene, including nuclear, cytoplasmic, mitochondrial, endoplasmic reticulum, and extracellular compartments. Different colors represent distinct subcellular localizations.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7886883/v1/19322010148ca1eae9b0df2d.jpg"},{"id":97259713,"identity":"9a093b2a-b5d3-47f9-a04a-a706a8a2f1ff","added_by":"auto","created_at":"2025-12-02 13:54:04","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":893822,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene set enrichment analysis (GSEA) of hub genes.\u003c/strong\u003e (A–E) SEA plots illustrating biological pathways enriched in samples with high expression of \u003cem\u003eCd53, Clec7a, Fcgr2b, Tlr7, \u003c/em\u003eand\u003cem\u003e Tlr9\u003c/em\u003e. These hub genes are predominantly involved in immune and inflammatory processes, including leukocyte transendothelial migration, lysosomal pathways, actin cytoskeleton regulation, Fcγ receptor-mediated signaling, and Toll-like receptor pathways. Several pathways related to cell cycle progression, DNA replication, and glycan metabolism were negatively enriched. Each plot shows the enrichment score (y-axis) versus the gene ranking (x-axis), with the peak indicating the core enriched gene set.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7886883/v1/a67557165e7ec315ebcdea97.jpg"},{"id":97367144,"identity":"8b40a1dc-1886-49c2-a18c-9e639b4651df","added_by":"auto","created_at":"2025-12-03 16:17:01","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":625611,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegulatory network of hub genes with miRNAs and transcription factors. \u003c/strong\u003e(A) miRNA–mRNA interaction network displaying predicted miRNA regulators (orange ellipses) targeting \u003cem\u003eClec7a\u003c/em\u003e and \u003cem\u003eCd53\u003c/em\u003e (pink diamonds). Green triangles denote downstream genes affected by these miRNAs. (B) Integrated network of miRNAs (orange ellipses), transcription factors (TFs; blue squares), and hub genes (pink diamonds) demonstrates multilayered regulatory control over \u003cem\u003eCd53, Clec7a, Fcgr2b, Tlr7, \u003c/em\u003eand\u003cem\u003e Tlr9\u003c/em\u003e. Notably, \u003cem\u003eClec7a\u003c/em\u003e emerges as a central node targeted by multiple miRNAs and TFs, suggesting a pivotal regulatory role.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7886883/v1/b3988cac58131c388e777479.jpg"},{"id":97367204,"identity":"d7da6038-3ca2-4744-b742-0d04fe205e81","added_by":"auto","created_at":"2025-12-03 16:17:26","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":407937,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDrug structure and disease association analysis of hub genes.\u003c/strong\u003e (A) Chemical structure of diphenylpyraline, a candidate compound predicted to interact with hub gene products. (B) Gene–disease association network revealing links between \u003cem\u003eCd53, Clec7a, Fcgr2b, Tlr7, \u003c/em\u003eand\u003cem\u003e Tlr9\u003c/em\u003e (pink diamonds) and a range of diseases (blue hexagons). \u003cem\u003eTlr7\u003c/em\u003e and \u003cem\u003eTlr9\u003c/em\u003e are associated with inflammatory and immune disorders, including systemic lupus erythematosus and Crohn’s disease. \u003cem\u003eClec7a\u003c/em\u003e is linked to fungal infections, while \u003cem\u003eCd53\u003c/em\u003e and \u003cem\u003eFcgr2b\u003c/em\u003e are implicated in tumorigenesis and autoimmune diseases.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7886883/v1/a64f11b288f7d45f7f71753f.jpg"},{"id":97366882,"identity":"fab23226-7457-40c6-9635-fad6dba383d3","added_by":"auto","created_at":"2025-12-03 16:12:01","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":239558,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelative mRNA expression levels of hub genes in control and AAA groups.\u003c/strong\u003e (A) \u003cem\u003eFgfr2b\u003c/em\u003e expression is significantly elevated in AAA compared to controls (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). (B) No significant difference is observed in \u003cem\u003eTlr7\u003c/em\u003e expression between the two groups (ns, not significant). (C) \u003cem\u003eCLEC7a\u003c/em\u003eexpression is significantly upregulated in AAA (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). (D) \u003cem\u003eTlr9\u003c/em\u003eexpression is also significantly increased in AAA samples (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). (E) \u003cem\u003eCd53\u003c/em\u003e expression is significantly higher in AAA than in controls (\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05). All results are presented as mean ± standard deviation, with statistical significance evaluated using a \u003cem\u003et\u003c/em\u003e-test (**\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Figure61.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7886883/v1/478506aa268654049ae67b5e.jpg"},{"id":103490245,"identity":"f6a2d056-73e6-4d65-937b-ff085f4743b4","added_by":"auto","created_at":"2026-02-26 09:43:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5939351,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7886883/v1/375bde07-bda7-4df4-89a6-d09e886b76e8.pdf"},{"id":97259718,"identity":"f0c1a30e-6b04-49ae-bf25-7853c66067d8","added_by":"auto","created_at":"2025-12-02 13:54:04","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1227095,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryTable3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7886883/v1/faeb13f228190d2dd7339d56.xlsx"},{"id":97259748,"identity":"7bfbb673-8969-473d-a709-22212ae31aed","added_by":"auto","created_at":"2025-12-02 13:54:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1736888,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial02.docx","url":"https://assets-eu.researchsquare.com/files/rs-7886883/v1/71d9ea8e3f427c8572f38d0a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of diagnostic biomarkers and therapeutic targets for abdominal aortic aneurysm via transcriptome sequencing and integrated bioinformatics","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAbdominal aortic aneurysm (AAA) is a life-threatening vascular disorder characterized by the localized dilation of the abdominal aorta, posing a major risk to cardiovascular health [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Clinically, AAA is defined as an aortic diameter at least 1.5 times greater than normal at the level of the renal arteries, typically exceeding 3.0 cm [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. AAAs are generally classified as either saccular (localized) or fusiform (circumferential), with the fusiform type accounting for more than 90% of cases. The clinical consequences of AAA are often severe, including compression of surrounding organs, abdominal pain, and, most critically, aortic rupture, which leads to hemorrhagic shock and high mortality [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite advances in open surgical repair and endovascular aneurysm repair (EVAR), the mortality rate following AAA rupture remains alarmingly high. Furthermore, current therapeutic strategies do not adequately prevent AAA recurrence or progression [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As a result, increasing attention has shifted toward elucidating the molecular mechanisms of AAA to uncover reliable diagnostic biomarkers and novel therapeutic targets [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Inflammation has emerged as a central contributor to AAA pathogenesis, with immune cell infiltration\u0026mdash;particularly by T cells, macrophages, and B cells\u0026mdash;promoting structural weakening of the aortic wall [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Although inflammation and immune dysregulation are well-established features of AAA, the underlying gene regulatory networks and their interactions remain insufficiently characterized.\u003c/p\u003e\u003cp\u003eAnimal models have played a critical role in elucidating AAA pathophysiology [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Refinement of these models is ongoing, aiming to better recapitulate the complexity of human AAA and enhance translational relevance [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Commonly used experimental models in mice, rats, and rabbits employ methods such as angiotensin II (AngII) infusion, elastase perfusion, and calcium chloride application to induce aneurysm formation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These approaches have not only advanced AAA research but also contributed to investigations into various diseases, including diabetes, obesity, neurological disorders, and cancer [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eComplementing \u003cem\u003ein vivo\u003c/em\u003e studies, bioinformatics has become an essential tool for analyzing high-throughput omics data, including those generated from transcriptomic studies[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Bioinformatic analyses enable the identification of key genes, signaling pathways, and molecular signatures implicated in AAA, offering new opportunities for mechanistic insight and therapeutic development [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The integration of RNA sequencing (RNA-seq) with computational analysis has proven especially effective for detecting novel exons, quantifying gene expression, and characterizing alternative splicing events [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These technologies have opened new avenues for understanding AAA at the molecular level and for identifying actionable targets for diagnosis and treatment.\u003c/p\u003e\u003cp\u003eBuilding on these advancements, the present study aims to identify key regulatory genes involved in AAA pathogenesis through a comprehensive approach integrating RNA-seq with differential gene expression and protein\u0026ndash;protein interaction network analyses [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. To further elucidate the biological roles of these genes, we employed subcellular and chromosomal localization profiling, as well as Gene Set Enrichment Analysis (GSEA). In addition, we constructed lncRNA-miRNA-mRNA and TF-miRNA-mRNA regulatory networks to explore upstream modulators of these key targets. Finally, drug prediction analysis was performed to identify potential small-molecule therapeutics that interact with the identified targets. Through this multidimensional framework, we aim to provide a detailed understanding of AAA molecular pathology and to identify novel candidates for clinical application in diagnosis and therapy.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Establishment of the Abdominal Aortic Aneurysm Animal Model and Tissue Collection\u003c/h2\u003e\u003cp\u003eMale Sprague\u0026ndash;Dawley (SD) rats (4\u0026ndash;6 weeks old, 280\u0026ndash;310 g) were obtained from Kunming Medical University and housed under controlled environmental conditions (22\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026deg;C, 60% relative humidity, 12-hour light/dark cycle) with free access to food and water. All animal procedures were conducted in accordance with the International Guiding Principles for Biomedical Research Involving Animals and were approved by the Ethics Committee for Animal Experiments at Kunming Medical University (Approval No. 2014YYGJ116).\u003c/p\u003e\u003cp\u003eA total of six rats were used to induce abdominal aortic aneurysm (AAA) via intra-aortic perfusion of porcine pancreatic elastase (0.4 U/\u0026micro;L; Sigma-Aldrich, S\u0026oslash;borg, Denmark). Under general anesthesia induced with isoflurane (3\u0026ndash;5% in oxygen) and maintained at \u0026ge;\u0026thinsp;5% via a nose cone, a midline laparotomy was performed to expose the abdominal aorta. The targeted segment was temporarily clamped and perfused with elastase for 30 minutes to degrade elastin and promote aneurysmal dilation. One rat died during the perioperative period, while the remaining five were monitored postoperatively under standard care conditions. On day 14, all surviving animals were humanely euthanized under deep isoflurane anesthesia. Depth of anesthesia was confirmed by loss of the righting reflex and absence of pedal reflex before continuing high-concentration isoflurane exposure (\u0026ge;\u0026thinsp;5%) until respiratory arrest occurred. Death was verified by the absence of respiration and heartbeat. AAA formation was confirmed by abdominal ultrasonography and defined as a\u0026thinsp;\u0026ge;\u0026thinsp;50% increase in aortic diameter relative to baseline. Aneurysmal aortic tissues were collected from the five surviving rats, along with abdominal aortic tissues from five healthy controls [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and subsequently subjected to transcriptome sequencing.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 RNA Extraction and library construction\u003c/h2\u003e\u003cp\u003eTotal RNA was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) following the manufacturer\u0026rsquo;s instructions. RNA quantity and purity were assessed using a NanoDrop ND-1000 spectrophotometer (NanoDrop, Wilmington, DE, USA). Reverse transcription of cleaved RNA fragments into cDNA was performed using SuperScript\u0026trade; II Reverse Transcriptase (Invitrogen, Cat. No. 1896649, USA). After adapter ligation and PCR amplification, sequencing libraries were constructed, and 2 \u0026times; 150 bp paired-end reads (PE150) were generated using the Illumina NovaSeq\u0026trade; 6000 platform (LC-Bio Technology Co., Ltd., Hangzhou, China).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Data processing\u003c/h2\u003e\u003cp\u003eInitial quality control of raw sequencing reads was performed using FastQC (v0.11.5), and base error rates were calculated with Phred. Clean reads were aligned to the rat reference genome using HISAT2 (Supplementary Fig.\u0026nbsp;1). The genome and annotation files were retrieved from the Ensembl database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://asia.ensembl.org/info/about/species.html\u003c/span\u003e\u003cspan address=\"https://asia.ensembl.org/info/about/species.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Transcript assembly and gene quantification were carried out using Cufflinks (v2.2.1), yielding count data for downstream analysis (Supplementary Table\u0026nbsp;1). To assess data dispersion and inter-group consistency, principal component analysis (PCA) was performed using the scatterplot3d package (v0.3-44) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] (Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Differential expression analysis\u003c/h2\u003e\u003cp\u003eDifferentially expressed genes (DEGs) between AAA and control groups were identified using DESeq2 (v3.4.1) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], with thresholds of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log\u003csub\u003e2\u003c/sub\u003e fold change| \u0026gt;2. The top 10 DEGs with the most significant variation were visualized using a volcano plot generated by the ggpubr package (v3.3.6) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and a heatmap was constructed using the ComplexHeatmap package (v2.14.0) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Enrichment analysis and protein-protein interaction (PPI) network construction\u003c/h2\u003e\u003cp\u003eTo explore the biological relevance of DEGs, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using the ClusterProfiler package (v4.2.2) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], with significance set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. A protein\u0026ndash;protein interaction (PPI) network was constructed using STRING (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org\u003c/span\u003e\u003cspan address=\"https://string-db.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with a minimum interaction score of 0.4 and visualized in Cytoscape (v3.9.1) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Candidate hub genes were identified by intersecting the top 40 genes ranked by five algorithms\u0026mdash;degree, maximum neighborhood component (DMNC), maximum clique centrality (MCC), neighborhood component centrality (MNC), and edge percolated component (EPC) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The intersecting genes were considered key candidates for further analysis, and their expression levels were assessed using the Wilcoxon test.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Chromosomal distribution and subcellular localization\u003c/h2\u003e\u003cp\u003eTo further investigate the genomic and spatial context of the identified key genes, RCircos (v1.2.0) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] was used to map their chromosomal locations. Organ- and tissue-specific expression patterns were explored via the BioGPS database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biogps.org\u003c/span\u003e\u003cspan address=\"https://biogps.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and a tissue\u0026ndash;gene expression network was visualized in Cytoscape (v3.9.1). The ggplot2 package (v3.4.1) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] was employed to visualize the results.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Gene Set Enrichment Analysis (GSEA)\u003c/h2\u003e\u003cp\u003eGene Set Enrichment Analysis (GSEA) is a statistical approach used to determine whether predefined gene sets exhibit significant, coordinated differences between two biological states. By evaluating gene set enrichment, GSEA reveals the biological pathways through which key genes may exert their function, offering valuable insight into the underlying mechanisms of disease. In this study, GSEA was applied to elucidate the functional roles of key genes in AAA. Gene sets corresponding to rat KEGG C2 pathways were retrieved from the Molecular Signatures Database (MSigDB, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Spearman correlation analysis was performed between key genes and all other genes using the psych package (v2.2.9) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and genes were ranked by correlation coefficients (|NES| \u0026gt;1, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.25). Adjusted \u003cem\u003eP\u003c/em\u003e-values were calculated using the Benjamini-Hochberg method (|correlation coefficient| \u0026gt;0.3, \u003cem\u003eP\u003c/em\u003e\u003csub\u003eadj\u003c/sub\u003e \u0026lt; 0.05). GSEA was performed using the ClusterProfiler package (v4.2.2) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and results were visualized with the enrichplot package (v3.18.1) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Regulatory network construction\u003c/h2\u003e\u003cp\u003eTo elucidate the upstream regulatory mechanisms governing key gene expression, integrated lncRNA-miRNA-mRNA and TF-miRNA-mRNA networks were constructed. Predicted interactions between key genes and associated miRNAs and lncRNAs were retrieved from the miRDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.mirdb.org/\u003c/span\u003e\u003cspan address=\"http://www.mirdb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using the multiMiR package (v1.16.0) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Transcription factors (TFs) targeting key genes were predicted via the NetworkAnalyst platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.networkanalyst.ca/\u003c/span\u003e\u003cspan address=\"https://www.networkanalyst.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using data from the JASPAR database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://jaspar.elixir.no/\u003c/span\u003e\u003cspan address=\"https://jaspar.elixir.no/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Regulatory networks were visualized using Cytoscape (v3.9.1).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Drug Prediction and molecular docking\u003c/h2\u003e\u003cp\u003eTo identify potential therapeutic compounds targeting key AAA-related genes, the Drug Signatures Database (DSigDB) was queried using the enrichR package (v3.2) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Structural information on candidate compounds was obtained from PubChem (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMolecular docking simulations were performed to assess binding interactions between key target proteins and candidate compounds. Three-dimensional structures of key proteins were downloaded from the RCSB Protein Data Bank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), while ligand structures were retrieved from PubChem. Docking simulations were used to calculate binding affinities, with a total score |\u0026gt;7.0| considered indicative of strong binding interactions. This approach allowed the prediction of stable drug-target complexes.\u003c/p\u003e\u003cp\u003eAdditionally, to explore the broader disease relevance of key genes, associations with other diseases were assessed using the disgenet2r package (v0.0.9) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], with data sourced from the DisGeNET database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://disgenet.com/\u003c/span\u003e\u003cspan address=\"https://disgenet.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.10 Reverse transcription-quantitative polymerase chain reaction (RT-qPCR)\u003c/h2\u003e\u003cp\u003eTotal RNA was extracted from five pairs of tissue samples (50 mg each) using TRIzol reagent (Ambion, 15596-018CN). Samples were thoroughly homogenized in 1 mL TRIzol and incubated on ice for 10 minutes to ensure complete cell lysis. Following this, 300 \u0026micro;L of chloroform (Chengdu Guearda Adhesive Co., Ltd.) was added, and the mixture was vigorously shaken for 30 seconds, then allowed to stand at room temperature for 10 minutes to facilitate phase separation. The samples were centrifuged at 12,000 \u0026times; \u003cem\u003eg\u003c/em\u003e for 15 minutes at 4\u0026deg;C, and the upper aqueous phase containing RNA was carefully collected. In case of over-aspiration, the solution was gently expelled to preserve RNA integrity. An equal volume of ice-cold isopropanol (Chengdu Kelong Chemicals Co., Ltd., 67-63-0) was added to the aqueous phase, followed by gentle inversion to mix and incubation at room temperature for 10 minutes. RNA was pelleted by centrifugation at 12,000 \u0026times; \u003cem\u003eg\u003c/em\u003e for 10 minutes at 4\u0026deg;C. The pellet was then washed twice with 75% ethanol (Chengdu Kelong Chemicals Co., Ltd., 64-17-5) to remove impurities. After discarding the supernatant, the tubes were inverted on blotting paper, and residual ethanol was carefully removed using a 10 \u0026micro;L pipette tip without disturbing the pellet. The RNA was air-dried until residual moisture evaporated and then dissolved in RNase-free water. RNA concentration was quantified using a NanoPhotometer N50, and samples were adjusted accordingly for subsequent reverse transcription.\u003c/p\u003e\u003cp\u003eFor cDNA synthesis, a SureScript First-Strand cDNA Synthesis Kit (Tlr9, Xavier Co., Ltd.) was used. Reagents were combined according to the manufacturer\u0026rsquo;s instructions and briefly centrifuged. The reverse transcription reaction was carried out under the following conditions: 25\u0026deg;C for 5 minutes, 50\u0026deg;C for 15 minutes, 85\u0026deg;C for 5 seconds, followed by a hold at 4\u0026deg;C.\u003c/p\u003e\u003cp\u003eFor qPCR, the resulting cDNA was diluted 5- to 20-fold with RNase/DNase-free ddH₂O. The qPCR reaction mixture was prepared and amplified using a CFX96 Real-Time PCR Detection System (Bio-Rad, XLFZ006) under the following thermal cycling conditions: initial denaturation at 95\u0026deg;C for 1 minute, followed by 40 cycles of denaturation at 95\u0026deg;C for 20 seconds, annealing at 55\u0026deg;C for 20 seconds, and extension at 72\u0026deg;C for 30 seconds. Melting curves were generated, and \u003cem\u003eCt\u003c/em\u003e values were recorded for downstream analysis. Primer sequences used in this study are provided in Supplementary Table\u0026nbsp;1.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.11 Statistical analysis of data\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using Cytoscape 3.9.1 and R software version 4.1.0. Statistical significance was determined using either Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test or the Wilcoxon test, as appropriate. A \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Functional Enrichment and PPI analysis of 400 DEGs\u003c/h2\u003e\u003cp\u003eFollowing transcriptome assembly using Cufflinks, four AAA and five control samples with optimal alignment rates (70%\u0026ndash;90%) were selected for downstream analysis (Supplementary Table\u0026nbsp;2). Differential expression analysis identified 400 differentially expressed genes (DEGs) between the AAA and control groups, with 314 upregulated and 86 downregulated in the AAA group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |Log₂FC| \u0026gt;2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGene Ontology (GO) enrichment analysis of the DEGs revealed 1,291 biological process (BP) terms, 87 cellular component (CC) terms, and 97 molecular function (MF) terms. DEGs were predominantly involved in the BP of \"positive regulation of response to external stimulus \", enriched in the CC term \"external side of plasma membrane\", and associated with the MF term \"cell adhesion molecule binding\" (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Additionally, 61 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were significantly enriched, with the PI3K-Akt signaling pathway showing the most notable enrichment (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003ePPI network analysis of the 400 DEGs yielded 324 nodes and 1,638 edges (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Through integration of five topological algorithms\u0026mdash;degree, DMNC, MCC, MNC, and EPC\u0026mdash;five hub genes were identified: \u003cem\u003eFcgr2b\u003c/em\u003e, \u003cem\u003eTlr7\u003c/em\u003e, \u003cem\u003eClec7a\u003c/em\u003e, \u003cem\u003eTlr9\u003c/em\u003e, and \u003cem\u003eCd53\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). The Wilcoxon test confirmed that all five genes were significantly upregulated in the AAA group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with \u003cem\u003eCd53\u003c/em\u003e exhibiting the highest expression level (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Chromosomal distribution and subcellular localization of key genes\u003c/h2\u003e\u003cp\u003eChromosomal mapping showed that \u003cem\u003eFcgr2b\u003c/em\u003e was located on chromosome 13, \u003cem\u003eTlr7\u003c/em\u003e on chromosome X, \u003cem\u003eClec7a\u003c/em\u003e on chromosome 4, \u003cem\u003eTlr9\u003c/em\u003e on chromosome 8, and \u003cem\u003eCd53\u003c/em\u003e on chromosome 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Analysis of tissue-specific expression patterns revealed that \u003cem\u003eFcgr2b\u003c/em\u003e, \u003cem\u003eTlr7\u003c/em\u003e, and \u003cem\u003eCd53\u003c/em\u003e were highly expressed in spleen tissue, while \u003cem\u003eCd53\u003c/em\u003e and \u003cem\u003eFcgr2b\u003c/em\u003e were also prominently expressed in bone marrow (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Subcellular localization data indicated that \u003cem\u003eFcgr2b\u003c/em\u003e, \u003cem\u003eTlr7\u003c/em\u003e, \u003cem\u003eClec7a\u003c/em\u003e, and \u003cem\u003eTlr9\u003c/em\u003e were primarily cytoplasmic, whereas \u003cem\u003eCd53\u003c/em\u003e was mainly localized in the nucleus (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.3 GSEA analysis of hub genes\u003c/h2\u003e\u003cp\u003eTo further elucidate the biological functions of the identified hub genes, gene set enrichment analysis (GSEA) was conducted. The results demonstrated that \u003cem\u003eCd53\u003c/em\u003e, \u003cem\u003eFcgr2b\u003c/em\u003e, and \u003cem\u003eTlr9\u003c/em\u003e were significantly enriched in pathways related to leukocyte transendothelial migration and regulation of the actin cytoskeleton. Meanwhile, \u003cem\u003eClec7a\u003c/em\u003e and \u003cem\u003eTlr7\u003c/em\u003e were associated with pathways involving the cell cycle, DNA replication, and glycan degradation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Molecular regulatory networks of key genes\u003c/h2\u003e\u003cp\u003eTo explore the molecular regulatory mechanisms of \u003cem\u003eClec7a\u003c/em\u003e and \u003cem\u003eCd53\u003c/em\u003e, both lncRNA\u0026ndash;miRNA\u0026ndash;mRNA and transcription factor (TF)\u0026ndash;miRNA\u0026ndash;mRNA interaction networks were constructed. \u003cem\u003eClec7a\u003c/em\u003e was predicted to interact with nine miRNAs, including rno-miR-377-5p, and seven corresponding lncRNAs, such as \u003cem\u003eFndc1\u003c/em\u003e. In contrast, \u003cem\u003eCd53\u003c/em\u003e was associated with only two miRNAs: rno-miR-325-3p and rno-miR-124-5p (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the TF\u0026ndash;miRNA\u0026ndash;mRNA regulatory network, 5, 12, 13, 9, and 12 transcription factors were identified for \u003cem\u003eFcgr2b\u003c/em\u003e, \u003cem\u003eTlr7\u003c/em\u003e, \u003cem\u003eClec7a\u003c/em\u003e, \u003cem\u003eTlr9\u003c/em\u003e, and \u003cem\u003eCd53\u003c/em\u003e, respectively. Notably, \u003cem\u003eJUN\u003c/em\u003e was a shared regulator among \u003cem\u003eTlr7\u003c/em\u003e, \u003cem\u003eFcgr2b\u003c/em\u003e, and \u003cem\u003eClec7a\u003c/em\u003e, while \u003cem\u003eGATA2\u003c/em\u003e was predicted to regulate \u003cem\u003eTlr9\u003c/em\u003e, \u003cem\u003eCd53\u003c/em\u003e, and \u003cem\u003eClec7a\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Drug and disease association of hub genes\u003c/h2\u003e\u003cp\u003eTo identify candidate drugs targeting AAA, drug\u0026ndash;gene interaction predictions were performed using the DSigDB database. A total of 131 compounds were predicted to interact with the five key genes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among them, AGN-PC-0JHFVD was predicted to target \u003cem\u003eCd53\u003c/em\u003e, \u003cem\u003eTlr9\u003c/em\u003e, \u003cem\u003eTlr7\u003c/em\u003e, and \u003cem\u003eFcgr2b\u003c/em\u003e, while diphenylpyraline was predicted to target \u003cem\u003eCd53\u003c/em\u003e, \u003cem\u003eTlr9\u003c/em\u003e, and \u003cem\u003eTlr7\u003c/em\u003e. Diphenylpyraline was selected as the most promising candidate based on its statistically significant \u003cem\u003eP\u003c/em\u003e-value (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA).\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\u003eKey genes are predicted to be involved in the development of drugs for AAA therapy.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTerm\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGenes\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGN-PC-0JHFVD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.00005E-08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eCd53;Tlr9;Tlr7;Fcgr2b\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiphenylpyraline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.75026E-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eCd53;Tlr9;Tlr7\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIsoguanine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.58731E-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eCd53;Tlr9;Tlr7\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIMIQUIMOD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.02451E-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eTlr9;Tlr7\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChloroquine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.47256E-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eTlr9;Tlr7\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInosinic acid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.15228E-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eTlr9;Tlr7\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBeta-D-allopyranose\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0002306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eTlr9;Tlr7\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChloroquine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00023538\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eTlr9;Tlr7\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrimethoprim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000890245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eCd53;Tlr9\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeukotriene\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.004243144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eClec7a\u003c/em\u003e\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\u003c/p\u003e\u003cp\u003eMolecular docking simulations indicated that diphenylpyraline formed stable binding conformations with \u003cem\u003eCd53\u003c/em\u003e, \u003cem\u003eTlr9\u003c/em\u003e, and \u003cem\u003eTlr7\u003c/em\u003e, with binding scores of \u0026minus;\u0026thinsp;9.2, \u0026minus;\u0026thinsp;7.1, and \u0026minus;\u0026thinsp;7.4, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Key amino acid interactions are illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB\u0026ndash;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD.\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 total scores of diphenylpyraline with \u003cem\u003eCd53\u003c/em\u003e, \u003cem\u003eTlr9\u003c/em\u003e, and \u003cem\u003eTlr7\u003c/em\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKey Genes - Key Active Ingredients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCenter\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiphenylpyraline-\u003cem\u003eCd53\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-9.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29, 53, 86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiphenylpyraline-\u003cem\u003eTlr9\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-7.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78, 53, 20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiphenylpyraline-\u003cem\u003eTlr7\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-26, -11, -11\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\u003eDisease prediction analysis revealed that \u003cem\u003eFcgr2b\u003c/em\u003e, \u003cem\u003eTlr7\u003c/em\u003e, \u003cem\u003eClec7a\u003c/em\u003e, \u003cem\u003eTlr9\u003c/em\u003e, and \u003cem\u003eCd53\u003c/em\u003e were associated with 130, 276, 126, 457, and 17 diseases, respectively. Notably, both \u003cem\u003eFcgr2b\u003c/em\u003e and \u003cem\u003eCd53\u003c/em\u003e were linked to neoplastic diseases, indicating their potential roles as diagnostic biomarkers for neoplasms (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Experimental validation of gene expression\u003c/h2\u003e\u003cp\u003eRT-qPCR was employed to validate the expression levels of the five hub genes in AAA and control tissue samples. The results confirmed that \u003cem\u003eFcgr2b\u003c/em\u003e, \u003cem\u003eClec7a\u003c/em\u003e, \u003cem\u003eTlr9\u003c/em\u003e, and \u003cem\u003eCd53\u003c/em\u003e were significantly upregulated in the AAA group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Although \u003cem\u003eTlr7\u003c/em\u003e expression was not statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), its expression level remained elevated compared to the control group. These results were largely consistent with transcriptomic findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA\u0026ndash;E).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eIn this study, we employed comprehensive transcriptomic and bioinformatics analyses to elucidate the molecular underpinnings of AAA, identifying five key genes\u0026mdash;\u003cem\u003eFcgr2b, Tlr7, Clec7a, Tlr9\u003c/em\u003e, and \u003cem\u003eCd53\u003c/em\u003e\u0026mdash;that may serve as critical regulators in AAA pathogenesis. These findings offer a novel theoretical foundation for advancing our understanding of AAA and may guide future diagnostic and therapeutic strategies.\u003c/p\u003e\u003cp\u003eFcgr2b, a low-affinity inhibitory receptor for the Fc region of IgG, has been previously identified as an independent prognostic marker in glioma via computational analyses [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Beyond its role in oncology, Fcgr2b deficiency is associated with exacerbated vascular inflammation and increased vulnerability to immune-mediated vascular damage, as shown in models of atherosclerosis and systemic lupus erythematosus [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. These studies underscore its role in immune homeostasis and vascular integrity. Although Fcgr2b has been implicated in vascular inflammation [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], a direct link to leukocyte transendothelial migration in AAA has not been established. Notably, our enrichment analysis revealed that Fcgr2b is significantly associated with the leukocyte transendothelial migration pathway, offering new insights into its potential function in modulating immune infiltration and disease progression in AAA.\u003c/p\u003e\u003cp\u003eTLR7, a Toll-like receptor that detects single-stranded RNA, initiates robust innate and adaptive immune responses via downstream signaling cascades. TLR7 overactivation has been linked to autoimmune conditions by enhancing B cell longevity and type I interferon production [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In vascular diseases, TLR7 contributes to endothelial dysfunction and immune cell infiltration, fostering chronic inflammation [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. These immune responses mirror those observed in AAA, particularly regarding leukocyte recruitment, cytokine release, and extracellular matrix degradation, suggesting that TLR7 may act as a molecular bridge between innate immune sensing and vascular remodeling in aneurysmal development.\u003c/p\u003e\u003cp\u003eClec7a (Dectin-1), a C-type lectin receptor predominantly expressed in myeloid cells, plays a pivotal role in fungal recognition and macrophage modulation. It has been shown to influence macrophage polarization and the resolution of inflammation in renal injury models [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. More recently, Clec7a has been implicated in atherosclerosis through its regulation of foam cell formation and proinflammatory macrophage phenotypes [38]. Given the overlap in inflammatory mechanisms between atherosclerosis and AAA, Clec7a may similarly contribute to AAA by modulating immune cell behavior and cytokine secretion in the aortic wall microenvironment.\u003c/p\u003e\u003cp\u003eTLR9, another endosomal Toll-like receptor, recognizes unmethylated CpG motifs and promotes inflammatory responses upon activation [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Its role in vascular pathology is gaining recognition, with evidence showing that TLR9 activation in endothelial and smooth muscle cells heightens inflammatory signaling [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Given the sustained immune activation and vascular stress characteristic of AAA, TLR9 may exacerbate pathologic remodeling via inflammasome activation and proinflammatory cytokine expression. Thus, therapeutic targeting of TLR9 could present a viable approach to mitigating aneurysmal progression.\u003c/p\u003e\u003cp\u003eCD53, a member of the tetraspanin family, is widely expressed in hematopoietic cells and plays a crucial role in immune cell signaling. Cd53 deficiency compromises lymphocyte function and increases susceptibility to infection, underscoring its immunoregulatory role [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. While its role in vascular pathology remains underexplored, Cd53 may influence AAA development through its effects on leukocyte adhesion, migration, and antigen presentation\u0026mdash;processes known to be dysregulated in aneurysmal tissue.\u003c/p\u003e\u003cp\u003eInterestingly, Clec7a and Tlr7 were also enriched in pathways related to DNA replication and cell cycle regulation, suggesting a potential role in genomic stability within vascular cells. Replication stress is increasingly recognized as a contributor to vascular dysfunction, particularly under conditions of oxidative stress and chronic inflammation. This stress activates the ATR kinase, which orchestrates DNA damage responses by coordinating repair pathways through mediators such as TopBP1 and ETAA1 [\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. ETAA1, in particular, serves as a critical ATR activator in response to stalled replication forks [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Together with ATM, ATR governs homologous recombination and suppresses deleterious DNA structures [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. These mechanisms may be relevant in AAA, where VSMCs exhibit phenotypic modulation, senescence, and apoptosis\u0026mdash;hallmarks often driven by DNA damage responses.\u003c/p\u003e\u003cp\u003e\u003cem\u003eIn silico\u003c/em\u003e drug prediction identified diphenylpyraline, an antihistamine with anticholinergic properties [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], as a promising therapeutic agent due to its high binding affinity with \u003cem\u003eCd53\u003c/em\u003e, \u003cem\u003eTlr9\u003c/em\u003e, and \u003cem\u003eTlr7\u003c/em\u003e. Given that AAA is driven by chronic inflammation [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], and that inflammatory cell infiltration promotes aortic wall degradation through the release of cytokines such as TNF-α and IL-6 [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], diphenylpyraline may exert protective effects by modulating histamine receptors and suppressing inflammatory cell activity.\u003c/p\u003e\u003cp\u003eNevertheless, this study has several limitations. The relatively small sample size may limit the generalizability and statistical robustness of our findings. Additionally, we did not assess the correlation between gene expression levels and aortic diameter, a key clinical parameter. Furthermore, the mechanistic roles of the identified genes and diphenylpyraline remain to be experimentally validated. To address these gaps, future studies will expand the sample size to enhance statistical power and incorporate aortic imaging data to correlate gene expression with aneurysm dimensions. We also plan to conduct cellular assays to investigate the specific roles of key genes (e.g., \u003cem\u003eFcgr2b\u003c/em\u003e) in inflammation and cell migration, and to explore their functions through gene knockout or overexpression in animal models. Moreover, \u003cem\u003ein vivo\u003c/em\u003e pharmacodynamic studies of diphenylpyraline will be performed to evaluate its efficacy in suppressing AAA progression and to elucidate its molecular interactions with the identified gene targets, thereby laying the groundwork for clinical translation.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study is the first to systematically identify and characterize the roles of \u003cem\u003eFcgr2b, Tlr7, Clec7a, Tlr9\u003c/em\u003e, and \u003cem\u003eCd53\u003c/em\u003e in AAA. Through integrative functional enrichment and network analyses, we reveal their involvement in immune cell infiltration, DNA replication stress, and inflammatory regulation\u0026mdash;mechanisms that are central to AAA pathogenesis. These findings provide a novel molecular framework for understanding AAA and open new avenues for biomarker discovery and targeted therapy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available in the Genome Sequence Archive (GSA) repository, accession number CRA033718 (https://ngdc.cncb.ac.cn/gsa).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll animal-related procedures were conducted in accordance with the International Guiding Principles for Biomedical Research Involving Animals and were reviewed and approved by the Ethics Committee for Animal Experiments of Kunming Medical University (Approval No. 2014YYGJ116).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRK: Data curation, Formal Analysis, Methodology, Writing original draft, Writing review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJT: Data curation, Formal Analysis, Methodology, Writing original draft, Writing review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eHY: Investigation, Software, Validation, Writing original draft.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eXL: Investigation, Software, Validation, Writing original draft.\u003c/p\u003e\n\u003cp\u003eYJ:Conceptualization, Formal Analysis, Methodology, Writing original draft.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRL: Supervision, Validation, Writing review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eYW: Supervision, Validation, Writing review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKG:Supervision, Visualization, Writing review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eXC: Project administration, Resources, Validation, Writing review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003ePZ: Project administration, Resources, Validation, Writing review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the Open Project of the Clinical Medical Center of the First People's Hospital of Yunnan Province (Grant Nos. 2022LCZXKF-XG02).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge GEO database for providing data. We also express our gratitude to researchers for their generous contribution of microarray datasets and to the creators of the web resources and data processing tools employed in this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Supplementary Material for this article can be found online at: XXX.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGolledge J, Thanigaimani S, Powell JT, Tsao PS. 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Int J Mol Sci. 2016;17(8):1312. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms17081312\u003c/span\u003e\u003cspan address=\"10.3390/ijms17081312\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"abdominal aortic aneurysm, diagnostic biomarkers, therapeutic targets, transcriptome sequencing, bioinformatics","lastPublishedDoi":"10.21203/rs.3.rs-7886883/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7886883/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aimed to identify key genes and potential therapeutic targets involved in the development of abdominal aortic aneurysm (AAA) through transcriptomic profiling in a rat model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRNA sequencing was performed on abdominal aortic tissues from AAA-induced rats and healthy controls. Differentially expressed genes (DEGs) were identified through bioinformatic analysis, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Protein–protein interaction (PPI) networks were constructed to identify central regulatory genes. Additional analyses included tissue-specific gene expression profiling, Gene Set Enrichment Analysis (GSEA), and molecular docking to predict candidate therapeutic compounds. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) was conducted to validate key gene expression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 400 DEGs were identified in AAA tissues, including 314 upregulated and 86 downregulated genes. Functional enrichment indicated significant involvement in biological processes such as response to external stimuli, plasma membrane localization, and cell adhesion. KEGG analysis highlighted the PI3K-Akt signaling pathway as prominently associated with AAA. PPI network analysis identified five hub genes—\u003cem\u003eFcgr2b\u003c/em\u003e, \u003cem\u003eTlr7\u003c/em\u003e, \u003cem\u003eClec7a\u003c/em\u003e, \u003cem\u003eTlr9\u003c/em\u003e, and \u003cem\u003eCd53\u003c/em\u003e—which were significantly upregulated in AAA tissues. Tissue-specific expression analysis revealed that these genes were predominantly expressed in immune-related organs such as the spleen and bone marrow. GSEA showed enrichment of \u003cem\u003eCd53\u003c/em\u003e, \u003cem\u003eFcgr2b\u003c/em\u003e, and \u003cem\u003eTlr9\u003c/em\u003e in leukocyte transendothelial migration and actin cytoskeleton regulation pathways, while \u003cem\u003eClec7a\u003c/em\u003e and \u003cem\u003eTlr7\u003c/em\u003e were linked to cell cycle progression and DNA replication. Molecular docking identified diphenylpyraline as a potential therapeutic compound targeting AAA-related pathways. RT-qPCR validation confirmed the differential expression of the five hub genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis integrative transcriptomic and bioinformatic analysis provides novel insights into the molecular mechanisms underlying AAA and identifies promising diagnostic biomarkers and therapeutic targets.\u003c/p\u003e","manuscriptTitle":"Identification of diagnostic biomarkers and therapeutic targets for abdominal aortic aneurysm via transcriptome sequencing and integrated bioinformatics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-02 13:53:58","doi":"10.21203/rs.3.rs-7886883/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":"b937f7b9-8cca-48c2-a68f-a98fbdb04979","owner":[],"postedDate":"December 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-26T09:42:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-02 13:53:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7886883","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7886883","identity":"rs-7886883","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}