PAX5 as a Common Diagnostic Marker in Inflammatory Bowel Disease and Rheumatoid Arthritis: A Bioinformatics-Based Exploration

PAX5 as a Common Diagnostic Marker in Inflammatory Bowel Disease and Rheumatoid Arthritis: A Bioinformatics-Based Exploration | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article PAX5 as a Common Diagnostic Marker in Inflammatory Bowel Disease and Rheumatoid Arthritis: A Bioinformatics-Based Exploration Juanjuan Zheng, Jingjing He, Jun Tian, Junru Liu, Zhenzhen Zhai, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8533287/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Background Inflammatory bowel disease (IBD) is a chronic autoimmune-related disease that causes inflammation of the intestine. Rheumatoid arthritis (RA) can be as an extraintestinal complication occurring concurrently with IBD. However, the commonly dysregulated node of IBD and RA has not been studied. Methods We screened differentially expressed genes (DEGs) in the IBD and RA datasets from the Gene Expression Omnibus (GEO). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to enrichment analysis. Determined the significant hub genes by constructing protein-protein interaction (PPI) network. Further search for the transcription factors that regulate the hub genes, and verify the expression of the hub genes and the transcription factors in additional IBD and RA datasets. Finally, the transcription factor PAX5 was validated by multiplex immunohistochemistry (mIHC) in the tissues of IBD and RA. Results 54 consensus DEGs showed strong disease association. GO analysis indicated that these genes were significantly enriched for activation of immune response. KEGG analysis showed these genes may be involved in the cytokine-cytokine receptor inter-action. The PPI enrichment analysis identified 10 hub genes, and transcription factor PAX5 regulates CD19 and CD79A among these 10 hub genes. PAX5 was selected as the optimal common dysregulated node for IBD and RA. The result was confirmed by mIHC. Conclusion Our integrated analysis reveals PAX5 as a key molecular link between IBD and RA, functioning through B-cell regulation (CD19/CD79A) and serving as both a diagnostic marker and therapeutic target. These findings provide the first evidence of a shared pathogenic mechanism connecting gut-joint inflammation via PAX5-mediated immune dysregulation. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Health sciences/Gastroenterology Biological sciences/Genetics Biological sciences/Immunology Health sciences/Rheumatology PAX5 Inflammatory bowel disease Rheumatoid arthritis Transcription factors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Inflammatory bowel disease (IBD) encompassing Crohn's disease (CD) and ulcerative colitis (UC) is a chronic systemic inflammatory condition, and it evolved into a global disease with rising prevalence in every continent 1 . People have noticed the extraintestinal manifestations of IBD, most frequently those affecting the joints, skin, eyes, and hepatobiliary system 2 . However, comorbid diseases, defined as an association of a group of diseases with a given condition, in IBD have been largely ignored 3 . Like other immune-mediated diseases, including psoriasis and rheumatoid arthritis (RA), IBD is associated with several comorbid conditions. RA is a systemic autoimmune disorder characterized by persistent synovial inflammation that leads to progressive joint destruction and functional impairment. While the precise etiology remains unclear, RA manifests as a multi-system disease featuring both articular and extra-articular pathology, including but not limited to subcutaneous nodules, interstitial lung disease, vasculitis, and concur-rent inflammatory bowel conditions 4 . Clinical epidemiology investigations reveal a pronounced predisposition toward immune-mediated disorders among individuals with IBD (including CD and UC phenotypes), where the association with RA emerges as particularly robust 5 , 6 . Simultaneously, experimental research on RA has also demonstrated that RA is prone to inducing dysbiosis of the gut microbiota 7 . Genetic analyses in European cohorts further support a bidirectional relationship, with elevated RA genetic risk scores associated with higher susceptibility to IBD (encompassing CD and UC) 8 . However, the generalizability of these findings to East Asian populations warrants further investigation 9 . It is of great clinical significance to find more specific diagnostic markers for inflammatory bowel disease and arthritis, and carrying out earlier treatment interventions. In the present study, we leveraged publicly available transcriptomic datasets from the GEO database to elucidate common pathogenic pathways and shared biomarkers between IBD and RA through integrative systems biology analyses. Our findings may provide novel insights into diagnostic and therapeutic approaches for patients with comorbid IBD and RA. 2. Material and Methods 2.1 IBD and RA microarray dataset collection We systematically collected and analyzed transcriptomic datasets from the Gene Expression Omnibus (GEO) database ( http://www.ncbi.nlm.nih.gov/geo ), incorporating both discovery and validation cohorts for comprehensive analysis. The primary datasets included GSE87466 (87 IBD patients vs 21 controls) and GSE55457 (13 RA patients vs 10 controls), while independent validation sets were derived from GSE55253 (20 RA patients vs 10 controls) and GSE47908 (39 IBD patients vs 15 controls). All raw microarray data were processed using standardized normalization protocols to ensure cross-dataset comparability, with the validation cohorts specifically selected from different GEO series to confirm the robustness of our findings across independent sample sets. 2.2 Identification of DEGs in IBD and RA To identify differentially expressed genes (DEGs) in our study, we employed the limma package in R, a robust bioinformatics tool for transcriptomic analysis 10 . Using stringent statistical thresholds ( p -value 1), we systematically identified distinct sets of DEGs by comparing: (1) IBD patients versus healthy controls, and (2) RA patients versus matched controls. The resulting DEG profiles were visualized through volcano plots, while the top 50 most significantly dysregulated genes were presented in heatmaps to illustrate expression patterns. For the identification of common pathogenic mechanisms, we performed intersection analysis of IBD- and RA-associated DEGs using an online Venn diagram tool (bioinformat-ics.psb.ugent.be/webtools/Venn), which revealed a core set of co-expressed genes that were subsequently selected for further functional characterization and pathway analysis. 2.3 Functional enrichment analysis for common DEGs To elucidate the biological roles and signaling networks associated with the co-expressed differentially expressed genes (co-DEGs), we performed comprehensive functional enrichment analyses. Using the R package clusterProfiler 11 , we conducted Gene Ontology (GO) classification and KEGG pathway enrichment analysis 12 – 14 , with statistical significance set at an adjusted p -value threshold of 0.05. The outcomes were visualized through bubble plots (for GO terms) and bar graphs (for KEGG pathways). Additionally, we also performed a functional enrichment analysis of co-DEGs via Metascape ( https://metascape . org) 15 , which is a powerful online annotation analysis program that consists of GO terms such as cellular component (CC), molecular function (MF), biological process (BP), and KEGG pathway analysis. 2.4 Construction of the PPI network of the co‑DEGs Protein-protein interaction (PPI) network analysis of the co-DEGs was performed using STRING ( http://string-db.org/ ), a database integrating both predicted and experimentally validated protein interactions. Interactions with a confidence threshold exceeding 0.4 were retained for subsequent analysis 16 . Subsequently, the PPI network was visualized with Cytoscape software (Version 3.10.1; http://cytoscape.org/ ). Hub gene identification was conducted through CytoHubba plugin implementation, applying the maximal clique centrality (MCC) algorithm to rank proteins and identify the 10 most significant hub genes 17 . 2.5 Verification of IBD-related hub genes expression in the RA data sets The expression profiles of hub genes were validated using two independent datasets from GEO, GSE47908 and GSE55235. GSE47908 is composed of 15 control samples and 39 samples with Inflammatory bowel disease. GSE55235 contains 30 biopsy samples, including 10 control samples and 20 RA samples. 2.6 Identification of transcription factor genes and regulatory network construction of top 3 transcription factors Significant transcription factors (TFs) were identified (adjusted p < 0.05) using TRRUST version 2 ( https://www.Grnpedia.org/trrust ). The three most influential TFs were selected according to their number of target DEGs. A regulatory network illustrating interactions between these top TFs and their corresponding target genes was constructed and visualized using Cytoscape. 2.7 Human biopsy specimens Human colon tissue and joint synovial tissue was obtained from Qilu Hospital of Shandong University Dezhou Hospital. The diagnosis of whether it is IBD, RA or normal was made by both clinical doctors and pathologists. All participants provided informed consent and this study was approved by the Human Ethical Committee of Qilu Hospital of Shandong University Dezhou Hospital. 2.8 Multiplex IHC staining and image analysis Multiplex immunofluorescence staining was performed using a commercial kit (AiFang Biological, AFIHC037, Hunan, China) following standard protocols. After deparaffinization and rehydration, tissue sections were blocked with 10% normal goat serum and incubated with primary antibodies against CD19 (Cell Signaling Technology, 90176S, MA, America) 1:2000, PAX5 (AiFang Biological, AFRM0171, Hunan, China) 1:3000, and MPO (ZSGB-BIO, ZA-0197, Beijing, China) 1:2000 at 4℃ overnight. Signal amplification was achieved using Pre-Amplification Mix and enzyme solutions, followed by nuclear counterstaining (DAPI) and mounting with ProLong Gold Antifade Mountant (Thermo Fisher, MA, United States). Images were acquired using a Zeiss LSM 700 confocal system and analyzed with QuPath software (v0.5.1) for cell phenotyping and quantification 18 . 2.9 Statistical analyses Statistical analyses were performed using R (v4.2.2) and GraphPad Prism (v8.0.2). For comparisons between two groups, two-tailed Student's t-tests were applied. Multiple group comparisons were conducted using one-way ANOVA. A significance threshold of P -values < 0.05 was adopted for all statistical tests. 3. Results 3.1 Identification of DEGs in GSE87466 and GSE55457 The basic information from the datasets related to IBD and RA is shown in Table 1 . With p value 0.5 as the screening conditions, a total of 575 genes were upregulated and 189 genes were downregulated in IBD. In RA, a total of 335 genes were upregulated and 123 genes were downregulated. Analysis of the volcano plots (Fig. 1 A, C) and heatmap clustering (Fig. 1 B, D) showed that the identified IBD-DEGs and RA-DEGs can easily distinguish patients with IBD or RA from healthy controls. The Venn diagram demonstrates that 54 DEGs (Table S1 ) were co-expressed in the two gene expression groups (Fig. 1 E, F). Table 1 Details from the datasets related to IBD or RA patients GEO ID Disease Platform Organism Experiment type Samples (case vs. control) Country / region GSE87466 IBD GPL13158 Homo sapiens Expression profiling by array 81 vs. 21 USA GSE55457 RA GPL96 Homo sapiens Expression profiling by array 13 vs.10 Germany 3.2 Functional enrichment analyses of co‑DEGs To elucidate the biological functions and pathways of IBD-associated shared genes in RA pathogenesis, we performed comprehensive GO and KEGG enrichment analyses on the 54 common genes. The GO analysis (Fig. 2 A, B) revealed these genes were predominantly enriched in immune-related biological processes including "activation of immune response", "immune response-activating signaling pathway", and "immune response-regulating signaling pathway". Cellular component analysis showed significant association with the "external side of plasma membrane", while molecular function analysis identified enrichment in "G protein-coupled receptor binding" and "receptor ligand activity". KEGG pathway analysis (Fig. 2 C, D) further demonstrated these shared genes were mainly involved in immune and inflammatory pathways, particularly "Viral protein interaction with cytokine and cytokine receptor", "Chemokine signaling pathway", and "Cytokine-cytokine receptor interaction". These findings collectively highlight that the shared genetic markers between IBD and RA are primarily associated with immune system activation and inflammatory response regulation, providing potential mechanistic insights into their clinical comorbidity. 3.3 Analysis of Protein-Protein Interaction Networks and Functional Modules To investigate potential functional relationships among the shared DEGs between IBD and RA, we constructed a protein-protein interaction (PPI) network using STRING with a stringent interaction confidence threshold (> 0.9). The resulting visualized with Cytoscape, including 40 protein nodes interconnected by 186 edges (Fig. 3 A), revealing extensive connectivity among the gene products. Subsequent module analysis using the MCODE algorithm identified two highly interconnected clusters containing 17 common DEGs forming 48 interaction pairs (Fig. 3 B, C). The most statistically significant module (Fig. 3 D) contained 9 central hub genes that likely represent key regulatory elements in the shared pathophysiology of these diseases. 3.4 Functional Characterization of Hub Genes Functional enrichment analysis of the 10 identified hub genes revealed their pre-dominant involvement in immune-inflammatory pathways through both GO and KEGG analyses. The GO results demonstrated significant enrichment in B cell activation, lymphocyte-mediated immunity, and plasma membrane-associated immune complexes (Fig. 4 A, B), while KEGG analysis highlighted three key inflammatory pathways: cytokine-cytokine receptor interactions, hematopoietic cell lineage differentiation, and chemokine signaling cascades (Fig. 4 C, D). These findings collectively emphasize the crucial role of dysregulated immune responses, particularly B-cell mediated immunity and cytokine networks, in the shared pathogenesis of IBD and RA, suggesting potential therapeutic targets for patients with both conditions. 3.5 The mRNA expression pattern of 10 hub genes To confirm the reliability, we validated hub genes in GSE55457. Similar to the results of mRNA microarray, the expression levels of CD2, CD19, CD27, CD38, CD79A, CXCL9, CXCL10, CXCL10, CXCL13, GZMB, GZMK were found to be significantly upregulated (Fig. 5 A). We further utilized a dataset (GSE55235) encompassing both osteoarthritis and rheumatoid arthritis to examine the differential expression of 10 hub genes across distinct cohorts. The findings substantiate that these ten hub genes exhibit significant upregulation in rheumatoid arthritis (RA), whereas no notable variations were observed in osteoarthritis (OA). (Fig. 5 B). 3.6 The expression pattern of transcription factor genes PAX5 in IBD and RA By using CytoScape (3.10.1) to visualize the regulatory relationship between transcription factors and hub genes (Table S2), the results showed that the hub genes CD79a and CD19 were simultaneously regulated by the transcription factor PAX5 (Fig. 6 A). We further examine the expression of PAX5 in four datasets. The results indicated that PAX5 was highly expressed in the disease group compared to the normal group in each dataset (Fig. 6 B, D, E, F). 3.7 mIHC analysis identifies PAX5 as a shared dysregulated transcription factor in IBD and RA To further verify the reliability and clinical significance of the PAX5 discovered by bioinformatics analysis, we obtained the colon and joint synovial biopsies from healthy individuals, IBD patients, RA patients, and detected PAX5, CD19 by multiplex immunofluorescence. Meanwhile, MPO as a reliable marker of inflammation 19 , was also detected to distinguish between the disease group and the normal group. As shown in Fig. 7 A, B. As an inflammatory indicator, MPO is significantly highly expressed in the intestinal tissues of IBD patients, and both CD19 and PAX5 are also highly expressed compared to the normal control group (Fig. 7 C). At the same time, this trend was observed in both normal and RA patients' joint synovial tissues (Fig. 7 D, E, F). 4. Discussion IBD and RA represent two prevalent chronic inflammatory conditions with growing epidemiological evidence suggesting shared pathogenic mechanisms 20 – 22 . While clinical observations have established an association between intestinal and articular inflammation, the precise etiological relationship remains to be fully elucidated. Notably, disease progression appears to correlate with increased comorbidity risk, highlighting the need for mechanistic investigations. To address this knowledge gap, we implemented an integrated bioinformatics approach to systematically identify common molecular pathways and potential diagnostic biomarkers that may underlie the pathophysiology of both IBD and RA, with the ultimate goal of facilitating early intervention strategies. Our comprehensive analysis of GEO datasets revealed 54 differentially expressed genes (DEGs) shared between IBD and RA. Through integrated bioinformatics approaches including functional enrichment (GO/KEGG) and protein-protein interaction network analysis, we identified 10 central hub genes from these common DEGs. Notably, these hub genes appear to be coordinately regulated by three key transcription factors, with PAX5 emerging as a particularly promising predictive biomarker for assessing comorbid risk in both IBD and RA patients. The transcription factor PAX5 is an essential regulator of B cell commitment and development, it exhibits selective expression restricted to B lymphocytes among hematopoietic cells 23 – 25 . Beyond their conventional immune functions, B cells contribute significantly to autoimmune pathogenesis through multiple mechanisms, including antigen presentation, autoantibody generation, immune complex deposition, and proinflammatory cytokine secretion 26 . Development of mature B cells involves numerous stages that provide rigorous control against autoreactivity 27 . In general, IBD is associated with chronic inflammation characterized by activated B and T lymphocytes and macrophages, increased granulocyte degranulation, and overproduction of cytokines, most notably interleukin (IL)-1a, tumor necrosis factor a, and IL-8 28 . B cells promote the initiation of inflammation in murine models of colitis. The expansion of B cells can block Foxp3 + Treg cells and aggravate inflammation in a murine model of IBD 29 . Longitudinal investigations of dextran sodium sulfate (DSS)-induced intestinal damage and subsequent mucosal repair demonstrate dynamic changes in immune cell populations. Although neutrophil and monocyte infiltration occurs transiently during the inflammatory process, B lymphocytes exhibit sustained expansion throughout the recovery phase, eventually becoming the predominant immune cell type in the colon lamina propria 30 . Comparative analyses reveal distinct phenotypic and functional alterations in B cells from CD and UC patients compared to healthy controls, suggesting their potential role in driving both localized and systemic inflammatory processes 28 . RA is a disease of unknown etiology that is characterized by changes in the synovial tissue, prompted by unknown initiating events, potentially involving infections and tissue injury. These pathological changes lead to characteristic clinical symptoms including articular pain, edema, and extra-articular complications, mediated through the action of arachidonic acid derivatives and pro-inflammatory cytokine networks 31 . Emerging evidence highlights the crucial involvement of B lymphocytes in RA pathogenesis, particularly considering the antibody-mediated nature of the disease. Notably, molecular analyses have revealed the presence of identical B-cell clonal populations in multiple affected joints of RA patients 32 . The surface marker CD19 is expressed from the early stages of B‑cell maturation onward, is present on memory B cells 33 . Overexpression of CD19 has been linked with development of autoimmunity 34 , 35 , which is consistent with our experimental results. When we used the transcriptome data of OA in the dataset to detect the expression of 10 hub genes, there was no significant difference between the OA group and the normal group, which indicates the specificity of these genes in RA. It's worth noting that this molecular understanding has translated into clinical practice, with B-cell-directed biological agents now constituting a cornerstone in the management of rheumatic conditions 36 . These pieces of evidence all demonstrate the crucial role of B cells in autoimmune diseases. Emerging research demonstrates that gut microbiota alterations in RA patients may trigger cross-reactive immune responses through molecular mimicry, where bacterial antigens share structural similarity with endogenous autoantigens, thereby activating CD4 + T cells and plasma cells 37 . Supporting this connection, a Mendelian randomization study by Dai et al. utilizing SNPs as instrumental variables revealed a genetically determined association between RA predisposition and elevated Crohn's disease risk in East Asian populations 38 . It is well known that the intestinal microbiota of IBD patients undergoes changes, which in turn alters the related metabolites. The progression of RA is related to gut microbial metabolism 39 . All the above studies have shown that the onset of RA and IBD are mutually reinforcing, and the function of B cells and the regulatory genes play a very important role in this process. The development and differentiation of B cells are regulated by PAX5. Therefore, PAX5 may be a common molecular mechanism for the two diseases, and it is possible that interfering with PAX5 and regulating B cells could be a potential therapeutic target. However, there are some limitations of our study. while bioinformatics-identified DEGs showed predictive value for IBD and RA development, and mIHC confirmed elevated expression of PAX5, CD19, and MPO compared to controls, these findings require further validation. Second, the underlying molecular mechanisms remain unexplored. Lastly, our sample sizes were relatively small, and larger-sample, multi-center research is needed. 5. Conclusion In conclusion, our comprehensive bioinformatics investigation integrating differential gene expression profiling, functional annotation, and PPI network analysis has elucidated shared molecular pathways underlying the pathogenesis of IBD and RA. This approach has not only revealed key pathogenic mechanisms but also identified PAX5 as a promising molecular target for therapeutic intervention. These findings offer novel genetic insights that may advance our understanding of the common etiological basis between these two immune-mediated disorders. Declarations Data Availability Statement The authors confirmed that all data required to evaluate the study’s conclusions are present in the paper. The relevant calculation codes can be obtained through the https://doi.org/10.5281/zenodo.16809592. Ethics statement The study was conducted in accordance with the Declaration of Helsinki, and approved by The Ethics Committee of the Qilu Hospital of Shandong University Dezhou Hospital (protocol code: 2024018, approval date: 16 January 2024). Author contributions Conceptualization, J.Z. and Y.B.; methodology, J.Z. and J.H.; software, J.Z.; validation, J.T., J.L. and Z.Z.; investigation, J.T.; resources, Z.Z. and Y.L; data curation, Z.J.; writing original draft preparation, J.Z. and J.H; writing review and editing, Y.B and H.Z.; visualization, J.L; supervision, H.Z.; project administration, Y.B.; funding acquisition, J.Z. and J.H. All authors have read and agreed to the published version of the manuscript. Funding This study was supported by the Key Project of Qilu Hospital of Shandong University Dezhou Hospital (dzyyxm010), the Shandong Province Medical and Health Science and Technology Development Program (202502070259)，Science and Technology Foundation of Guizhou Provincial Health Commission (2025GZWJKJXM1401). 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. References Kaplan, G. G. The global burden of IBD: from 2015 to 2025. Nat. Rev. Gastroenterol. Hepatol. 12 , 720–727 (2015). Malik, T. F. & Aurelio, D. M. Extraintestinal Manifestations of Inflammatory Bowel Disease. in StatPearls (StatPearls Publishing, (2024). Argollo, M. et al. Comorbidities in inflammatory bowel disease: a call for action. lancet Gastroenterol. Hepatol. 4 , 643–654 (2019). Smolen, J. S., Aletaha, D. & McInnes, I. B. Rheumatoid arthritis. 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1","display":"","copyAsset":false,"role":"figure","size":612867,"visible":true,"origin":"","legend":"\u003cp\u003eIBD and RA DEGs analysis. (A, B) Volcano plots and clustered heatmap showed DEGs in GSE87466; (C, D) Volcano plots and clustered heatmap showed DEGs in GSE55457; (E, F) Veen diagrams showed the number of upregulated and downregulated DEGs between GSE87466 and GSE55457.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8533287/v1/70f29510e009ea9923862d6f.png"},{"id":100872009,"identity":"a8bb13f4-998a-4b21-84ff-391e30dda37c","added_by":"auto","created_at":"2026-01-22 09:37:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1776397,"visible":true,"origin":"","legend":"\u003cp\u003eGO and KEGG enrichment analyses of DEGs from GSE87466 and GSE55457. (A, B) The enriched GO terms of DEGs; (C, D) KEGG pathway enrichment results (Adapted from Kanehisa Laboratories\u003ca href=\"#_ENREF_12\" title=\"Kanehisa, 2025 #17\"\u003e\u003csup\u003e12-14\u003c/sup\u003e\u003c/a\u003e, with permission). BP biological process, CC cellular component, MF molecular function\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8533287/v1/bb28764ec10cc0b98c8e605b.png"},{"id":100872004,"identity":"b0b83f11-be17-419d-a423-f3c4f7c5bf4a","added_by":"auto","created_at":"2026-01-22 09:37:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1578481,"visible":true,"origin":"","legend":"\u003cp\u003eProtein-protein interaction network and functional module analysis of shared differentially expressed genes. (A) The constructed PPI network displays interactions among common DEGs, with red nodes representing upregulated genes and green nodes indicating downregulated genes. (B, C) The two most significant gene clusters identified through MCODE analysis, demonstrating highly interconnected protein modules. (D) The core network of top 10 hub genes identified by CytoHubba, representing potential key regulators in the IBD-RA pathogenic axis.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8533287/v1/90cf6b098b8c275548081554.png"},{"id":100872006,"identity":"3bc0b7d5-0d6f-4160-9f0a-9e0e2840bd82","added_by":"auto","created_at":"2026-01-22 09:37:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":480768,"visible":true,"origin":"","legend":"\u003cp\u003eEnriched items in GO and KEGG analysis of 10 hub genes. (A, B) Enriched items in the GO analysis; (C, D) Enriched items in the KEGG pathway analysis (Adapted from Kanehisa Laboratories\u003csup\u003e12-14\u003c/sup\u003e, with permission).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8533287/v1/0133e64b8b9805580ced396f.png"},{"id":100872007,"identity":"901c4c63-5316-42fe-8fad-4c53a8eabaf0","added_by":"auto","created_at":"2026-01-22 09:37:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1222805,"visible":true,"origin":"","legend":"\u003cp\u003eThe mRNA expression levels of 10 hub genes were verified by IBD and RA data set. (A) Expression of 10 hub genes in the GSE55457 data set; (B) Expression of 10 hub genes in the GSE55235 data set. ns: not significant, ***: \u003cem\u003ep\u003c/em\u003e\u0026lt;0.005, ****: \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8533287/v1/5589a3fcf45c9f8250b29be3.png"},{"id":100872011,"identity":"3ab516cc-9ff6-44ab-902c-11075ed3fa3c","added_by":"auto","created_at":"2026-01-22 09:37:17","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":121599,"visible":true,"origin":"","legend":"\u003cp\u003eThe transcription factors analysis of regulating hub genes expression. (A) The transcription factor regulatory network of hub genes; (B, C) the mRNA expression levels of PAX5 in test set; (D, E) the mRNA expression levels of PAX5 in validation set. *: \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, **: \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01, ***: \u003cem\u003ep\u003c/em\u003e\u0026lt;0.005\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8533287/v1/b30f8051997878086ea9113d.png"},{"id":100872045,"identity":"2ba910fd-3001-4551-a7e3-a1535ee0b5aa","added_by":"auto","created_at":"2026-01-22 09:37:19","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":656658,"visible":true,"origin":"","legend":"\u003cp\u003ePAX5 and immune cell expression in IBD and RA. (A, B) Multiplex immunofluorescence staining of colon biopsies from healthy controls (HC) and IBD patients, showing co-expression patterns of B-cell marker CD19 (green), transcription factor PAX5 (red), and neutrophil marker MPO (magenta). (C) Quantitative analysis of immune cell infiltration in colonic mucosa. (D, E) Corresponding staining patterns in synovial tissues from HC and RA patients, demonstrating similar immune cell distributions. (F) Statistical quantification of cellular infiltration in joint tissues. All sections were counterstained with DAPI (blue) for nuclear visualization. ****: \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.0001; ***: \u003cem\u003ep\u003c/em\u003e \u0026lt;0.0005.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8533287/v1/2556ea30faa612256b52e28e.png"},{"id":101943951,"identity":"8f069b39-e1f3-4a27-8ee0-5229bab8ec98","added_by":"auto","created_at":"2026-02-05 09:46:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7313040,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8533287/v1/12bca78b-5ab1-470b-af59-5881620b34c3.pdf"},{"id":100872005,"identity":"e9a8e534-257f-4088-ac4e-0d3c83361ebb","added_by":"auto","created_at":"2026-01-22 09:37:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":141625,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformationfiles.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8533287/v1/eeb7e6df77f8f45bb9d769b4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"PAX5 as a Common Diagnostic Marker in Inflammatory Bowel Disease and Rheumatoid Arthritis: A Bioinformatics-Based Exploration","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eInflammatory bowel disease (IBD) encompassing Crohn's disease (CD) and ulcerative colitis (UC) is a chronic systemic inflammatory condition, and it evolved into a global disease with rising prevalence in every continent\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. People have noticed the extraintestinal manifestations of IBD, most frequently those affecting the joints, skin, eyes, and hepatobiliary system\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. However, comorbid diseases, defined as an association of a group of diseases with a given condition, in IBD have been largely ignored\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Like other immune-mediated diseases, including psoriasis and rheumatoid arthritis (RA), IBD is associated with several comorbid conditions.\u003c/p\u003e \u003cp\u003eRA is a systemic autoimmune disorder characterized by persistent synovial inflammation that leads to progressive joint destruction and functional impairment. While the precise etiology remains unclear, RA manifests as a multi-system disease featuring both articular and extra-articular pathology, including but not limited to subcutaneous nodules, interstitial lung disease, vasculitis, and concur-rent inflammatory bowel conditions\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eClinical epidemiology investigations reveal a pronounced predisposition toward immune-mediated disorders among individuals with IBD (including CD and UC phenotypes), where the association with RA emerges as particularly robust\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Simultaneously, experimental research on RA has also demonstrated that RA is prone to inducing dysbiosis of the gut microbiota \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Genetic analyses in European cohorts further support a bidirectional relationship, with elevated RA genetic risk scores associated with higher susceptibility to IBD (encompassing CD and UC)\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. However, the generalizability of these findings to East Asian populations warrants further investigation\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIt is of great clinical significance to find more specific diagnostic markers for inflammatory bowel disease and arthritis, and carrying out earlier treatment interventions. In the present study, we leveraged publicly available transcriptomic datasets from the GEO database to elucidate common pathogenic pathways and shared biomarkers between IBD and RA through integrative systems biology analyses. Our findings may provide novel insights into diagnostic and therapeutic approaches for patients with comorbid IBD and RA.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 IBD and RA microarray dataset collection\u003c/h2\u003e \u003cp\u003eWe systematically collected and analyzed transcriptomic datasets from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), incorporating both discovery and validation cohorts for comprehensive analysis. The primary datasets included GSE87466 (87 IBD patients vs 21 controls) and GSE55457 (13 RA patients vs 10 controls), while independent validation sets were derived from GSE55253 (20 RA patients vs 10 controls) and GSE47908 (39 IBD patients vs 15 controls). All raw microarray data were processed using standardized normalization protocols to ensure cross-dataset comparability, with the validation cohorts specifically selected from different GEO series to confirm the robustness of our findings across independent sample sets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Identification of DEGs in IBD and RA\u003c/h2\u003e \u003cp\u003eTo identify differentially expressed genes (DEGs) in our study, we employed the limma package in R, a robust bioinformatics tool for transcriptomic analysis \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Using stringent statistical thresholds (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2FC| \u0026gt; 1), we systematically identified distinct sets of DEGs by comparing: (1) IBD patients versus healthy controls, and (2) RA patients versus matched controls. The resulting DEG profiles were visualized through volcano plots, while the top 50 most significantly dysregulated genes were presented in heatmaps to illustrate expression patterns. For the identification of common pathogenic mechanisms, we performed intersection analysis of IBD- and RA-associated DEGs using an online Venn diagram tool (bioinformat-ics.psb.ugent.be/webtools/Venn), which revealed a core set of co-expressed genes that were subsequently selected for further functional characterization and pathway analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Functional enrichment analysis for common DEGs\u003c/h2\u003e \u003cp\u003eTo elucidate the biological roles and signaling networks associated with the co-expressed differentially expressed genes (co-DEGs), we performed comprehensive functional enrichment analyses. Using the R package clusterProfiler\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, we conducted Gene Ontology (GO) classification and KEGG pathway enrichment analysis\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, with statistical significance set at an adjusted \u003cem\u003ep\u003c/em\u003e-value threshold of 0.05. The outcomes were visualized through bubble plots (for GO terms) and bar graphs (for KEGG pathways). Additionally, we also performed a functional enrichment analysis of co-DEGs via Metascape (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://metascape\u003c/span\u003e\u003cspan address=\"https://metascape\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. org)\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, which is a powerful online annotation analysis program that consists of GO terms such as cellular component (CC), molecular function (MF), biological process (BP), and KEGG pathway analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Construction of the PPI network of the co‑DEGs\u003c/h2\u003e \u003cp\u003eProtein-protein interaction (PPI) network analysis of the co-DEGs was performed using STRING (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://string-db.org/\u003c/span\u003e\u003cspan address=\"http://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a database integrating both predicted and experimentally validated protein interactions. Interactions with a confidence threshold exceeding 0.4 were retained for subsequent analysis\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Subsequently, the PPI network was visualized with Cytoscape software (Version 3.10.1; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cytoscape.org/\u003c/span\u003e\u003cspan address=\"http://cytoscape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Hub gene identification was conducted through CytoHubba plugin implementation, applying the maximal clique centrality (MCC) algorithm to rank proteins and identify the 10 most significant hub genes\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Verification of IBD-related hub genes expression in the RA data sets\u003c/h2\u003e \u003cp\u003eThe expression profiles of hub genes were validated using two independent datasets from GEO, GSE47908 and GSE55235. GSE47908 is composed of 15 control samples and 39 samples with Inflammatory bowel disease. GSE55235 contains 30 biopsy samples, including 10 control samples and 20 RA samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Identification of transcription factor genes and regulatory network construction of top 3 transcription factors\u003c/h2\u003e \u003cp\u003eSignificant transcription factors (TFs) were identified (adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) using TRRUST version 2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.Grnpedia.org/trrust\u003c/span\u003e\u003cspan address=\"https://www.Grnpedia.org/trrust\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The three most influential TFs were selected according to their number of target DEGs. A regulatory network illustrating interactions between these top TFs and their corresponding target genes was constructed and visualized using Cytoscape.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Human biopsy specimens\u003c/h2\u003e \u003cp\u003eHuman colon tissue and joint synovial tissue was obtained from Qilu Hospital of Shandong University Dezhou Hospital. The diagnosis of whether it is IBD, RA or normal was made by both clinical doctors and pathologists. All participants provided informed consent and this study was approved by the Human Ethical Committee of Qilu Hospital of Shandong University Dezhou Hospital.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Multiplex IHC staining and image analysis\u003c/h2\u003e \u003cp\u003eMultiplex immunofluorescence staining was performed using a commercial kit (AiFang Biological, AFIHC037, Hunan, China) following standard protocols. After deparaffinization and rehydration, tissue sections were blocked with 10% normal goat serum and incubated with primary antibodies against CD19 (Cell Signaling Technology, 90176S, MA, America) 1:2000, PAX5 (AiFang Biological, AFRM0171, Hunan, China) 1:3000, and MPO (ZSGB-BIO, ZA-0197, Beijing, China) 1:2000 at 4℃ overnight. Signal amplification was achieved using Pre-Amplification Mix and enzyme solutions, followed by nuclear counterstaining (DAPI) and mounting with ProLong Gold Antifade Mountant (Thermo Fisher, MA, United States). Images were acquired using a Zeiss LSM 700 confocal system and analyzed with QuPath software (v0.5.1) for cell phenotyping and quantification\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Statistical analyses\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using R (v4.2.2) and GraphPad Prism (v8.0.2). For comparisons between two groups, two-tailed Student's t-tests were applied. Multiple group comparisons were conducted using one-way ANOVA. A significance threshold of \u003cem\u003eP\u003c/em\u003e-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was adopted for all statistical tests.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Identification of DEGs in GSE87466 and GSE55457\u003c/h2\u003e \u003cp\u003eThe basic information from the datasets related to IBD and RA is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. With \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2FC|\u0026gt; 0.5 as the screening conditions, a total of 575 genes were upregulated and 189 genes were downregulated in IBD. In RA, a total of 335 genes were upregulated and 123 genes were downregulated. Analysis of the volcano plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, C) and heatmap clustering (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, D) showed that the identified IBD-DEGs and RA-DEGs can easily distinguish patients with IBD or RA from healthy controls. The Venn diagram demonstrates that 54 DEGs (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) were co-expressed in the two gene expression groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, F).\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\u003eDetails from the datasets related to IBD or RA patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGEO ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDisease\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlatform\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrganism\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExperiment\u003c/p\u003e \u003cp\u003etype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSamples (case vs. control)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003cp\u003e/ region\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE87466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIBD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGPL13158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHomo\u003c/p\u003e \u003cp\u003esapiens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExpression profiling by array\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81 vs. 21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE55457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGPL96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHomo\u003c/p\u003e \u003cp\u003esapiens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExpression profiling by array\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13 vs.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGermany\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 \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Functional enrichment analyses of co‑DEGs\u003c/h2\u003e \u003cp\u003eTo elucidate the biological functions and pathways of IBD-associated shared genes in RA pathogenesis, we performed comprehensive GO and KEGG enrichment analyses on the 54 common genes. The GO analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B) revealed these genes were predominantly enriched in immune-related biological processes including \"activation of immune response\", \"immune response-activating signaling pathway\", and \"immune response-regulating signaling pathway\". Cellular component analysis showed significant association with the \"external side of plasma membrane\", while molecular function analysis identified enrichment in \"G protein-coupled receptor binding\" and \"receptor ligand activity\". KEGG pathway analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, D) further demonstrated these shared genes were mainly involved in immune and inflammatory pathways, particularly \"Viral protein interaction with cytokine and cytokine receptor\", \"Chemokine signaling pathway\", and \"Cytokine-cytokine receptor interaction\". These findings collectively highlight that the shared genetic markers between IBD and RA are primarily associated with immune system activation and inflammatory response regulation, providing potential mechanistic insights into their clinical comorbidity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Analysis of Protein-Protein Interaction Networks and Functional Modules\u003c/h2\u003e \u003cp\u003eTo investigate potential functional relationships among the shared DEGs between IBD and RA, we constructed a protein-protein interaction (PPI) network using STRING with a stringent interaction confidence threshold (\u0026gt;\u0026thinsp;0.9). The resulting visualized with Cytoscape, including 40 protein nodes interconnected by 186 edges (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), revealing extensive connectivity among the gene products. Subsequent module analysis using the MCODE algorithm identified two highly interconnected clusters containing 17 common DEGs forming 48 interaction pairs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, C). The most statistically significant module (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD) contained 9 central hub genes that likely represent key regulatory elements in the shared pathophysiology of these diseases.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Functional Characterization of Hub Genes\u003c/h2\u003e \u003cp\u003eFunctional enrichment analysis of the 10 identified hub genes revealed their pre-dominant involvement in immune-inflammatory pathways through both GO and KEGG analyses. The GO results demonstrated significant enrichment in B cell activation, lymphocyte-mediated immunity, and plasma membrane-associated immune complexes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B), while KEGG analysis highlighted three key inflammatory pathways: cytokine-cytokine receptor interactions, hematopoietic cell lineage differentiation, and chemokine signaling cascades (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, D). These findings collectively emphasize the crucial role of dysregulated immune responses, particularly B-cell mediated immunity and cytokine networks, in the shared pathogenesis of IBD and RA, suggesting potential therapeutic targets for patients with both conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5 The mRNA expression pattern of 10 hub genes\u003c/h2\u003e \u003cp\u003eTo confirm the reliability, we validated hub genes in GSE55457. Similar to the results of mRNA microarray, the expression levels of CD2, CD19, CD27, CD38, CD79A, CXCL9, CXCL10, CXCL10, CXCL13, GZMB, GZMK were found to be significantly upregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). We further utilized a dataset (GSE55235) encompassing both osteoarthritis and rheumatoid arthritis to examine the differential expression of 10 hub genes across distinct cohorts. The findings substantiate that these ten hub genes exhibit significant upregulation in rheumatoid arthritis (RA), whereas no notable variations were observed in osteoarthritis (OA). (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.6 The expression pattern of transcription factor genes PAX5 in IBD and RA\u003c/h2\u003e \u003cp\u003eBy using CytoScape (3.10.1) to visualize the regulatory relationship between transcription factors and hub genes (Table S2), the results showed that the hub genes CD79a and CD19 were simultaneously regulated by the transcription factor PAX5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). We further examine the expression of PAX5 in four datasets. The results indicated that PAX5 was highly expressed in the disease group compared to the normal group in each dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, D, E, F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.7 mIHC analysis identifies PAX5 as a shared dysregulated transcription factor in IBD and RA\u003c/h2\u003e \u003cp\u003eTo further verify the reliability and clinical significance of the PAX5 discovered by bioinformatics analysis, we obtained the colon and joint synovial biopsies from healthy individuals, IBD patients, RA patients, and detected PAX5, CD19 by multiplex immunofluorescence. Meanwhile, MPO as a reliable marker of inflammation\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, was also detected to distinguish between the disease group and the normal group. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, B. As an inflammatory indicator, MPO is significantly highly expressed in the intestinal tissues of IBD patients, and both CD19 and PAX5 are also highly expressed compared to the normal control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). At the same time, this trend was observed in both normal and RA patients' joint synovial tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD, E, F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIBD and RA represent two prevalent chronic inflammatory conditions with growing epidemiological evidence suggesting shared pathogenic mechanisms\u003csup\u003e\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. While clinical observations have established an association between intestinal and articular inflammation, the precise etiological relationship remains to be fully elucidated. Notably, disease progression appears to correlate with increased comorbidity risk, highlighting the need for mechanistic investigations. To address this knowledge gap, we implemented an integrated bioinformatics approach to systematically identify common molecular pathways and potential diagnostic biomarkers that may underlie the pathophysiology of both IBD and RA, with the ultimate goal of facilitating early intervention strategies.\u003c/p\u003e \u003cp\u003eOur comprehensive analysis of GEO datasets revealed 54 differentially expressed genes (DEGs) shared between IBD and RA. Through integrated bioinformatics approaches including functional enrichment (GO/KEGG) and protein-protein interaction network analysis, we identified 10 central hub genes from these common DEGs. Notably, these hub genes appear to be coordinately regulated by three key transcription factors, with PAX5 emerging as a particularly promising predictive biomarker for assessing comorbid risk in both IBD and RA patients.\u003c/p\u003e \u003cp\u003eThe transcription factor PAX5 is an essential regulator of B cell commitment and development, it exhibits selective expression restricted to B lymphocytes among hematopoietic cells\u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Beyond their conventional immune functions, B cells contribute significantly to autoimmune pathogenesis through multiple mechanisms, including antigen presentation, autoantibody generation, immune complex deposition, and proinflammatory cytokine secretion\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Development of mature B cells involves numerous stages that provide rigorous control against autoreactivity\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn general, IBD is associated with chronic inflammation characterized by activated B and T lymphocytes and macrophages, increased granulocyte degranulation, and overproduction of cytokines, most notably interleukin (IL)-1a, tumor necrosis factor a, and IL-8\u003csup\u003e28\u003c/sup\u003e. B cells promote the initiation of inflammation in murine models of colitis. The expansion of B cells can block Foxp3\u0026thinsp;+\u0026thinsp;Treg cells and aggravate inflammation in a murine model of IBD\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Longitudinal investigations of dextran sodium sulfate (DSS)-induced intestinal damage and subsequent mucosal repair demonstrate dynamic changes in immune cell populations. Although neutrophil and monocyte infiltration occurs transiently during the inflammatory process, B lymphocytes exhibit sustained expansion throughout the recovery phase, eventually becoming the predominant immune cell type in the colon lamina propria\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Comparative analyses reveal distinct phenotypic and functional alterations in B cells from CD and UC patients compared to healthy controls, suggesting their potential role in driving both localized and systemic inflammatory processes\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRA is a disease of unknown etiology that is characterized by changes in the synovial tissue, prompted by unknown initiating events, potentially involving infections and tissue injury. These pathological changes lead to characteristic clinical symptoms including articular pain, edema, and extra-articular complications, mediated through the action of arachidonic acid derivatives and pro-inflammatory cytokine networks\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Emerging evidence highlights the crucial involvement of B lymphocytes in RA pathogenesis, particularly considering the antibody-mediated nature of the disease. Notably, molecular analyses have revealed the presence of identical B-cell clonal populations in multiple affected joints of RA patients\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. The surface marker CD19 is expressed from the early stages of B‑cell maturation onward, is present on memory B cells\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Overexpression of CD19 has been linked with development of autoimmunity\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, which is consistent with our experimental results. When we used the transcriptome data of OA in the dataset to detect the expression of 10 hub genes, there was no significant difference between the OA group and the normal group, which indicates the specificity of these genes in RA. It's worth noting that this molecular understanding has translated into clinical practice, with B-cell-directed biological agents now constituting a cornerstone in the management of rheumatic conditions\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. These pieces of evidence all demonstrate the crucial role of B cells in autoimmune diseases.\u003c/p\u003e \u003cp\u003eEmerging research demonstrates that gut microbiota alterations in RA patients may trigger cross-reactive immune responses through molecular mimicry, where bacterial antigens share structural similarity with endogenous autoantigens, thereby activating CD4\u0026thinsp;+\u0026thinsp;T cells and plasma cells\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Supporting this connection, a Mendelian randomization study by Dai et al. utilizing SNPs as instrumental variables revealed a genetically determined association between RA predisposition and elevated Crohn's disease risk in East Asian populations\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. It is well known that the intestinal microbiota of IBD patients undergoes changes, which in turn alters the related metabolites. The progression of RA is related to gut microbial metabolism\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. All the above studies have shown that the onset of RA and IBD are mutually reinforcing, and the function of B cells and the regulatory genes play a very important role in this process. The development and differentiation of B cells are regulated by PAX5. Therefore, PAX5 may be a common molecular mechanism for the two diseases, and it is possible that interfering with PAX5 and regulating B cells could be a potential therapeutic target.\u003c/p\u003e \u003cp\u003eHowever, there are some limitations of our study. while bioinformatics-identified DEGs showed predictive value for IBD and RA development, and mIHC confirmed elevated expression of PAX5, CD19, and MPO compared to controls, these findings require further validation. Second, the underlying molecular mechanisms remain unexplored. Lastly, our sample sizes were relatively small, and larger-sample, multi-center research is needed.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, our comprehensive bioinformatics investigation integrating differential gene expression profiling, functional annotation, and PPI network analysis has elucidated shared molecular pathways underlying the pathogenesis of IBD and RA. This approach has not only revealed key pathogenic mechanisms but also identified PAX5 as a promising molecular target for therapeutic intervention. These findings offer novel genetic insights that may advance our understanding of the common etiological basis between these two immune-mediated disorders.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirmed that all data required to evaluate the study\u0026rsquo;s conclusions are present in the paper. The relevant calculation codes can be obtained through the https://doi.org/10.5281/zenodo.16809592.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki, and approved by The Ethics Committee of the Qilu Hospital of Shandong University Dezhou Hospital (protocol code: 2024018, approval date: 16 January 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, J.Z. and Y.B.; methodology, J.Z. and J.H.; software, J.Z.; validation, J.T., J.L. and Z.Z.; investigation, J.T.; resources, Z.Z. and Y.L; data curation, Z.J.; writing original draft preparation, J.Z. and J.H; writing review and editing, Y.B and H.Z.; visualization, J.L; supervision, H.Z.; project administration, Y.B.; funding acquisition, J.Z. and J.H. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Key Project of Qilu Hospital of Shandong University Dezhou Hospital (dzyyxm010), the Shandong Province Medical and Health Science and Technology Development Program (202502070259)，Science and Technology Foundation of Guizhou Provincial Health Commission (2025GZWJKJXM1401).\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"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKaplan, G. G. The global burden of IBD: from 2015 to 2025. \u003cem\u003eNat. Rev. Gastroenterol. 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Immunol.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e, 1608262 (2025).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"PAX5, Inflammatory bowel disease, Rheumatoid arthritis, Transcription factors","lastPublishedDoi":"10.21203/rs.3.rs-8533287/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8533287/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eInflammatory bowel disease (IBD) is a chronic autoimmune-related disease that causes inflammation of the intestine. Rheumatoid arthritis (RA) can be as an extraintestinal complication occurring concurrently with IBD. However, the commonly dysregulated node of IBD and RA has not been studied.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe screened differentially expressed genes (DEGs) in the IBD and RA datasets from the Gene Expression Omnibus (GEO). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to enrichment analysis. Determined the significant hub genes by constructing protein-protein interaction (PPI) network. Further search for the transcription factors that regulate the hub genes, and verify the expression of the hub genes and the transcription factors in additional IBD and RA datasets. Finally, the transcription factor PAX5 was validated by multiplex immunohistochemistry (mIHC) in the tissues of IBD and RA.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e54 consensus DEGs showed strong disease association. GO analysis indicated that these genes were significantly enriched for activation of immune response. KEGG analysis showed these genes may be involved in the cytokine-cytokine receptor inter-action. The PPI enrichment analysis identified 10 hub genes, and transcription factor PAX5 regulates CD19 and CD79A among these 10 hub genes. PAX5 was selected as the optimal common dysregulated node for IBD and RA. The result was confirmed by mIHC.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur integrated analysis reveals PAX5 as a key molecular link between IBD and RA, functioning through B-cell regulation (CD19/CD79A) and serving as both a diagnostic marker and therapeutic target. These findings provide the first evidence of a shared pathogenic mechanism connecting gut-joint inflammation via PAX5-mediated immune dysregulation.\u003c/p\u003e","manuscriptTitle":"PAX5 as a Common Diagnostic Marker in Inflammatory Bowel Disease and Rheumatoid Arthritis: A Bioinformatics-Based Exploration","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-22 09:37:12","doi":"10.21203/rs.3.rs-8533287/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-04T06:36:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-28T20:44:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-28T13:11:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2144084620241395455835097639266433879","date":"2026-04-22T11:59:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"61331047936343251610655458941116042685","date":"2026-04-20T05:56:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-28T20:06:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20925824663872064089464020344220599850","date":"2026-01-21T10:58:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-20T20:20:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-20T20:18:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-20T13:21:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-15T05:00:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-01-15T04:54:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3276e04b-26aa-497a-af5c-62b697b24770","owner":[],"postedDate":"January 22nd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-04T06:36:21+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":61516588,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":61516589,"name":"Health sciences/Diseases"},{"id":61516590,"name":"Health sciences/Gastroenterology"},{"id":61516591,"name":"Biological sciences/Genetics"},{"id":61516592,"name":"Biological sciences/Immunology"},{"id":61516593,"name":"Health sciences/Rheumatology"}],"tags":[],"updatedAt":"2026-05-04T06:41:28+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-22 09:37:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8533287","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8533287","identity":"rs-8533287","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}