Abstract Adults can develop ulcerative colitis (UC), a chronic inflammatory illness of the colon, while atherosclerosis (AA) is a chronic inflammatory disease of the blood vessels caused by a range of risk factors. Prior research has demonstrated that UC increases the risk of AA, although the underlying pathological mechanisms are not entirely understood. The purpose of this work was to discover differentially expressed genes (DEGs) in UC and AA and investigate their molecular processes using a bioinformatics method. The UC ([38]GSE36807) and AA ([39]GSE28829) datasets were obtained from the Gene Expression Omnibus (GEO) database. Following the identification of genes that are differentially expressed in common with UC and AA, functional annotation, the construction of protein-protein interaction (PPI) networks and modules, the identification of hub genes, and co-expression analysis were carried out. A total of 105 (including 92 up-regulated and 13 down-regulated genes) DEGs were selected for correlation analysis in the above two datasets, and after Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional analysis immune responses, cytokines, and chemokines were found to play crucial roles in both diseases. Finally, a total of 16 hub genes were identified by CytoHubba and MCODE plugins in Cytoscape, including Chemokine (C-C motif) ligand 4(CCL4), Toll-like receptor 2 (TLR2), Integrin Beta 2(ITGB2), Chemokine (C-C motif) Receptor 1(CCR1), Toll-Like Receptor 8 (TLR8), Fc Fragment of IgG Receptor IIa (FCGR2A), Neutrophil Cytosolic Factor 2(NCF2), Leukocyte immunoglobulin-like receptor B2(LILRB2), FGR proto-oncogene, Src family tyrosine kinase(FGR), Intercellular Adhesion Molecule 1 (ICAM1), Caspase 1(CASP1), Matrix Metallopeptidase 9(MMP9), Cluster of Differentiation 163(CD163), Complement Component 5a Receptor 1 (C5AR1), Neutrophil Cytosolic Factor 4 (NCF4), Selectin P (SELP). This study discovered a link between UC and AA, as well as shared hub genes and pathways, which may bring new insights into the processes of UC and AA. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-025-95125-4. Keywords: Ulcerative colitis, Atherosclerosis, Bioinformatics, DEG’s, Hub genes Subject terms: Gastrointestinal diseases, Ulcerative colitis Introduction Atherosclerosis (AA) is a chronic inflammatory disease of the blood vessels characterized by the deposition of various cells, lipids, and tissue debris within the arterial walls^[40]1. Ulcerative colitis (UC) is a chronic inflammatory disease of the colon, often associated with genetic, environmental, and immune dysregulation factors^[41]2. UC has a strong association with AA development. Studies dating back to 1996 indicate that UC patients face an increased risk of AA and that the risk of atherosclerotic cardiovascular disease is 2–4 times higher in UC patients compared to non-UC individuals^[42]3,[43]4. AA plays a crucial role in the pathogenesis of UC when combined with cardiovascular disease. Both UC and AA share common inflammatory, environmental, and immune pathways, including dysregulation of cytokines such as Tumor Necrosis Factor-alpha (TNF-α), toll-like receptors, vascular endothelial growth factor, Interleukin-1 (IL-1), Interleukin-6 (IL-6), and lipopolysaccharides. While the UC is a known risk factor for AA, the precise mechanisms linking the two diseases remain unclear. Inflammatory mediators are likely central to these overlapping pathways. In UC, T cells initiate the immune response and differentiate into Th1 and Th2 subtypes. T helper type 1 (Th1) cells secrete classical pro-inflammatory factors like interferon-gamma (IFN-γ), TNF-α, and Interleukin-2 (IL-2), which reduce gut mucus proteins and compromise the intestinal protective barrier^[44]5. TNF-α and IFN-γ contribute to atherosclerotic plaque growth. Meanwhile, T helper 17 (Th17) cells secrete Interleukin-17 (IL-17) and Interleukin-22 (IL-22), which increase reactive oxygen species, accelerating neointima formation and intraplaque bleeding. IL-17 can weaken the fibrous cap of atherosclerotic plaques, potentially leading to myocardial infarction^[45]6. Additionally, vascular smooth muscle cells in AA produce large amounts of extracellular matrix, secreting Monocyte Chemoattractant Protein-1 (MCP-1), Interleukin-1 beta (IL-1β), and IL-6. These molecules regulate neighboring cell function and release extracellular vesicles that promote vascular calcification^[46]7. Gene array technology allows researchers to measure gene expression data, enhancing the understanding of disease pathogenesis at the genetic level. Identifying common transcription factors (TFs) may offer novel insights into the shared pathogenesis of UC and AA. This study aimed to identify hub genes in UC associated with AA pathogenesis. These gene modules were then analyzed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and Protein-Protein Interaction (PPI) network analysis to identify hub genes. Sixteen key hub genes were identified, their TFs were analyzed, and their expression was verified. These hub genes may provide valuable insights into the biological mechanisms underlying UC and AA. Materials and methods GEO data download and collation Relevant gene expression datasets were retrieved from the GEO database using the keywords “UC” or “AA.” The inclusion criteria were as follows: (1) The sequencing samples must be human; (2) The expression profiles for both diseases must be from the same sequencing platform (Affymetrix [47]GPL570 platform, Affymetrix Human Genome U133 Plus 2.0 Array); (3) The gene expression profiles must include both case and control groups; (4) The dataset must provide processed data or raw data suitable for re-analysis. After screening, two datasets were downloaded: [48]GSE28829, which includes 13 samples of early atherosclerotic (EA) and 16 samples of advanced atherosclerotic (AA) from human carotid arteries, and [49]GSE36807, which includes 7 healthy control (HC) samples and 13 samples of UC. Identification of DEGs Differentially expressed genes (DEGs) for the analysis of UC and AA were extracted using the online analysis tool GEO2R ([50]https://www.ncbi.nlm.nih.gov/geo/geo2r/)^[51]8. The screening criteria for DEGs were as follows: adjusted p-value < 0.05 and log2 fold change (FC) > 1. Gene differential expression was calculated using the “limma” package in R (R 4.2.2). The “ggplot2” package was used to plot gene heatmaps and volcano plots, with log2FC as the horizontal coordinate and -log10(P.adj) as the vertical coordinate. Additionally, the overlapping differentially expressed genes between UC and AA were visualized using the online Venn diagram tool ([52]http://bioinformatics.psb.ugent.be/webtools/Venn/). Functional enrichment analysis for DEGs Differentially expressed genes that overlapped between UC and AA were analyzed for GO enrichment, which provides a simple annotation of the gene products in terms of biological pathways and cellular components involved in their function. KEGG^[53]9 signaling pathway enrichment analysis was performed using the R package “clusterProfiler.” A p-value of < 0.05 was used as the threshold for statistical significance. PPI network analysis and hub gene identification PPI network analysis was performed using the online database STRING ([54]http://string-db.org)^[55]10. An interaction composite score > 0.4 was set as the threshold. PPI networks were visualized using Cytoscape software ([56]http://www.cytoscape.org)^[57]11. TKey functional modules were analyzed using the Cytoscape plug-in MCODE, with the following settings: k-core = 2, degree cutoff = 2, max depth = 100, and node score cutoff = 0.2. Hub genes were identified using Cytoscape’s cytoHubba plugin, with the Degree algorithm applied to determine the hub genes. A co-expression network for these hub genes was constructed using GeneMANIA^[58]12. Validation of hub genes expression with other data sets Two datasets, [59]GSE38713^[60]13 and [61]GSE100927^[62]14, were used to validate the expression of mRNA for the identified hub genes. The two datasets validate the expression of mRNA for identified hub genes. The [63]GSE38713 dataset consists of 13 healthy control colon samples, 8 inactive UC samples and 22 active UC samples. the [64]GSE100927 dataset consists of 69 atherosclerotic samples and 35 control arterial samples. The two data sets were compared using a t-test, and a p-value < 0.05 suggested statistical significance. Predicting TFs and validating The Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRRUST) is a manually annotated database of transcriptional regulatory networks^[65]15, TRRUST contains the target genes corresponding to transcription factors and the regulatory relationships between transcription factors. patterns with 8,972 known regulatory messages, or 59.8%. Transcription factors regulating hub genes were obtained through the TRRUST database and finally the expression levels of transcription factors for hub genes were verified in [66]GSE36807 and [67]GSE28829 using a t-test and adjusted p < 0.05 was considered statistically significant. Results DEGs identification The flowchart of our study is shown in Fig. [68]1. After standard processing of the dataset, the “limma” package in R software was used to visualize the volcano and heat maps of the DEGs in the two datasets. A total of 1,048 DEGs were identified in [69]GSE36807 and 571 DEGs in [70]GSE28829 (Fig. [71]2A,B). After performing an intersection of the Venn diagrams, 124 common DEGs were obtained. Upon removing genes with differing expression trends in [72]GSE36807 and [73]GSE28829, 13 downregulated DEGs and 92 upregulated DEGs were identified (Fig. [74]2C,D). Fig. 1. [75]Fig. 1 [76]Open in a new tab Research design flow chart. Fig. 2. [77]Fig. 2 [78]Open in a new tab UC and AA datasets. The volcano plot and Venn diagram of differentially expressed genes (DEGs). (A) Volcano plot of DEGs in [79]GSE36807. (B) Volcano plot of DEGs in [80]GSE28829; Upregulated genes are marked in light red; downregulated genes are marked in light green. (C) Venn diagrams of the [81]GSE36807 and [82]GSE28829 datasets showing down-regulation of DEGs. (D) Venn diagrams of the [83]GSE36807 and [84]GSE28829 datasets showing upregulated expression of DEGs. Functional analysis of DEGs To gain a more accurate understanding of the selected DEGs, the “clusterProfiler” package in R was used for enrichment analysis of GO and KEGG pathways. A filtering condition of adjusted P < 0.05 was applied, and the top 10 entries with the most significant GO and KEGG enrichments were selected. The results revealed that the DEGs were enriched in biological processes such as leukocyte migration, regulation of leukocyte migration, regulation of positive leukocyte migration, regulation of monocyte migration, regulation of extracellular infiltration, cytokine-mediated signaling pathways, and regulation of monocyte migration. In terms of cellular components, the DEGs were primarily associated with secretory granule membranes, tertiary granules, peptidase inhibitor complexes, paclitaxel-1-enriched granules, and specific granule membranes. They were also involved in protein complexes related to cell adhesion, specific granules, membrane rafts, and membrane microdomains. Molecular functional analysis showed that the DEGs were significantly enriched in serine-type endopeptidase activity, serine-type peptidase activity, serine hydrolase activity, endopeptidase activity, integrin binding, extracellular matrix binding, pattern recognition receptor (PRR) activity, collagen binding, glycolipid binding, and G protein-coupled receptor binding (Fig. [85]3B). Important KEGG pathways enriched in the DEGs included Staphylococcus aureus infection, neutrophil extracellular trap formation, complement and coagulation cascades, lipid and arachidonic acid metabolism, leukocyte trans-endothelial migration, leishmaniasis, malaria, rheumatoid arthritis, the Nuclear Factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling pathway, and Coronavirus Disease 2019 (COVID-19) (Fig. [86]3C). Fig. 3. [87]Fig. 3 [88]Open in a new tab Protein–protein interaction network and enrichment analysis of common DEGs. (A) PPI network diagram. Red indicates up-regulated genes, and green indicates down-regulated genes. (B,C) The enrichment analysis results of GO and KEGG Pathway. Adjusted P-value < 0.05 was considered significant. Analysis of PPI networks A PPI network of co-expressed genes with a composite score > 0.4 was constructed using Cytoscape, comprising 273 interaction pairs and 79 nodes (Fig. [89]3A). The MCODE plug-in in Cytoscape identified three closely related sub-network gene modules, which included 27 common differentially expressed genes and 73 interaction pairs (Fig. [90]4A–C). GO enrichment analysis of these genes revealed associations with inflammatory and immune responses, as well as NADPH responses (Fig. [91]4D). Additional pathways included migration, the Toll-like receptor (TLR) signaling pathway, and lipid and arachidonic acid metabolism (Fig. [92]4E). Fig. 4. [93]Fig. 4 [94]Open in a new tab GO and KEGG pathway enrichment analysis of differentially expressed genes in ulcerative colitis and atherosclerosis. (A–C) Are three closely related sub-network gene clustering modules. (D,E) is an enrichment analysis of the modular genes GO and KEGG. The size of their circles indicates the number of genes and the horizontal coordinates indicate the frequency of participating genes in the total genes. Statistical significance is represented by p-values, p < 0.05 indicating significant enrichment. Selection and analysis of DEGs The Cytoscape plugin cytoHubba was used to analyze the PPI network and identify hub genes. The top 16 hub genes (Table [95]1) were identified using the MCC, EPC, MNC, Degree, Closeness, and Radiality methods of the cytoHubba plugin (Fig. [96]5A). Their full names and associated functions are provided in Table [97]1. Based on the GeneMANIA database, the co-expression network and the functions associated with these genes were analyzed (Fig. [98]5B). GO analysis revealed that these genes are primarily involved in phagocytosis, superoxide anion production, leukocyte trafficking, neuroinflammatory responses, and superoxide metabolism processes (Fig. [99]5C), highlighting the crucial role of the inflammatory response in both disease processes. KEGG pathway analysis indicated that these genes are mainly involved in TLR signaling, phagosomes, neutrophil extracellular trap formation, lipid and arachidonic acid metabolism, and leukocyte migration across endothelial cells (ECs, Fig. [100]5D). Table 1. The top 16 hub genes rank in CytoHubba. MCC EPC MNC Degree Closeness Radiality CCL4 ITGB2 ITGB2 ITGB2 ITGB2 ITGB2 TLR2 MMP9 MMP9 MMP9 MMP9 TLR2 ITGB2 CCL4 ICAM1 ICAM1 ICAM1 ICAM1 CCR1 ICAM1 TLR2 TLR2 TLR2 MMP9 TLR8 TLR2 CCL4 CCL4 CCL4 FCGR2A FCGR2A CCR1 CCR1 CCR1 CCR1 CCL4 NCF2 FCGR2A NCF2 FCGR2A FCGR2A CCR1 LILRB2 NCF2 FCGR2A LILRB2 NCF2 NCF2 FGR TLR8 LILRB2 NCF2 LILRB2 LYN ICAM1 FGR FGR FGR FGR FGR CASP1 CASP1 TLR8 TLR8 TLR8 C5AR1 MMP9 LILRB2 CD163 CASP1 C5AR1 NCF4 CD163 SELP CASP1 CD163 CD163 SELP C5AR1 CD163 SELP NCF4 NCF4 PLAUR NCF4 NCF4 NCF4 SPP1 SELP CD163 TNFRSF1B C5AR1 SPP1 SELP CASP1 LILRB2 SELP BCL2A1 CXCL2 C5AR1 LYN TLR8 CXCL2 TNFRSF1B C5AR1 CXCL2 SPP1 CASP1 BCL2A1 CXCL2 CDH5 CDH5 CDH5 BCL2A1 [101]Open in a new tab Fig. 5. [102]Fig. 5 [103]Open in a new tab Co-expression analysis and functional enrichment of hub genes identified in UC and AA. (A) Upset plot showing 16 overlapping hub genes screened by six algorithms. (B) Analysis of Hub genes and their co-expressed genes by GeneMANIA. (C,D) GO and KEGG enrichment analysis of the hub genes. The outermost circle is term on the right and the inner circle on the left represents the significant p-value of the corresponding pathway of the gene. Validation and expression of DEGs To assess the accuracy of the expression levels of these pivotal genes, this study selected two additional datasets: UC ([104]GSE38713) and AA ([105]GSE100927). The expression levels of the pivotal genes were analyzed, and the results showed that in UC ([106]GSE38713), all pivotal genes except for the FGR gene were significantly upregulated in comparison to normal colon tissue (Fig. [107]S1). In AA ([108]GSE100927), the expression of all genes was higher in atherosclerotic plaques than in normal vascular tissue, except for TLR8, which had a missing expression value (Fig. [109]S2). Prediction and validation of TFs A total of 16 TFs were predicted to regulate the expression of these genes, based on the TRRUST database (Fig. [110]6A; Table [111]2). Further validation revealed that six TFs (CEBPA, HIF1A, IRF1, PPARG, SIRT1, SPI1) were expressed at high levels in UC and AA (Fig. [112]6B, C). These TFs were found to synergistically regulate the hub genes CD163, ICAM1, TLR2, ITGB2, MMP9, and NCF2. Fig. 6. [113]Fig. 6 [114]Open in a new tab TFs regulatory network and their expression in [115]GSE36807 and [116]GSE28829. (A) retrotransposon regulation, yellow represents retrotransposons, red represents hub genes. (B,C) expression levels of retrotransposons in [117]GSE36807 and [118]GSE28829. Comparison of the two data sets was performed using the mean t-test. p-value < 0.05 suggests statistical significance. HC health control, UC ulcerative colitis, EA early atherosclerotic, AA atherosclerotic. *p < 0.05; **p < 0.01; ****p < 0.001. Table 2. Key transcriptional factors (TFs) of hub genes. Key TF Description p value List of overlapped genes ETS2 v-ets erythroblastosis virus E26 oncogene homolog 2 (avian) 2.44E−06 CD163, MMP9, ICAM1 SPI1 Spleen focus forming virus (SFFV) proviral integration oncogene spi1 1.83E−05 NCF2, ITGB2, CD163 SP1 Sp1 transcription factor 3.33E−05 ITGB2, TLR2, ICAM1, MMP9, CD163 STAT1 Signal transducer and activator of transcription 1, 91 kDa 4.57E−05 MMP9, ICAM1, CCR1 KLF5 Kruppel-like factor 5 (intestinal) 5.23E−05 ITGB2, MMP9 RELA v-rel reticuloendotheliosis viral oncogene homolog A (avian) 9.92E−05 CCL4, MMP9, ICAM1, TLR2 NFKB1 Nuclear factor of kappa light polypeptide gene enhancer in B-cells 1 0.000102 MMP9, ICAM1, CCL4, TLR2 NFKBIA Nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha 0.000114 ICAM1, MMP9 SIRT1 Sirtuin 1 0.000743 ICAM1, MMP9 CEBPA CCAAT/enhancer binding protein (C/EBP), alpha 0.000839 ICAM1, CD163 IRF1 Interferon regulatory factor 1 0.000839 NCF2, MMP9 PPARG Peroxisome proliferator-activated receptor gamma 0.0014 MMP9, ICAM1 HDAC1 Histone deacetylase 1 0.00162 MMP9, ICAM1 ETS1 v-ets erythroblastosis virus E26 oncogene homolog 1 (avian) 0.002 CASP1, MMP9 HIF1A Hypoxia inducible factor 1, alpha subunit (basic helix-loop-helix transcription factor) 0.0022 TLR2, ITGB2 STAT3 Signal transducer and activator of transcription 3 (acute-phase response factor) 0.0063 MMP9, ICAM1 [119]Open in a new tab Discussion Recent studies have increasingly confirmed the link between UC and AA. The pathogenesis of both UC and AA is related to chronic inflammation, primarily caused by abnormal endothelial cell function, oxidative stress, and an imbalance in cytokine secretion^[120]16. Research indicates that UC is influenced by genetic, immune, and environmental factors, leading to intestinal dysbiosis. This disruption allows the intestinal barrier to be compromised, enabling lipopolysaccharide (LPS) to cross the intestinal mucosal barrier, thereby activating immune and endothelial cells. This activation triggers inflammation and immune dysregulation, which contributes to the formation of atherosclerotic plaques^[121]17. In AA, Th1 cells are activated to produce pro-inflammatory cytokines such as IL-1, IL-6, and tumor necrosis factor (TNF), which in turn initiate a cascade of inflammatory responses. These cytokines further activate Th1 and Th17 cells, contributing to the development of UC. Both diseases are also linked to the activation of the Janus Kinase (JAK) and Signal Transducer and Activator of Transcription (STAT) pathways, which are downstream of cytokine-mediated lymphocyte activation, particularly with IL-6^[122]18. AA results from impaired metabolism of fatty substances deposited in the artery linings, including cholesterol, triglycerides, celiac particles, sphingolipids, ceramides, low-density lipoprotein (LDL), and apolipoproteins^[123]19. UC can be associated with AA through abnormal lipid metabolism, with studies showing a positive correlation between the degree of lipid abnormalities and the expression of inflammatory factors. Consequently, impaired lipid metabolism is positively associated with the severity of UC^[124]20. Furthermore, the intima-media thickness of the internal carotid artery is closely related to UC disease activity^[125]21,[126]22. UC and AA may share similar pathogenic pathways, such as inflammatory and immunomodulatory pathways, which are intricately linked to the mechanisms underlying both diseases. The primary objective of this study was to identify common DEGs in UC and AA, in order to reveal potential therapeutic targets and predict the efficacy of biologic agents for treating UC complicated by AA. This study utilized bioinformatics analysis of two separate gene microarray datasets for UC and AA, sourced from the GEO database. A total of 105 common DEGs were identified in both diseases, including 16 hub genes (namely: CCL4, TLR2, ITGB2, CCR1, TLR8, FCGR2A, NCF2, LILRB2, FGR, ICAM1, CASP1, MMP9, CD163, C5AR1, NCF4, and SELP). The results of GO enrichment analysis showed that these DEGs were mainly enriched in processes such as leukocyte migration, neuroinflammatory responses, and positive regulation of cytokine production. KEGG analysis revealed that the DEGs were primarily enriched in pathways associated with neutrophil extracellular trap formation, lipids and AA, Leishmaniosis, Staphylococcus aureus infection, leukocyte trans-endothelial migration, malaria, and COVID-19. These findings indicate that the identified genes are predominantly involved in inflammatory and immune pathways, where cytokines and chemokines play significant roles in the development and progression of UC and AA. For example, serum levels of IL-1β, IL-6, IL-17, and TNF-α are significantly elevated in both diseases and are key in their pathogenesis. In AA, TLR4 is a typical receptor in the innate immune response, activating the NF-κB signaling pathway and releasing AA-related inflammatory factors, such as IL-1β, IL-6, TNF-α, and IFN-γ^[127]23. In UC, antigen-presenting naïve T cells differentiate into T helper cell subtypes that produce cytokines. Elevated levels of IL-9 and IL-13 in the colonic mucosa led to abnormal barrier function, increased neutrophil migration, and heightened mucosal permeability^[128]24. Additionally, 16 TFs were identified that may regulate the expression of these genes. Further validation revealed that six TFs were highly expressed in both UC and atherosclerotic plaques and were found to be synergistically involved in the regulation of six hub genes (namely: CD163, ICAM1, TLR2, ITGB2, MMP9, and NCF2). Among them, the CD163 is a protein primarily expressed on the surface of macrophages and serves as a marker of macrophage activation. CD163-expressing macrophages can upregulate the endothelial expression of Vascular Cell Adhesion Molecule (VCAM) on microvessels through the NF-κB signaling pathway, promoting the entry of inflammatory cells into the vessel wall, increasing vascular permeability, and accelerating atherosclerotic thrombosis^[129]25. Increased CD163 expression in UC has been linked to the presence of CD163 in macrophages derived from monocytes. Cross-linking of CD163 with EDHU1-Ab induces the secretion of pro-inflammatory cytokines, such as TNF-α, IL-1β, and IL-6, which contribute to enhanced mucosal inflammation^[130]26. Matrix metalloproteinase (MMP)-mediated protein hydrolysis plays a critical role in regulating cellular homeostasis and is involved in the signaling cascades of growth and inflammation. This occurs through the activation of cytokines and the release of sequestered growth factors. MMP9, a member of the MMP family, is an endoprotease with gelatinase activity that compromises endothelial integrity, disrupts collagen fiber production, and contributes to the thickening of the vessel wall, thus accelerating atherosclerotic thrombosis^[131]27. The generation and persistence of inflammation in UC are associated with MMP9 activity, which is induced by cytokines such as TNF-α and Transforming Growth Factor-beta (TGF-β). These cytokines enhance IL-8 and activate IL-1β, maintaining the pro-inflammatory process^[132]28. MMP9 also causes apoptosis of epithelial cells through proteolytic breakdown of the basement membrane, disrupting the epithelial barrier of the colonic mucosa and exacerbating the inflammatory response in the colon^[133]29. Intercellular Adhesion Molecule-1 (ICAM-1) is a transmembrane glycoprotein predominantly expressed in endothelial cells, epithelial cells, and immune cells. ICAM-1 binds to ITGB2 to regulate leukocyte adhesion and extravasation, controlling protein kinase C, proto-oncogene tyrosine protein kinase Src activity, and intracellular calcium signaling to regulate endothelial cell shape and vascular barrier function^[134]30. Studies have confirmed that in the gut epithelium, ICAM-1 expression levels are linked to neutrophil retention in the intestinal lumen, modulating intestinal permeability and delaying wound healing. Upregulation of ICAM-1 by pro-inflammatory cytokines in UC promotes leukocyte adhesion and extravasation into tissues, contributing to a prolonged inflammatory cycle in the colonic mucosa^[135]30–[136]32. In AA, ECs can enhance NF-κB signaling, regulate the secretion of pro-inflammatory cytokines such as IL-1β and TNF-α, and induce ICAM-1 expression. This increases endothelial cell activation and enhances atherosclerotic plaque formation, directly contributing to the inflammatory response within the vessel wall^[137]31,[138]32. TLR is a membrane PRR. TLR2 recognizes molecular patterns associated with various pathogens and plays a role in both the innate immune and inflammatory responses. TLR2 interacts with co-receptors to recruit bridging proteins such as Myeloid Differentiation Primary Response 88 (MyD88) or TRIF, which stimulate signaling pathways and induce the production of pro-inflammatory cytokines and chemokines^[139]33. Studies have shown that TLR2 regulates immune and epithelial cells in the gut, contributing to UC progression by increasing blood endotoxin levels and mediating inflammation^[140]34. TLR2 also promotes vascular smooth muscle cell migration to the intima during AA and contributes to coronary endothelial dysfunction following ischemia/reperfusion through the activation of neutrophils and free radical production. Therefore, TLR2 expression and activation can regulate the inflammatory process after vascular injury in a mouse model^[141]35. Integrin beta 2 (ITGB2), which encodes integrin β2, is an important regulator of cell survival, proliferation, adhesion, and migration^[142]36. ITGB2 promotes the trans-endothelial migration of leukocytes, disrupts endothelial barrier function, induces the release of pro-inflammatory factors, and participates in the inflammatory process in UC^[143]37. β2 integrins play a crucial role in adhesion during leukocyte recruitment, antigen presentation, pathogen clearance, and thrombosis, with their expression significantly increased in AA^[144]38,[145]39. The NCF2 gene encodes neutrophil cytoplasmic factor 2 (p67phox), a component of NADPH oxidase, which plays an antibacterial, anti-inflammatory, and oxidative role in NADPH cellular functions. It is a major source of reactive oxygen species (ROS), which regulate cell proliferation, differentiation, and death^[146]40. Reduced NCF2 expression impairs NADPH oxidase complex synthesis and reduces ROS production, which may be involved in the pathogenesis of inflammatory autoimmune diseases and potentially in UC^[147]41. Oxidative stress is a key risk factor for AA, and NADPH is involved in AA inflammation through oxidative stress, triggering adverse effects such as thrombosis^[148]42. Currently, there are few direct studies investigating the involvement of NCF2 in UC and AA, highlighting the need for future research on this topic. Several studies have demonstrated a significantly increased risk of atherosclerosis (AA) in patients with ulcerative colitis (UC). Measurements of carotid intima-media thickness (CIMT) have shown that UC patients exhibit significantly higher CIMT values compared to healthy controls, indicating an elevated risk of early atherosclerosis. Furthermore, longer disease duration in UC patients is significantly associated with the progression of atherosclerosis^[149]43. The proportion of abnormal intima-media thickness in patients with active UC was significantly higher than in healthy controls (48.15% vs. 26.67%, p = 0.05), suggesting that active inflammation in UC may substantially increase the risk of subclinical atherosclerosis^[150]44. Metabolic abnormalities also play a critical role in AA development among UC patients. A study identified elevated homocysteine levels and insulin resistance as key predictors of atherosclerosis in UC patients^[151]45. Cardiovascular events are notably more frequent in older UC patients, particularly in those with additional cardiovascular risk factors, underscoring the need for targeted management in this population^[152]46. The critical role of inflammation in UC-associated AA has also been confirmed. Chronic inflammation in UC may directly lead to vascular endothelial injury and plaque formation through pro-inflammatory cytokines (e.g., TNF-α, IL-6) and their downstream signaling pathways (e.g., NF-κB)^[153]47 Additionally, UC and AA share certain immune-regulatory pathways, such as Toll-like receptor signaling, suggesting potential common molecular mechanisms underlying these two conditions. While previous studies have separately investigated key genes associated with UC^[154]48 and AA^[155]49, systematic exploration of their shared molecular mechanisms using bioinformatics approaches remains limited. This study integrated multiple independent datasets from the GEO database to identify common hub genes and associated transcription factors (TFs) between UC and AA and constructed complex interaction networks. Through these shared differentially expressed genes (DEGs), we successfully identified critical nodes, providing reliable preliminary evidence for elucidating the shared pathogenesis of UC and AA. The analytical results demonstrated high consistency across datasets and aligned with previously reported mechanisms, further validating the robustness of our findings. However, this study has certain limitations. First, as the gene interaction networks were based on predictions from public databases, further experimental validation is required to elucidate the specific molecular mechanisms of the identified hub genes and TFs in UC and AA. Second, due to constraints in clinical sample availability and funding, we were unable to validate the expression levels of these key genes. Nonetheless, our findings underscore the significant risk of AA in UC patients, potentially driven by chronic inflammation, metabolic dysregulation, and immune regulatory abnormalities. Future studies should integrate clinical samples to further validate these molecular mechanisms and explore prevention and treatment strategies for AA in UC patients, thereby deepening our understanding of the pathological links between these two diseases. Conclusions This study investigated DEGs in UC and AA and explored their molecular processes using a bioinformatics method. In brief, the common DEGs for UC and AA were identified and enrichment and PPI network analyses were performed. A total of 16 hub genes (namely: CCL4, TLR2, ITGB2, CCR1, TLR8, FCGR2A, NCF2, LILRB2, FGR, ICAM1, CASP1, MMP9, CD163, C5AR1, NCF4, and SELP) and six TFc (namely: CEBPA, HIF1A, IRF1, PPARG, SIRT1, and SPI1) were jointly involved in the pathogenesis of both diseases. These mechanisms may be mediated by specific hub genes. The findings provide new insights into the molecular mechanisms of UC and AA, which can aid in the development of early diagnostic strategies, prognostic markers, and therapeutic targets. Electronic supplementary material Below is the link to the electronic supplementary material. [156]Supplementary Material 1^ (517.6KB, docx) [157]Supplementary Material 2^ (254.9KB, docx) Author contributions M.W., D. L. conceived the research design and analyzed the data. X. X., Q. S., Y. X., and L.S. wrote the manuscript. Z. A. and X. Y. provide supervision and funding. All authors reviewed the manuscript and agreed to publish it. Funding This work was supported by the National Natural Science Foundation of China (No.82360978); the Fifth National Training Program for Excellent Clinical Talents in Traditional Chinese Medicine of the State Administration of Traditional Chinese Medicine (No. Guozhong Medicine Renjiao [2022] No.1); Guizhou University of Traditional Chinese Medicine Chinese Medicine Spleen and Stomach Disease Science and Technology Innovation Talent Team Construction (No. Guizhou Chinese Medicine TD [2022] No.005). Data availability The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article. Further inquiries can be directed to the corresponding authors. Declarations Competing interests The authors declare no competing interests. Consent for publication We confirm that this work is original and has not been published elsewhere, nor is it currently under consideration for publication elsewhere. And all authors have approved the manuscript for submission. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Contributor Information Zhenxiang An, Email: anzhenxiang057@gzy.edu.cn. Xiaofang Yang, Email: 363110152@qq.com. References