GBP4 is an Accurate Diagnostic Biomarker and a Potential Treatment Target for Crohn’s Disease

GBP4 is an Accurate Diagnostic Biomarker and a Potential Treatment Target for Crohn’s Disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article GBP4 is an Accurate Diagnostic Biomarker and a Potential Treatment Target for Crohn’s Disease Heng Shi, Qin Peng, Xian-Ling Zhou, Shi-Ping Zhu, Sheng-Yun Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1144474/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Extensive evidence has shown that immune cell infiltration is associated with the pathogenesis of Crohn’s disease (CD). In the present study, we explored the potential mechanism underlying the pathogenesis biomarkers for CD. Methods: The GSE179285 dataset containing sequence data for intestinal mucosal was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in the intestinal mucosa of CD patients and healthy individuals were then identified. The infiltration pattern of 22 immune cell types was assessed using the CIBERSORT algorithm. The DEGs and 22 immune cell types were combined to find the key gene network using weighted gene co-expression network analysis (WGCNA), and pathway enrichment analyzes were performed on the hub module in the WGCNA. A linear regression model for the relationship between the expression of the hub genes in CD patients and infiltration of immune cells were also developed. The utility and accuracy of the hub genes for CD diagnosis were assessed using receiver operating characteristic (ROC) analysis. The accuracy of the model was validated using GSE20881 dataset. Results: There were 1135 DEGs between the intestinal mucosal tissue of CD patients and healthy individuals. Of these DEGs, 711 genes were upregulated, whereas 424 of them were downregulated. There was also a significant difference in the infiltration of immune cells to the intestinal mucosal between the CD patients and healthy individuals. WGCNA revealed that the turquoise module genes were strongly correlated with the infiltration of M1 macrophages (cor=0.68, p=10 -16 ). Pathway enrichment analysis further showed the genes in the turquoise module mainly regulated the secretion of interferon-gamma and other immune effector molecules. Finally, the expression of GBP4, the identified hub gene, strongly correlated with the infiltration of M1 macrophages (adjusted r-squared=0.661, p<2x10 -16 ), and is a relatively good marker for CD diagnostic prediction (AUC=0.736). The relationship between GBP4 expression and infiltration of M1 macrophages (adjusted r-squared=0.435, p<2x10 -16 ) and prognostic value of the gene (AUC=0.702) were verified using the GSE20881 validation dataset. Conclusion: GBP4 is a potential biomarker for accurate CD diagnosis. The expression of GBP4 promotes the infiltration of M1 macrophages to the intestinal mucosa of CD patients. Biomedical Engineering GBP4 Crohn’s disease immune cells biomarker Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Crohn’s disease (CD) is a chronic inflammatory bowel disease caused by genetic and environmental factors, and alteration in the composition and abundance of gut microbiota. Research also shows the disease can lead to severely debilitating and dysregulated immune response [1, 2]. The incidence of CD is increasing worldwide, but it is highest in North America and Northern Europe [3-5]. In China, the economic growth in the country has paralleled an increase in the incidence of CD [6, 7]. Although the precise etiology of CD remains unclear, dysregulated and excessive immune responses against pathogenic gut microbiota have been implicated in the development of CD [8]. Obviously, immune responses, especially the immune cells, play an important role in CD. Traditional techniques such as immunohistochemistry and flow cytometry, do not explicitly reveal the immune landscape in the intestinal mucosa of CD patients. Among the more well-studied genes, such as NOD2[9, 10], CARD15 [11, 12] and PRKCQ [13], have been implicated in CD occurrence and development. However, these genes are not entirely related to immune response and therefore are not ideal targets for immunotherapy. As the immunotherapy has been recommended by clinical guideline of CD treatment [14, 15], it is imperative to identify reliable targets in CD patients for immunotherapy. CIBERSORT is a gene expression-based algorithm that accurately reveals the infiltration pattern of immune cells based on gene expression profiles [16]. We investigated infiltration of 22 immune cell types to the intestinal mucosa of CD patients and healthy individuals. Weighted gene co-expression network analysis (WGCNA) is a bioinformatics analytical method for accurate exploration of the relationships between genes and phenotypes [17]. The distinct advantage of WGCNA is that genes can be clustered into co-expression modules, which connect the phenotypic characteristics and the changes in gene expression. The diagnostic value of hub genes can be assessed using receiver operating characteristic (ROC) curve analysis [18]. In the present study, the gene-sequence data for the infiltration of immune cells to the intestinal mucosa of CD patients and healthy individuals were downloaded from the Gene Expression Omnibus (GEO) database. The finding of this study will unpack the complex activities in the immune microenvironment of intestinal mucosa of CD patients, which may reveal new therapeutic targets for the treatment of the disease. Results CD microarray datasets The diagrammatic flow of this study was shown in Figure 1. GSE179285[22] and GSE20881[23] datasets were used in this study. GSE179285 was the training set, whereas GSE20881 was the validation set. Data on GSE number, numbers of samples, gender, sites of mucosal collection, platform, and inflammation are shown in Table 1. There was no statistically significant difference (p > 0.05) between the training dataset and the validation dataset. DEGs between CD patients and healthy individuals Based on the GSE179285, there were 1135 DEGs between CD patients and healthy individuals, in which 711 genes were upregulated whereas 424 genes were downregulated (Figure 2A). The expression profile of the top 50 most upregulated genes and the top 50 most downregulated genes (Additional file 1) were displayed using a heatmap (Figure 2B). The upregulated genes occurred in the ileum, whereas the downregulated genes occurred in colon. Immune cell infiltration The proportion of immune cells varied between the intestinal mucosa tissues of CD patients and normal individuals (Figure 3A-3B, Table 2). Compared with normal tissue, the proportion of CD8 T cells, activated CD4 T cells memory, M1 Macrophages, and neutrophils were significantly higher in the intestinal mucosa of CD patients. Contrarily, a reverse trend was observed for T regulatory cells (Tregs), gamma delta T cells, activated NK cells, M2 Macrophages, and resting Mast cells (Figure 4A). The proportions of plasma cells, CD4 naïve T cells, activated dendritic cells were almost insignificant. There was a strong positive correlation between infiltration of M1 Macrophages and neutrophils (Pearson correlation = 0.519, p < 0.0001), but a strong negative correlation between infiltration of resting Mast cells and activated Mast cells (Pearson correlation = -0.523, p < 0.001) (Figure 4B) Overall, these findings demonstrated the complex, intricate network of immune response in the intestinal mucosa of CD patients. WGCNA and identification of hub genes The soft thresholding power β was set at 18 in the subsequent analysis, because the scale independence reached 0.85 and had a relatively high-average connectivity (Figure 5A). A total of 23 outliner samples were detected, and the height cut-off value was set at 680 (Figure 5B). Four coexpression modules of DEGs were constructed by WGCNA (Figure 6A), and the relationship between modules and infiltration of the immune cells was performed. We found the most significant correlation between the turquoise module and infiltration of Macrophages M1 (cor=0.68, p=1x10 -25 ) (Figure 6B). The immune-related gene in the turquoise module ( GBP4 ) was then identified based on MM > 0.9 and GS > 0.7 (Figure 7A). The expression level of the hub gene is shown in Figure 7B. Compared with healthy individuals, GBP4 was significantly upregulated in the colon and ileum of CD patients. Functional enrichment analysis The GO analysis showed that the brown module mainly regulated vesicle coating, vesicle targeting, Golgi vesicle budding, positive regulation of lipid biosynthetic process, and lipoprotein particle assembly, the grey module mainly regulated antigen processing and presentation, adaptive immune response, reactive oxygen species responses, interferon-gamma responses, immune effector process regulation, and the turquoise module mainly regulated interferon-gamma responses, immune effector process regulation, regulation of response to biotic stimulus, positive regulation of cytokine production, and leukocyte cell-cell adhesion (Figure 7C, Additional file 2). The KEGG analysis further revealed that the grey module mainly regulated antigen processing and presentation, allograft rejection, viral myocarditis, graft-versus-host disease, and Type I diabetes mellitus, and the turquoise module mainly regulated antigen processing and presentation, allograft rejection, viral myocarditis, staphylococcus aureus infection, pertussis, cytokine-cytokine receptor interaction, leishmaniasis, and viral protein interaction with cytokine pathways (Figure 7D, Additional file 3). Linear model and ROC curve analysis There was a positive linear correlation between the expression of GBP4 and infiltration of M1 Macrophages to the intestinal mucosa of CD patients (Macrophage M1=0.0359382+0.0061959* GBP4 , adjust r-squared=0.661, p < 2x10 -16 ) (Figure 8A). The AUC for the diagnostic value of GBP4 for CD was 0.736 (Figure 8B). The strong correlation between the expression of GBP4 and infiltration of Macrophages M1(Macrophage M1=0.0009155+0.1334921* GBP4 , adjust r-squared=0.435, p < 2x10 -16 ), as well as the good diagnostic value of the gene for CD (AUC=0.702) (Figure 8D) was confirmed using the validation set. Discussion CD is a relapsing inflammatory disease, mainly affecting the gastrointestinal tract, and frequently presents with abdominal pain, fever, bowel obstruction or as well as bloody or mucoid diarrhea [24]. The precise pathogenesis of CD remains unclear, but it has been linked to excessive immune response [25-27]. Unraveling the complex immune network underlying CD pathogenesis can uncover new targets for the treatment of the disease. In the present study, we identified 1135 DEGs between CD patients and healthy individuals, some of which have been previously reported. OLFM4 , which was the most upregulated gene, negatively regulates H. pylori- specific immune responses [28] and mucosal defense responses during inflammatory bowel disease [29]. The downregulated gene, FABP1 , is a validated biomarker of CD diagnosis [30]. The function of other notable in CD such as CHP2 is not well understood . Furthermore, the upregulated gene expression was observed in the ileum, which is the most common site for the disease [31]. CIBERSORT revealed a significant difference in proportion of immune cells in the intestinal mucosa of CD and healthy individuals. Macrophage and CD4+ T cells accounted for the largest proportion of the infiltrating immune cells. So far, it had already been reported that macrophage and CD4+ T cells played an important role in CD [32, 33]. Intestinal macrophages are a heterogeneous population of cells thought to be derived from classical blood monocytes, mediated by CCR2[34]. During inflammation, the recruited monocytes differentiate into inflammatory macrophages sensitive to stimulation by Toll-like receptors. The macrophages also secret proinflammatory cytokines, further promoting inflammation [35-37]. In CD patients, the CD14+ macrophages, which secret abundant TNF-α, are the largest proportion of immune cells on the inflamed mucosa [38, 39]. The proportion of infiltrating macrophages in the intestinal mucosa of CD patients is in line with our analysis by CIBERSORT. CD4+ T cells can also release a large amount of proinflammatory cytokines such as IFN-γ and IL-17/IL-22, and these cytokines contribute to the progression of CD [40]. We observed a significant difference in the proportion of resting NK cells, activated NK cells, monocytes, resting mast cells, and neutrophils in the intestinal mucosa of CD patients and normal individuals. Monocytes regulate the phagocytosis of pathogens, digesting processing and presentation of antigens, and releases of effector molecules such as chemokines and cytokines. Moreover, monocytes are thought to be the only source of intestinal macrophages, and changes in the composition of peripheral blood monocytes in CD patients have been reported [41, 42]. NK cells provide a rapid innate immune response, killing target cells without priming. Mast cells, which predominate at mucosal surfaces, are also crucial for early host defense. Mast cells selectively recruit and positively modulate the function of NK cells through soluble mediators such as interferons [43]. WGCNA of the GSE179285 dataset identified a strong link between the turquoise module and infiltration of macrophages M1. GO analysis revealed the genes in the turquoise module mainly regulate interferon-gamma response, regulation of immune effector process, regulation of response to biotic stimulus positive, regulation of cytokine production, and leukocyte cell-cell adhesion. Interferon-gamma can induce transcription of metal transporter, which contributes to CD pathogenesis [44]. Interferon-gamma-target therapy can be used in treating active CD [45]. Inflammation is closely related to regulating the immune effector process, response to biotic factors, production of cytokine, and adhesion of leukocytes to endothelial cells [46]. KEGG analyses demonstrated that staphylococcus aureus infection, pertussis, cytokine-cytokine receptor interaction, leishmaniasis, and interaction of viral protein with cytokine and cytokine receptor were important pathways in our study. Staphylococcus aureus [47], pertussis [48], and leishmaniasis [49] are some of the opportunistic infections in CD patients due to the immunomodulation and immunosuppressive therapies. Herein, we found a strong linear relationship between the expression of GBP4 and the infiltration of M1 macrophages in CD patients. Guanylate Binding Protein 4 ( GBP4 ) regulates innate immune response via interferon gamma. GO annotations revealed GBP4 regulates several biological processes, including GTP binding and GTP ase activity. Little is known about the GBP families. In mice, GBP s protect against lethal bacterial infections [50] through the GBP 4 inflammasome-dependent production of prostaglandins [51]. Moreover, GBP4 is an immune-related signature biomarker for predicting prognoses and immunotherapeutic responses in patients with muscle-invasive bladder cancer [52], and an immune microenvironment biomarker for the prognosis of ovarian cancer [53]. Also, GBP4 takes part in the type-I interferon response and displays a positive correlation with macrophages [54]. However, there is no report about CD with GBP4 . Regarding limitations, first, the results are based on the computational algorithm. Although the accuracy of this technique has been validated, the finding of this study should be verified using in vivo experiments in the future. Second, given the small sample size, the finding of this study may have been exaggerated. Conclusion In conclusion, there is a significant difference in the infiltration of immune cells to intestinal mucosa tissues of CD patients and healthy individuals. Given that GBP4 is a differently expressed gene between healthy individuals and CD patients and is a driver gene of macrophages, the gene is a potential biomarker for the CD diagnosis and prognosis as well as an immunotherapeutic target for CD treatment. Methods Source of data Gene expression data of CD patients and healthy individuals was downloaded from GEO database ( http://www.ncbi.nlm.nih.gov/geo/ ). The screening criteria for the gene expression datasets were as follows: (1) the study type was limited to expression profiling by array; (2) gene expression data in the intestinal mucosa of CD patients and normal individuals; (3) Each dataset contained for at least 100 samples; (4) analyzable processed data or raw data. Data preprocessing and differential gene analysis Data were preprocessed and analyzed using the R software ( https://www.r-project.org/ ) through the following steps: (1) The probe names of each gene were converted to gene symbols, moreover, when a target gene corresponded to multiple probes, the average expression values of the probes was used to represent the expression level of the gene; (2) genes were excluded if the gene expression level was zero in more than half of the samples; (3) genes lacking expression level data for over 30% of the samples were also removed. Differential expression analysis was performed using the “ limma ” R package [19]. Adjusted p value 1.2 or fold change <-1.2 were set as the threshold for significant differential expression. Immune infiltration analysis The composition and proportion of 22 immune cells in the intestinal mucosa of CD patients and healthy individuals were estimated using the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) tool in combination with leukocyte signature matrix (LM22) based on gene expression profiles of the cells [16]. The permutations (perm) of the deconvolution algorithm were set at 1000. Construction of network and identification of hub genes The coexpression network of DEGs and the infiltration of immune cells was performed as previously described [17]. First, the soft thresholding power β , to which coexpression similarity was raised to calculate adjacency, was calculated using the pickSoftThreshold function in the “ WGCNA ” R package. Second, the samples were clustered to identify any obvious outliers. Third, the coexpression network was then constructed. Fourth, key gene modules were identified using hierarchical clustering and the dynamic tree cut function. Gene significance (GS) and module membership (MM) were then calculated to match modules to specific immune cells. According to the correlation between the immune cells and ME and p value, and the module with the highest correlation coefficient and the smallest p value was selected as the most relevant module for the immune cells. Finally, the hub genes in the relevant module for the immune cells were identified based on MM > 0.9 and GS > 0.7. Functional enrichment analysis Biological process and pathway regulated by the genes in the modules were identified using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analysis via the “ clusterProfiler ” R package [20]. The cutoff of the q-value was set at 0.05. Linear model and ROC curve analysis The Best linear model for immune cell and hub genes was derived using a stepwise forward linear regression analysis. The following statistic model was developed: y = β0 + β1x1 + β2 x2 +…+ βixi , where y is the proportion of immune cell, xi is the expression value of hub genes, β0 was the intercept of the regression equation, and βi is the regression coefficients. The utility and accuracy of the hub genes for CD diagnosis were assessed by receiver operating characteristic (ROC) analysis using the “ ROCR ” R package [21]. The area under curve (AUC) was then calculated and screened for genes with AUC greater than 0.7. Statistical analysis Data were analyzed using R software (Rx64 4.0.3). Differences between two groups were analyzed using the Wilcoxon test, whereas the Kruskal-Wallis test used for multiple groups. The correlation between different immune cell subtypes to the intestinal mucosa of CD patients was performed using the Pearson correlation coefficient. Statistical significance was set at p < 0.05. Abbreviations CD, Crohn’s disease; GEO, Gene Expression Omnibus; WGCNA, weighted gene co-expression network analysis; DEGs, differentially expressed genes; ROC, receiver operating characteristic; AUC, area under curve; CIBERSORT, Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts; GS, Gene significance; MM, module membership; KEGG, Kyoto Encyclopedia of Genes and Genomes; GO, Gene Ontology. Declarations Author contributions Conception and design: HS & QP; Administrative support: S-Y S; Provision of study materials or patients: HS & QP; Collection and assembly of data: HS & QP; Data analysis and interpretation: X-L Z, S-P Z; Manuscript writing: HS; Final approval of manuscript: All authors Acknowledgements Not applicable Availability of data and materials The data that support the findings of this study are openly available in GEO database ( http://www.ncbi.nlm.nih.gov/geo/ ) Conflicts of interests The authors declare that they have no conflicts of interest. Ethics approval and consent to participate Not applicable Consent for publication Not applicable Funding None. References Ahmad T, Tamboli CP, Jewell D, Colombel JF: Clinical relevance of advances in genetics and pharmacogenetics of IBD. Gastroenterology 2004, 126 (6):1533-1549. Torres J, Mehandru S, Colombel JF, Peyrin-Biroulet L: Crohn's disease. Lancet 2017, 389 (10080):1741-1755. 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Huo X, Sun H, Liu S, Liang B, Bai H, Wang S, Li S: Identification of a Prognostic Signature for Ovarian Cancer Based on the Microenvironment Genes. Front Genet 2021, 12 :680413. Ottenhoff TH, Dass RH, Yang N, Zhang MM, Wong HE, Sahiratmadja E, Khor CC, Alisjahbana B, van Crevel R, Marzuki S, et al: Genome-wide expression profiling identifies type 1 interferon response pathways in active tuberculosis. PLoS One 2012, 7 (9):e45839. Tables Table 1 Characteristics of training and validation datasets Characteristics GSE179285 (Training set) GSE20881 (Validation set) P-value Samples CD 168 99 0.667 Healthy controls 31 73 Site of mucosal collection Colon 109 150 0.667 Ileum 90 22 Gender Female 13 27 0.793 Male 27 26 Inflammation Inflamed 47 70 0.650 Uninflamed 152 102 Platform GPL6480 GPL1708 Notes: One patient or healthy individual may have one or more samples. Table 2 Comparison of 22 proportion between CD and normal tissue Immune cell CIBERSORT fraction in % of all infiltrating immune cells (mean± SD) CD tissue Normal tissue P-value B cells naive 0.0233±0.0331 0.033±0.038 0.2538 B cells memory 0.0489±0.0542 0.0599±0.0713 0.7750 Plasma cells 0.0005±0.0024 0±0.0002 0.3943 T cells CD8 0.0441±0.0492 0.025±0.0369 0.0205 T cells CD4 naive 0.0002±0.0015 0.0002±0.0013 0.4076 T cells CD4 memory resting 0.1427±0.071 0.1452±0.0735 0.7587 T cells CD4 memory activated 0.0654±0.0517 0.0312±0.0328 0.0003 T cells follicular helper 0.0001±0.0008 0 0.5490 T cells regulatory Tregs. 0.0301±0.0228 0.0385±0.0226 0.0337 T cells gamma delta 0.0322±0.038 0.0444±0.0358 0.0201 NK cells resting 0.008±0.0175 0.0022±0.0096 0.0064 NK cells activated 0.0713±0.0472 0.0994±0.0472 0.0018 Monocytes 0.0087±0.0168 0.0015±0.0042 0.0188 Macrophages M0 0.1068±0.0643 0.0797±0.0613 0.0524 Macrophages M1 0.0814±0.0465 0.0595±0.0346 0.0162 Macrophages M2 0.156±0.0637 0.1939±0.0672 0.0024 Dendritic cells resting 0.0076±0.0142 0.0149±0.0273 0.0519 Dendritic cells activated 0 0 - Mast cells resting 0.1088±0.0749 0.1437±0.0688 0.0114 Mast cells activated 0.032±0.0524 0.0105±0.0223 0.0625 Eosinophils 0.0094±0.0148 0.0096±0.0183 0.5390 Neutrophils 0.0226±0.0332 0.0077±0.008 0.0171 Notes: P values in red indicate statistical significance (p < 0.05). Additional Declarations No competing interests reported. Supplementary Files Additionalfile2.docx Additionalfile1.docx Additionalfile3.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1144474","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":68676792,"identity":"17fe4541-9d0f-4e9e-ae50-4435a8d9c204","order_by":0,"name":"Heng Shi","email":"","orcid":"","institution":"The First Affiliated Hospital of Jinan University, Jinan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Heng","middleName":"","lastName":"Shi","suffix":""},{"id":68676793,"identity":"41b66f9f-368a-498f-a0ee-27c6ac2004bc","order_by":1,"name":"Qin Peng","email":"","orcid":"","institution":"The Central Hospital of Shaoyang, University of South China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qin","middleName":"","lastName":"Peng","suffix":""},{"id":68676794,"identity":"7d07ad2e-0566-42aa-96e6-98cfa5e4dd7f","order_by":2,"name":"Xian-Ling Zhou","email":"","orcid":"","institution":"The First Affiliated Hospital of Jinan University, Jinan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xian-Ling","middleName":"","lastName":"Zhou","suffix":""},{"id":68676795,"identity":"0d36f7ae-b830-406b-a2e6-ff6f8c720875","order_by":3,"name":"Shi-Ping Zhu","email":"","orcid":"","institution":"First Affiliated Hospital of Jinan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shi-Ping","middleName":"","lastName":"Zhu","suffix":""},{"id":68676796,"identity":"540ec727-e83b-4d88-b0cf-8f88bceb24dd","order_by":4,"name":"Sheng-Yun Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYDACCQST8UFChYQcG3v7AaK1MBt8OGNjzMdzJoFoLWySM9vSEudJOBjg1SE/u/nYw69th/PkZ+QekOZhO5zeJsGQwPCjYhtOLQZ3jqUby7YdLja4kZdgzMNzOLdNuvEAY8+Z27i1SOSYSUu2HU7cIJFjkMwjAdQicyCBmbENtxb5GfnfwFrmz8gxOMxjcDidTSLBAK8Whhs5bJIfgVoabuQYNs5ISEsgqMXgRpqZNMO59MQNZ94YM3w4YGPYBgzkg/j8Ij8j+ZnkjzLrxPntOeY/Ev9JyMu3tx988KMCj8OAgJmXDU3kAF71QMD44w8hJaNgFIyCUTCiAQCNV1xriKsp0wAAAABJRU5ErkJggg==","orcid":"","institution":"First Affiliated Hospital of Jinan University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Sheng-Yun","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2021-12-06 08:29:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1144474/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1144474/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":16349461,"identity":"f48244b5-1821-4713-b543-a9fe469baf77","added_by":"auto","created_at":"2021-12-10 15:18:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":21707,"visible":true,"origin":"","legend":"The diagrammatic workflow of the preset study.","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-1144474/v1/146239b94c92c67c7fab7ddf.png"},{"id":16350067,"identity":"5fe91721-cf5b-4fcf-856c-61a1bdd110e1","added_by":"auto","created_at":"2021-12-10 15:24:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":271429,"visible":true,"origin":"","legend":"(A) Volcano plots for the differentially expressed genes in the intestinal mucosa of CD patients and healthy individuals: red dots represent upregulated expressed genes, whereas blue dots represent downregulated expressed genes, and gray dots represent non-differentially expressed genes. (B). Heatmap for the top 50 most upregulated genes and the top 50 most downregulated genes.","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-1144474/v1/dea311072b70a4e7f37643d4.png"},{"id":16349685,"identity":"12a3067b-444b-4842-b357-452f5bfdcb44","added_by":"auto","created_at":"2021-12-10 15:21:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":98608,"visible":true,"origin":"","legend":"The proportion of infiltrating immune cells in the intestinal mucosa. (A) Health individuals. (B) CD patients.","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-1144474/v1/d30647d65d44f5b7c8f4a67b.png"},{"id":16349466,"identity":"8b77fa74-a3e8-4a10-968c-55a6e918bd69","added_by":"auto","created_at":"2021-12-10 15:18:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":80862,"visible":true,"origin":"","legend":"(A) Box plot for the differentially infiltrated immune cells in the intestinal mucosa between CD patients and normal individuals. (B) Correlation heatmap for the correlations between infiltrated immune cells in the intestinal mucosa of CD patients. The Pearson correlation coefficient was applied for the test, *, p \u003c 0.05, **, p \u003c 0.01, ***, p \u003c 0.001.","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-1144474/v1/e55636059eb4693300827db9.png"},{"id":16349469,"identity":"7d5f7303-f496-426b-bd76-d06cbcde0174","added_by":"auto","created_at":"2021-12-10 15:18:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":110659,"visible":true,"origin":"","legend":"(A) The network topology of various soft-thresholding powers. (a) The x-axis represents the soft-thresholding power, whereas the y-axis represents the scale-free topology model fit index. (b) The x-axis reflects the soft-thresholding power. The y-axis reflects the mean connectivity (degree). (B) The sample dendrogram and the infiltration of immune cells heatmap. 23 outliner samples were identified. Only samples in a red dotted square box were included.","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-1144474/v1/1cdcb610c07fca93f1fc5959.png"},{"id":16349462,"identity":"9e73a641-a3b9-4373-b8e6-ec78969646d5","added_by":"auto","created_at":"2021-12-10 15:18:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":66530,"visible":true,"origin":"","legend":"(A) Identification of modules by gene co-expression network. The branches of the cluster dendrogram represent the modules, whereas the leaves on the cluster dendrogram represent the genes. (B) Module-trait associations. Each row corresponds to a module, and each column corresponds to a proportion of infiltrating immune cell. Each cell contains the corresponding correlation and p-value.","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-1144474/v1/0622b3dd25f57fe63e9b195c.png"},{"id":16349687,"identity":"1d81b983-ac3c-4818-862f-c199fb26f10a","added_by":"auto","created_at":"2021-12-10 15:21:20","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":60206,"visible":true,"origin":"","legend":"(A) Scatter diagrams for each gene in turquoise module and M1 Macrophages. (B) Box plot for the expression level of the hub gene. (C) GO analysis of the genes in modules. The node size reflects the gene count, and the node color reflects the P -value [−log10 (P value)]. (D) KEGG analysis of genes in modules. The node size reflects the gene count, and the node color reflects the P-value [−log10 (P value)].","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-1144474/v1/284e2489bf986e4e570f2aec.png"},{"id":16349467,"identity":"d40b974b-6882-4545-b65a-055973ca2709","added_by":"auto","created_at":"2021-12-10 15:18:20","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":41086,"visible":true,"origin":"","legend":"(A)Scatter plot and linear model of the hub gene and infiltration of M1 Macrophages based on GSE179285. (B) The ROC curve of the hub gene based on GSE179285. (C) Scatter plot and linear model of the hub gene and infiltration of M1 Macrophages based on GSE20881. (D) The ROC curve of the hub gene based on GSE20881.","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-1144474/v1/b735165860db458551335121.png"},{"id":16350068,"identity":"dd5d2a23-d961-4ab3-8cff-f82f333ffa7b","added_by":"auto","created_at":"2021-12-10 15:24:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2727548,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1144474/v1/2eb6a339-bfe4-4ba6-ae84-eb00f6efbaf6.pdf"},{"id":16349464,"identity":"ae62fdb0-84e6-453b-bc34-4ba0e19d60ca","added_by":"auto","created_at":"2021-12-10 15:18:20","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":25899,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-1144474/v1/7388cd471be0a3597b33fb9d.docx"},{"id":16349463,"identity":"ec239522-8c5e-4d5d-bf7c-60b1f6aa8c77","added_by":"auto","created_at":"2021-12-10 15:18:20","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":24914,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-1144474/v1/60b2feba1b08ff2d569caa30.docx"},{"id":16349683,"identity":"49dcf394-eac6-45f9-aa0c-24852481ebee","added_by":"auto","created_at":"2021-12-10 15:21:20","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":21416,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile3.docx","url":"https://assets-eu.researchsquare.com/files/rs-1144474/v1/ef5de0c9ae6658fa12864eeb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cem\u003eGBP4\u003c/em\u003e is an Accurate Diagnostic Biomarker and a Potential Treatment Target for Crohn’s Disease\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"Crohn’s disease (CD) is a chronic inflammatory bowel disease caused by genetic and environmental factors, and alteration in the composition and abundance of gut microbiota. Research also shows the disease can lead to severely debilitating and dysregulated immune response [1, 2]. The incidence of CD is increasing worldwide, but it is highest in North America and Northern Europe [3-5]. In China, the economic growth in the country has paralleled an increase in the incidence of CD [6, 7]. \nAlthough the precise etiology of CD remains unclear, dysregulated and excessive immune responses against pathogenic gut microbiota have been implicated in the development of CD [8]. Obviously, immune responses, especially the immune cells, play an important role in CD. Traditional techniques such as immunohistochemistry and flow cytometry, do not explicitly reveal the immune landscape in the intestinal mucosa of CD patients. Among the more well-studied genes, such as NOD2[9, 10], CARD15 [11, 12] and PRKCQ [13], have been implicated in CD occurrence and development. However, these genes are not entirely related to immune response and therefore are not ideal targets for immunotherapy. As the immunotherapy has been recommended by clinical guideline of CD treatment [14, 15], it is imperative to identify reliable targets in CD patients for immunotherapy. \n\tCIBERSORT is a gene expression-based algorithm that accurately reveals the infiltration pattern of immune cells based on gene expression profiles [16]. We investigated infiltration of 22 immune cell types to the intestinal mucosa of CD patients and healthy individuals. Weighted gene co-expression network analysis (WGCNA) is a bioinformatics analytical method for accurate exploration of the relationships between genes and phenotypes [17]. The distinct advantage of WGCNA is that genes can be clustered into co-expression modules, which connect the phenotypic characteristics and the changes in gene expression. The diagnostic value of hub genes can be assessed using receiver operating characteristic (ROC) curve analysis [18].\n\tIn the present study, the gene-sequence data for the infiltration of immune cells to the intestinal mucosa of CD patients and healthy individuals were downloaded from the Gene Expression Omnibus (GEO) database. The finding of this study will unpack the complex activities in the immune microenvironment of intestinal mucosa of CD patients, which may reveal new therapeutic targets for the treatment of the disease. \n"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCD microarray datasets\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe diagrammatic flow of this study was shown in Figure 1. GSE179285[22] and GSE20881[23] datasets were used in this study. GSE179285 was the training set, whereas GSE20881 was the validation set. Data on GSE number, numbers of samples, gender, sites of mucosal collection, platform, and inflammation are shown in Table 1. There was\u0026nbsp;no statistically significant difference (p \u0026gt; 0.05) between the training dataset and the validation dataset.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDEGs between CD patients and healthy individuals\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the GSE179285, there were 1135 DEGs between CD patients and healthy individuals, in which 711\u0026nbsp;genes\u0026nbsp;were upregulated whereas 424\u0026nbsp;genes\u0026nbsp;were downregulated (Figure 2A). The expression profile of the top 50 most upregulated genes and the top 50 most downregulated genes (Additional file 1) were displayed using a heatmap (Figure 2B). The upregulated genes occurred in the ileum, whereas the downregulated genes occurred in colon.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmune cell infiltration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe proportion of immune cells varied between the intestinal mucosa tissues of CD patients and normal\u0026nbsp;individuals\u0026nbsp;(Figure 3A-3B, Table 2). Compared with normal tissue, the proportion of CD8 T cells, activated CD4 T cells memory, M1 Macrophages, and neutrophils were significantly higher in the intestinal mucosa of CD patients. Contrarily, a reverse trend was observed for T regulatory cells (Tregs), gamma delta T cells, activated NK cells, M2 Macrophages, and resting Mast cells (Figure 4A). The proportions of plasma cells, CD4 na\u0026iuml;ve T cells, activated dendritic cells were almost insignificant. There was a strong positive correlation between infiltration of M1 Macrophages and neutrophils (Pearson correlation = 0.519, p \u0026lt; 0.0001), but a strong negative correlation between infiltration of resting Mast cells and activated Mast cells (Pearson correlation = -0.523, p \u0026lt; 0.001) (Figure 4B) Overall, these findings demonstrated the complex, intricate network of immune response in the intestinal mucosa of CD patients. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWGCNA and identification of hub genes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe soft thresholding power \u003cem\u003e\u0026beta;\u003c/em\u003e was set at 18 in the subsequent analysis, because the scale independence reached 0.85 and had a relatively high-average connectivity (Figure 5A). A total of 23 outliner samples were detected, and the height cut-off value was set at 680 (Figure 5B). Four coexpression modules of DEGs were constructed by WGCNA (Figure 6A), and the relationship between modules and infiltration of the immune cells was performed. We found the most significant correlation between the turquoise module and infiltration of Macrophages M1 (cor=0.68, p=1x10\u003csup\u003e-25\u003c/sup\u003e) (Figure 6B). The immune-related gene in the turquoise module (\u003cem\u003eGBP4\u003c/em\u003e) was then identified based on MM \u0026gt; 0.9 and GS \u0026gt; 0.7 (Figure 7A). The expression level of the hub gene is shown in Figure 7B.\u0026nbsp;Compared with healthy individuals, \u003cem\u003eGBP4\u003c/em\u003e was significantly upregulated in the colon and ileum of CD patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GO analysis showed that the brown module mainly regulated vesicle coating, vesicle targeting, Golgi vesicle budding, positive regulation of lipid biosynthetic process, and lipoprotein particle assembly, the grey module mainly regulated antigen processing and presentation, adaptive immune response, reactive oxygen species responses, interferon-gamma responses, immune effector process regulation, and the turquoise module mainly regulated interferon-gamma responses, immune effector process regulation, regulation of response to biotic stimulus, positive regulation of cytokine production, and leukocyte cell-cell adhesion (Figure 7C, Additional file 2). The KEGG analysis further revealed that the grey module mainly regulated antigen processing and presentation, allograft rejection, viral myocarditis, graft-versus-host disease, and Type I diabetes mellitus, and the turquoise module mainly regulated antigen processing and presentation, allograft rejection, viral myocarditis, staphylococcus aureus infection, pertussis, cytokine-cytokine receptor interaction, leishmaniasis, and viral protein interaction with cytokine pathways (Figure 7D, Additional file 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLinear model and ROC curve analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was a positive linear correlation between the expression of \u003cem\u003eGBP4\u003c/em\u003e and infiltration of M1 Macrophages to the intestinal mucosa of CD patients (Macrophage M1=0.0359382+0.0061959*\u003cem\u003eGBP4\u003c/em\u003e, adjust r-squared=0.661, p \u0026lt; 2x10\u003csup\u003e-16\u003c/sup\u003e) (Figure 8A). The AUC for the diagnostic value of \u003cem\u003eGBP4\u003c/em\u003e for CD was 0.736 (Figure 8B). The strong correlation between the expression of\u003cem\u003e\u0026nbsp;GBP4\u003c/em\u003e and infiltration of Macrophages M1(Macrophage M1=0.0009155+0.1334921*\u003cem\u003eGBP4\u003c/em\u003e, adjust r-squared=0.435, p \u0026lt; 2x10\u003csup\u003e-16\u003c/sup\u003e), as well as the good diagnostic value of the gene for CD (AUC=0.702) (Figure 8D) was confirmed using the validation set.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCD is a relapsing inflammatory disease, mainly affecting the gastrointestinal tract, and frequently presents with abdominal pain, fever, bowel obstruction or as well as bloody or mucoid diarrhea [24]. The precise pathogenesis of CD remains unclear, but it has been linked to excessive immune response [25-27]. Unraveling the complex immune network underlying CD pathogenesis can uncover new targets for the treatment of the disease.\u003c/p\u003e\n\u003cp\u003eIn the present study, we identified 1135 DEGs between CD patients and healthy individuals, some of which have been previously reported. \u003cem\u003eOLFM4\u003c/em\u003e, which was the most upregulated gene, negatively regulates \u003cem\u003eH. pylori-\u003c/em\u003especific immune responses [28] and mucosal defense responses during inflammatory bowel disease [29]. The downregulated gene, \u003cem\u003eFABP1\u003c/em\u003e, is a validated biomarker of CD diagnosis [30]. The function of other notable in CD such as \u003cem\u003eCHP2\u003c/em\u003e is not well understood\u003cem\u003e.\u0026nbsp;\u003c/em\u003eFurthermore, the upregulated gene expression was observed in the ileum, which is the most common site for the disease [31].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCIBERSORT revealed a significant difference in proportion of immune cells in the intestinal mucosa of CD and healthy individuals. Macrophage and CD4+ T cells accounted for the largest proportion of the infiltrating immune cells. So far, it had already been reported that macrophage and CD4+ T cells played an important role in CD [32, 33]. Intestinal macrophages are a heterogeneous population of cells thought to be derived from classical blood monocytes, mediated by CCR2[34]. During inflammation, the recruited monocytes differentiate into inflammatory macrophages sensitive to stimulation by Toll-like receptors. The macrophages also secret proinflammatory cytokines, further promoting inflammation [35-37]. In CD patients, the CD14+ macrophages, which secret abundant TNF-\u0026alpha;, are the largest proportion of immune cells on the inflamed mucosa [38, 39]. The proportion of infiltrating macrophages in the intestinal mucosa of CD patients is in line with our analysis by CIBERSORT. CD4+ T cells can also release a large amount of proinflammatory cytokines such as IFN-\u0026gamma; and IL-17/IL-22, and these cytokines contribute to the progression of CD [40]. We observed a significant difference in the proportion of resting NK cells, activated NK cells, monocytes, resting mast cells, and neutrophils in the intestinal mucosa of CD patients and normal individuals. Monocytes regulate the phagocytosis of pathogens, digesting processing and presentation of antigens, and releases of effector molecules such as chemokines and cytokines. Moreover, monocytes are thought to be the only source of intestinal macrophages, and changes in the composition of peripheral blood monocytes in CD patients have been reported [41, 42]. NK cells provide a rapid innate immune response, killing target cells without priming. Mast cells, which predominate at mucosal surfaces, are also crucial for early host defense. Mast cells selectively recruit and positively modulate the function of NK cells through soluble mediators such as interferons [43].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWGCNA of the GSE179285 dataset identified a strong link between the turquoise module and infiltration of macrophages M1. GO analysis revealed the genes in the turquoise module mainly regulate interferon-gamma response, regulation of immune effector process, regulation of response to biotic stimulus positive, regulation of cytokine production, and leukocyte cell-cell adhesion. Interferon-gamma can induce transcription of metal transporter, which contributes to CD pathogenesis [44]. Interferon-gamma-target therapy can be used in treating active CD [45]. Inflammation is closely related to regulating the immune effector process, response to biotic factors, production of cytokine, and adhesion of leukocytes to endothelial cells [46]. KEGG analyses demonstrated that \u003cem\u003estaphylococcus aureus\u003c/em\u003e infection, pertussis, cytokine-cytokine receptor interaction, leishmaniasis, and interaction of viral protein with cytokine and cytokine receptor were important pathways in our study. \u003cem\u003eStaphylococcus aureus\u0026nbsp;\u003c/em\u003e[47], pertussis [48], and leishmaniasis [49] are some of the opportunistic infections in CD patients due to the immunomodulation and immunosuppressive therapies.\u003c/p\u003e\n\u003cp\u003eHerein, we found a strong linear relationship between the expression of \u003cem\u003eGBP4\u003c/em\u003e and the infiltration of M1 macrophages in CD patients. Guanylate Binding Protein 4 (\u003cem\u003eGBP4\u003c/em\u003e) regulates innate immune response via interferon gamma. GO annotations revealed \u003cem\u003eGBP4\u003c/em\u003e regulates several biological processes, including \u003cem\u003eGTP\u003c/em\u003e binding and \u003cem\u003eGTP\u003c/em\u003ease activity. Little is known about the \u003cem\u003eGBP\u003c/em\u003e families. In mice, \u003cem\u003eGBP\u003c/em\u003es protect against lethal bacterial infections [50] through the \u003cem\u003eGBP\u003c/em\u003e4 inflammasome-dependent production of prostaglandins [51]. Moreover, \u003cem\u003eGBP4\u003c/em\u003e is an immune-related signature biomarker for predicting prognoses and immunotherapeutic responses in patients with muscle-invasive bladder cancer [52], and an immune microenvironment biomarker for the prognosis of ovarian cancer [53]. Also, \u003cem\u003eGBP4\u003c/em\u003e takes part in the type-I interferon response and displays a positive correlation with macrophages [54]. However, there is no report about CD with \u003cem\u003eGBP4\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegarding limitations, first, the results are based on the computational algorithm. Although the accuracy of this technique has been validated, the finding of this study should be verified using in \u003cem\u003evivo\u003c/em\u003e experiments in the future. Second, given the small sample size, the finding of this study may have been exaggerated.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, there is a significant difference in the infiltration of immune cells to intestinal mucosa tissues of CD patients and healthy individuals. Given that \u003cem\u003eGBP4\u003c/em\u003e is a differently expressed gene between healthy individuals and CD patients and is a driver gene of macrophages, the gene is a potential biomarker for the CD diagnosis and prognosis as well as an immunotherapeutic target for CD treatment.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eSource of data\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene expression data of CD patients and healthy individuals was downloaded from GEO database (\u003ca href=\"http://www.ncbi.nlm.nih.gov/geo/\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/\u003c/a\u003e). The screening criteria for the gene expression datasets were as follows: (1) the study type was limited to expression profiling by array; (2) gene expression data in the intestinal mucosa of CD patients and normal individuals; (3) Each dataset contained for at least 100 samples; (4) analyzable processed data or raw data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData preprocessing and differential gene analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were preprocessed and analyzed using the R software (\u003ca href=\"https://www.r-project.org/\"\u003ehttps://www.r-project.org/\u003c/a\u003e) through the following steps: (1) The probe names of each gene were converted to gene symbols, moreover, when a target gene corresponded to multiple probes, the average expression values of the probes was used to represent the expression level of the gene; (2) genes were excluded if the gene expression level was zero in more than half of the samples; (3) genes lacking expression level data for over 30% of the samples were also removed. Differential expression analysis was performed using the \u0026ldquo;\u003cem\u003elimma\u003c/em\u003e\u0026rdquo; R package [19]. Adjusted p value \u0026lt; 0.05 and fold change \u0026gt;1.2 or fold change \u0026lt;-1.2 were set as the threshold for significant differential expression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmune infiltration analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe composition and proportion of 22 immune cells in the intestinal mucosa of CD patients and healthy individuals were estimated using the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) tool in combination with leukocyte signature matrix (LM22) based on gene expression profiles of the cells [16]. The permutations (perm) of the deconvolution algorithm were set at 1000.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of network and identification of hub genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe coexpression network of DEGs and the infiltration of immune cells was performed as previously described [17]. First, the soft thresholding power \u003cem\u003e\u0026beta;\u003c/em\u003e, to which coexpression similarity was raised to calculate adjacency, was calculated using the pickSoftThreshold function in the \u0026ldquo;\u003cem\u003eWGCNA\u003c/em\u003e\u0026rdquo; R package. Second, the samples were clustered to identify any obvious outliers. Third, the coexpression network was then constructed. Fourth, key gene modules were identified using hierarchical clustering and the dynamic tree cut function. Gene significance (GS) and module membership (MM) were then calculated to match modules to specific immune cells. According to the correlation between the immune cells and ME and p value, and the module with the highest correlation coefficient and the smallest p value was selected as the most relevant module for the immune cells. Finally, the hub genes in the relevant module for the immune cells were identified based on MM \u0026gt; 0.9 and GS \u0026gt; 0.7.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBiological process and pathway regulated by the genes in the modules were identified using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analysis via the \u0026ldquo;\u003cem\u003eclusterProfiler\u003c/em\u003e\u0026rdquo; R package \u0026nbsp;[20]. The cutoff of the q-value was set at 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLinear model and ROC curve analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Best linear model for immune cell and hub genes was derived using a stepwise forward linear regression analysis. The following statistic model was developed: \u003cem\u003ey\u003c/em\u003e=\u003cem\u003e\u0026beta;0\u003c/em\u003e+\u003cem\u003e\u0026beta;1x1\u003c/em\u003e+\u003cem\u003e\u0026beta;2\u003cem\u003ex2\u003c/em\u003e\u003c/em\u003e+\u0026hellip;+\u003cem\u003e\u0026beta;ixi\u003c/em\u003e\u003cem\u003e,\u0026nbsp;\u003c/em\u003ewhere\u0026nbsp;\u003cem\u003ey\u003c/em\u003e is the proportion of immune cell,\u0026nbsp;\u003cem\u003exi\u003c/em\u003e is the expression value of hub genes,\u0026nbsp;\u003cem\u003e\u0026beta;0\u0026nbsp;\u003c/em\u003ewas the intercept of the regression equation,\u0026nbsp;and\u0026nbsp;\u003cem\u003e\u0026beta;i\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eis the regression coefficients. \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe utility and accuracy of the hub genes for CD diagnosis were assessed by receiver operating characteristic\u0026nbsp;(ROC) analysis using the\u0026nbsp;\u0026ldquo;\u003cem\u003eROCR\u003c/em\u003e\u0026rdquo; R package [21]. The area under curve (AUC) was then calculated and screened for genes with AUC greater than 0.7.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were analyzed using R software (Rx64 4.0.3). Differences between two groups were analyzed using the Wilcoxon test, whereas the Kruskal-Wallis test used for multiple groups. The correlation between different immune cell subtypes to the intestinal mucosa of CD patients was performed using the Pearson correlation coefficient. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u0026nbsp;\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCD, Crohn\u0026rsquo;s disease; GEO, Gene Expression Omnibus; WGCNA, weighted gene co-expression network analysis; DEGs, differentially expressed genes; ROC, receiver operating characteristic; AUC, area under curve; CIBERSORT, Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts; GS, Gene significance; MM, module membership; KEGG, Kyoto Encyclopedia of Genes and Genomes; GO, Gene Ontology.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design: HS \u0026amp; QP; Administrative support: S-Y S; Provision of study materials or patients: HS \u0026amp; QP; Collection and assembly of data: HS \u0026amp; QP; Data analysis and interpretation: X-L Z, S-P Z; Manuscript writing: HS; Final approval of manuscript: All authors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are openly available in\u0026nbsp;GEO database (\u003ca href=\"http://www.ncbi.nlm.nih.gov/geo/\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interests \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmad T, Tamboli CP, Jewell D, Colombel JF: \u003cstrong\u003eClinical relevance of advances in genetics and pharmacogenetics of IBD.\u003c/strong\u003e\u003cem\u003eGastroenterology \u003c/em\u003e2004, \u003cstrong\u003e126\u003c/strong\u003e(6):1533-1549.\u003c/li\u003e\n\u003cli\u003eTorres J, Mehandru S, Colombel JF, Peyrin-Biroulet L: \u003cstrong\u003eCrohn's disease.\u003c/strong\u003e\u003cem\u003eLancet \u003c/em\u003e2017, \u003cstrong\u003e389\u003c/strong\u003e(10080):1741-1755.\u003c/li\u003e\n\u003cli\u003eBenchimol EI, Mack DR, Nguyen GC, Snapper SB, Li W, Mojaverian N, Quach P, Muise AM: \u003cstrong\u003eIncidence, outcomes, and health services burden of very early onset inflammatory bowel disease.\u003c/strong\u003e\u003cem\u003eGastroenterology \u003c/em\u003e2014, 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X, Sun H, Liu S, Liang B, Bai H, Wang S, Li S: \u003cstrong\u003eIdentification of a Prognostic Signature for Ovarian Cancer Based on the Microenvironment Genes.\u003c/strong\u003e\u003cem\u003eFront Genet \u003c/em\u003e2021, \u003cstrong\u003e12\u003c/strong\u003e:680413.\u003c/li\u003e\n\u003cli\u003eOttenhoff TH, Dass RH, Yang N, Zhang MM, Wong HE, Sahiratmadja E, Khor CC, Alisjahbana B, van Crevel R, Marzuki S, et al: \u003cstrong\u003eGenome-wide expression profiling identifies type 1 interferon response pathways in active tuberculosis.\u003c/strong\u003e\u003cem\u003ePLoS One \u003c/em\u003e2012, \u003cstrong\u003e7\u003c/strong\u003e(9):e45839.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Characteristics of training and validation datasets\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"112%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"39.795918367346935%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGSE179285\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Training set)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGSE20881\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Validation set)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003eSamples\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003eCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25.31645569620253%\"\u003e\n \u003cp\u003eHealthy controls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.050632911392405%\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.582278481012658%\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.050632911392405%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003eSite of mucosal collection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003eColon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25.31645569620253%\"\u003e\n \u003cp\u003eIleum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.050632911392405%\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.582278481012658%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.050632911392405%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e0.793\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25.31645569620253%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.050632911392405%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.582278481012658%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.050632911392405%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003eInflammation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003eInflamed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e0.650\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003eUninflamed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003ePlatform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003eGPL6480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003eGPL1708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNotes: One patient or healthy individual may have one or more samples.\u003c/p\u003e\n\u003cp id=\"isPasted\"\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Comparison of 22 proportion between CD and normal tissue\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"30.22284122562674%\"\u003e\n \u003cp\u003eImmune cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" width=\"69.77715877437326%\"\u003e\n \u003cp\u003eCIBERSORT fraction in % of all infiltrating immune cells (mean\u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.139720558882235%\"\u003e\n \u003cp\u003eCD tissue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.14171656686627%\"\u003e\n \u003cp\u003eNormal tissue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"40.7185628742515%\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\"\u003e\n \u003cp\u003eB cells naive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e0.0233\u0026plusmn;0.0331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.33426183844011%\"\u003e\n \u003cp\u003e0.033\u0026plusmn;0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.41225626740947%\"\u003e\n \u003cp\u003e0.2538\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\"\u003e\n \u003cp\u003eB cells memory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e0.0489\u0026plusmn;0.0542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.33426183844011%\"\u003e\n \u003cp\u003e0.0599\u0026plusmn;0.0713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.41225626740947%\"\u003e\n \u003cp\u003e0.7750\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\"\u003e\n \u003cp\u003ePlasma cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e0.0005\u0026plusmn;0.0024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.33426183844011%\"\u003e\n \u003cp\u003e0\u0026plusmn;0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.41225626740947%\"\u003e\n \u003cp\u003e0.3943\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\"\u003e\n \u003cp\u003eT cells CD8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e0.0441\u0026plusmn;0.0492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.33426183844011%\"\u003e\n \u003cp\u003e0.025\u0026plusmn;0.0369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.41225626740947%\"\u003e\n \u003cp\u003e0.0205\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\"\u003e\n \u003cp\u003eT cells CD4 naive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e0.0002\u0026plusmn;0.0015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.33426183844011%\"\u003e\n \u003cp\u003e0.0002\u0026plusmn;0.0013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.41225626740947%\"\u003e\n \u003cp\u003e0.4076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\"\u003e\n \u003cp\u003eT cells CD4 memory resting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e0.1427\u0026plusmn;0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.33426183844011%\"\u003e\n \u003cp\u003e0.1452\u0026plusmn;0.0735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.41225626740947%\"\u003e\n \u003cp\u003e0.7587\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\"\u003e\n \u003cp\u003eT cells CD4 memory activated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e0.0654\u0026plusmn;0.0517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.33426183844011%\"\u003e\n \u003cp\u003e0.0312\u0026plusmn;0.0328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.41225626740947%\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\"\u003e\n \u003cp\u003eT cells follicular helper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e0.0001\u0026plusmn;0.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.33426183844011%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.41225626740947%\"\u003e\n \u003cp\u003e0.5490\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\"\u003e\n \u003cp\u003eT cells regulatory Tregs.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e0.0301\u0026plusmn;0.0228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.33426183844011%\"\u003e\n \u003cp\u003e0.0385\u0026plusmn;0.0226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.41225626740947%\"\u003e\n \u003cp\u003e0.0337\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\"\u003e\n \u003cp\u003eT cells gamma delta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e0.0322\u0026plusmn;0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.33426183844011%\"\u003e\n \u003cp\u003e0.0444\u0026plusmn;0.0358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.41225626740947%\"\u003e\n \u003cp\u003e0.0201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\"\u003e\n \u003cp\u003eNK cells resting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e0.008\u0026plusmn;0.0175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.33426183844011%\"\u003e\n \u003cp\u003e0.0022\u0026plusmn;0.0096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.41225626740947%\"\u003e\n \u003cp\u003e0.0064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\"\u003e\n \u003cp\u003eNK cells activated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e0.0713\u0026plusmn;0.0472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.33426183844011%\"\u003e\n \u003cp\u003e0.0994\u0026plusmn;0.0472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.41225626740947%\"\u003e\n \u003cp\u003e0.0018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\"\u003e\n \u003cp\u003eMonocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e0.0087\u0026plusmn;0.0168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.33426183844011%\"\u003e\n \u003cp\u003e0.0015\u0026plusmn;0.0042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.41225626740947%\"\u003e\n \u003cp\u003e0.0188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\"\u003e\n \u003cp\u003eMacrophages M0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e0.1068\u0026plusmn;0.0643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.33426183844011%\"\u003e\n \u003cp\u003e0.0797\u0026plusmn;0.0613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.41225626740947%\"\u003e\n \u003cp\u003e0.0524\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\"\u003e\n \u003cp\u003eMacrophages M1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e0.0814\u0026plusmn;0.0465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.33426183844011%\"\u003e\n \u003cp\u003e0.0595\u0026plusmn;0.0346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.41225626740947%\"\u003e\n \u003cp\u003e0.0162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\"\u003e\n \u003cp\u003eMacrophages M2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e0.156\u0026plusmn;0.0637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.33426183844011%\"\u003e\n \u003cp\u003e0.1939\u0026plusmn;0.0672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.41225626740947%\"\u003e\n \u003cp\u003e0.0024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\"\u003e\n \u003cp\u003eDendritic cells resting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e0.0076\u0026plusmn;0.0142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.33426183844011%\"\u003e\n \u003cp\u003e0.0149\u0026plusmn;0.0273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.41225626740947%\"\u003e\n \u003cp\u003e0.0519\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\"\u003e\n \u003cp\u003eDendritic cells activated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.33426183844011%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.41225626740947%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\"\u003e\n \u003cp\u003eMast cells resting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e0.1088\u0026plusmn;0.0749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.33426183844011%\"\u003e\n \u003cp\u003e0.1437\u0026plusmn;0.0688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.41225626740947%\"\u003e\n \u003cp\u003e0.0114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\"\u003e\n \u003cp\u003eMast cells activated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e0.032\u0026plusmn;0.0524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.33426183844011%\"\u003e\n \u003cp\u003e0.0105\u0026plusmn;0.0223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.41225626740947%\"\u003e\n \u003cp\u003e0.0625\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\"\u003e\n \u003cp\u003eEosinophils\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e0.0094\u0026plusmn;0.0148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.33426183844011%\"\u003e\n \u003cp\u003e0.0096\u0026plusmn;0.0183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.41225626740947%\"\u003e\n \u003cp\u003e0.5390\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.22284122562674%\"\u003e\n \u003cp\u003eNeutrophils\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.030640668523677%\"\u003e\n \u003cp\u003e0.0226\u0026plusmn;0.0332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.33426183844011%\"\u003e\n \u003cp\u003e0.0077\u0026plusmn;0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.41225626740947%\"\u003e\n \u003cp\u003e0.0171\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes: P values in red indicate statistical significance (p \u0026lt; 0.05).\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"GBP4, Crohn’s disease, immune cells, biomarker","lastPublishedDoi":"10.21203/rs.3.rs-1144474/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1144474/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Extensive evidence has shown that immune cell infiltration is associated with the pathogenesis of Crohn’s disease (CD). In the present study, we explored the potential mechanism underlying the pathogenesis biomarkers for CD.\u003c/p\u003e\u003cp\u003eMethods: The GSE179285 dataset containing sequence data for intestinal mucosal was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in the intestinal mucosa of CD patients and healthy individuals were then identified. The infiltration pattern of 22 immune cell types was assessed using the CIBERSORT algorithm. The DEGs and 22 immune cell types were combined to find the key gene network using weighted gene co-expression network analysis (WGCNA), and pathway enrichment analyzes were performed on the hub module in the WGCNA. A linear regression model for the relationship between the expression of the hub genes in CD patients and infiltration of immune cells were also developed. The utility and accuracy of the hub genes for CD diagnosis were assessed using receiver operating characteristic\u0026nbsp;(ROC) analysis. The accuracy of the model was validated using GSE20881 dataset.\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u003c/p\u003e\u003cp\u003eResults: There were 1135 DEGs between the intestinal mucosal tissue of CD patients and healthy individuals. Of these DEGs, 711 genes were upregulated, whereas 424 of them were downregulated. There was also a significant difference in the infiltration of immune cells to the intestinal mucosal between the CD patients and healthy individuals. WGCNA revealed that the turquoise module genes were strongly correlated with the infiltration of M1 macrophages (cor=0.68, p=10\u003csup\u003e-16\u003c/sup\u003e). Pathway enrichment analysis further showed the genes in the turquoise module mainly regulated the secretion of interferon-gamma and other immune effector molecules. Finally, the expression of GBP4, the identified hub gene, strongly correlated with the infiltration of M1 macrophages (adjusted r-squared=0.661, p\u0026lt;2x10\u003csup\u003e-16\u003c/sup\u003e), and is a relatively good marker for CD diagnostic prediction (AUC=0.736). The relationship between GBP4 expression and infiltration of M1 macrophages (adjusted r-squared=0.435, p\u0026lt;2x10\u003csup\u003e-16\u003c/sup\u003e) and prognostic value of the gene (AUC=0.702) were verified using the GSE20881 validation dataset.\u003c/p\u003e\u003cp\u003eConclusion: GBP4 is a potential biomarker for accurate CD diagnosis. The expression of GBP4 promotes the infiltration of M1 macrophages to the intestinal mucosa of CD patients.\u003c/p\u003e","manuscriptTitle":"GBP4 is an Accurate Diagnostic Biomarker and a Potential Treatment Target for Crohn’s Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-12-10 15:18:18","doi":"10.21203/rs.3.rs-1144474/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b48cff31-cc37-48d7-bd86-444e9a3547b9","owner":[],"postedDate":"December 10th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":9067127,"name":"Biomedical Engineering"}],"tags":[],"updatedAt":"2021-12-10T15:18:19+00:00","versionOfRecord":[],"versionCreatedAt":"2021-12-10 15:18:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1144474","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1144474","identity":"rs-1144474","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}