Identification of Key Genes Related to Ankylosing Spondylitis Using WGCNA and Bioinformatics Analysis

Identification of Key Genes Related to Ankylosing Spondylitis Using WGCNA and Bioinformatics Analysis | 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 Identification of Key Genes Related to Ankylosing Spondylitis Using WGCNA and Bioinformatics Analysis Liyi Yuan, Zeqian Liang, Ronghai Wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3219142/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 Objective: Ankylosing spondylitis (AS) is a chronic inflammatory disease characterized by the inflammation of the spine and sacroiliac joints. Understanding the underlying immune cells and key genes associated with AS is crucial for unraveling its pathogenesis. In this study, we employed weighted gene co-expression network analysis (WGCNA) to identify immune cells and key genes involved in AS. The GSE11886 dataset, obtained from the GEO database, was utilized for the analysis of differentially expressed genes (DEGs). Subsequently, the WGCNA package was applied to screen for key modules and genes that correlated with clinical characteristics of AS. The intersection of DEGs obtained from the analysis and genes within the blue module led to the identification of key genes, which were further subjected to correlation analysis. Our findings revealed a total of 279 DEGs, including 123 up-regulated and 156 down-regulated genes, as determined by a volcano map. Additionally, WGCNA analysis unveiled a key module strongly associated with AS. Within this module, we identified 22 key genes, namely CLIC3, LY75, TNFAIP3, TNFAIP6, STAT1, GBP1, TNFSF13B, CD69, IFITM1, WLS, CNRIP1, LY86, ICAM4, NMRK2, DNASE2B, AMDHD1, TUBB2A, DEXI, TPD52L1, ASRGL1, CECR6, and FAM213B. The discovery of these modules and key genes provides a theoretical foundation for further exploration of the mechanisms underlying the development and progression of AS. Biological sciences/Computational biology and bioinformatics Biological sciences/Genetics Health sciences/Biomarkers ankylosing spondylitis WGCNA bioinformatics analysis key modules differentially expressed genes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Ankylosing spondylitis (AS) is an immune-mediated chronic inflammatory disease primarily affecting the spine and sacroiliac joints [ 1 , 2 ]. The most prevalent symptoms of AS include back pain and progressive spinal ankylosis [ 3 , 4 ]. AS can result in joint injury and fusion of the major sacroiliac joints, and in severe cases, it may lead to the formation of bamboo ridges in spondylosis [ 5 , 6 ]. Moreover, the disease manifests symptoms in other areas such as the knees, hands, and feet, causing pain and swelling that significantly impact patients' health and overall quality of life [ 7 , 8 ]. Despite extensive research, the exact cause of AS remains unclear, emphasizing the necessity for further molecular investigations into its etiology. Bioinformatics analysis, utilizing gene microarray data, is a practical approach for evaluating gene expression profiles and can contribute to the study of the occurrence and progression of etiological mechanisms [ 9 ]. Among the various bioinformatics techniques, weighted gene co-expression network analysis (WGCNA) stands out as a widely utilized method to explore complex gene regulatory networks [ 10 , 11 ]. By incorporating data from multiple samples and genes, WGCNA enables effective prediction of candidate biological biomarkers or therapeutic targets [ 12 , 13 ]. Furthermore, WGCNA allows for the investigation of associations between microarray data and clinical features, facilitating the identification of specific genes linked to diseases based on patient prognosis [ 14 , 15 ]. This study employed WGCNA to investigate gene expression profiles obtained from the Gene Expression Omnibus (GEO) database, a repository provided by the National Biotechnology Information Center (NCBI). The primary objective was to identify critical genes associated with ankylosing spondylitis. By utilizing this approach, we aim to establish a reference foundation for comprehending the occurrence and development of AS, as well as identifying potential biomarkers or therapeutic targets for this condition. The findings of this study hold the potential to enhance our understanding of AS at a molecular level, thereby opening avenues for improved diagnostic and therapeutic strategies. 2. Methods 2.1 Data Collection and Preprocessing The gene expression profile GSE11886 contained AS data downloaded from the GEO database [ 16 ] ( https://www.ncbi.nlm.nih.gov/geo/ ). GSE26712 consists of 9 normal samples and 7 AS samples. 2.2 Screening of differentially expressed genes (DEGs) The limma package in R language was used to standardize chip data and filter DEGs,Set |log2Fold Change| ≥ 1 and adjusted P < 0. 05 to analyze and identify genes with significant differences in expression. 2.3 Construction of WGCNA WGCNA was performed to explore the key modules and genes associated with AS [ 17 ]. The WGCNA package in R language was used to analyze the data of GSE11886. The optimal value of the weighted parameters of the adjacent functions was obtained by the pickSoftThreshold function in WGCNA package, which was used as a soft threshold for network construction. Using the similarity function of the topological overlap matrix (TOM), the adjacency value was transformed into TOM matrix with the appropriate function value. The genes were divided into different modules. Calculated the correlation of clinical characteristics of the module, selected the module with the highest correlation with hypertension and analyzed the genes in this module for further investigation. 2.4 Analysis of Key genes The essential genes were obtained by integrating the DEGs and genes in WGCNA trait-related module, and further correlation analysis was carried out. 3. Results 3.1 Identification of DEGs The data of GSE 11886 were divided into two groups: the normal control group and AS group, and the screening conditions were set: |1og2FC|>0.5 and P < 0.05. A total of 279 DEGs were screened out by a volcano map, including 123 up-regulated and 156 down-regulated genes ( Fig. 1 ). 3.2 Data screening for WGCNA The expression matrix of genes and samples was generated by normalization and gene ID transformation. The top 1000 genes with the highest average expression were selected to construct the gene co-expression module. Outliers were detected after processing the data set, and the results showed no apparent outliers in the data set, so the following step analysis was carried out directly to analyze the gene clustering module and clinical grouping phenotype ( Fig. 2 ). 3.3 Calculation of soft -thresholding power Through the algorithm of the WGCNA packet, β = 15 is selected as the soft threshold to build a scale-free network, as shown in Fig. 3 . 3.4 Construction of WGCNA The hierarchical clustering tree was constructed using the dynamic hybrid cutting method ( Fig. 4 a ), and 12 modules were obtained ( Fig. 4 b ). 3.5 Correlation Analysis between modules The gene modules obtained by hierarchical clustering were evaluated by correlation analysis. Then, we observed the correlation between modules; the darker the color, the higher the correlation between modules ( Fig. 5 a ). According to the degree of topological overlap, all genes were selected to make a heat map ( Fig. 5 b ). The heat map described the topological overlap matrix (TOM) between all modules included in the analysis; light colors indicated lower overlap, while darker red indicated increased overlap. 3.6 Correlation between gene expression and sample groups According to the Pearson correlation analysis, the association between gene modules and the sample phenotype was calculated, and the gene module's heat map and the sample's phenotype were plotted. The blue module (blue, cor = 0.62, P < 0.01) was the module with the highest correlation to ankylosing spondylitis ( Fig. 6 a ). We also found a good correlation between gene significance (GS) and module membership (MM) in the blue module (cor = 0.66, P < 0. 01), as shown in Fig. 6 b. Therefore, the blue module might be used as a hub module closely related to AS for further investigation. 3.7 Analysis of key genes Based on the intersection of genes in the blue module and DEGs, 22 key genes related to AS were obtained. They were CLIC3, LY75, TNFAIP3, TNFAIP6, STAT1, GBP1, TNFSF13B, CD69, IFITM1, WLS, CNRIP1, LY86, ICAM4, NMRK2, DNASE2B, AMDHD1, TUBB2A, DEXI, TPD52L1, ASRGL1, CECR6, and FAM213B. Furthermore, they were positively correlated, as shown in Fig. 7 . 4. Discussion AS is a complex disease influenced by various factors, and its etiology remains uncertain, resulting in the absence of a cure [ 18 ]. Consequently, there is an urgent need in clinical practice to unravel the molecular mechanisms underlying AS, identify potential therapeutic targets, and enhance treatment options. Recent studies have indicated that AS is a multifaceted autoimmune disorder involving multiple genes [ 19 , 20 ]. Thus, there is a necessity to develop novel biomarkers and explore potential molecular targets to prevent and treat AS effectively. WGCNA is an unsupervised hierarchical clustering method that can identify gene modules associated with disease phenotypes [ 21 , 22 ]. WGCNA enables the integration of highly interconnected genes in primary biological pathways [ 23 , 24 ], facilitating the identification of gene modules with significant biological implications [ 25 , 26 ]. By combining this analysis with clinical information, it is possible to identify key modules and potential hub genes related to the pathogenesis of the disease [ 27 ]. In this study, we employed WGCNA to analyze the GSE11886 dataset and explore modules associated with the clinical characteristics of AS. Through data mining and advanced bioinformatics analysis methods, we provide new research insights and potential candidate target molecules for investigating the pathogenesis of AS. In the present study, we constructed 22 gene co-expression modules and identified one module, the blue module, highly associated with AS. Subsequently, we intersected the differentially expressed genes obtained from differential gene expression (DEG) analysis with the genes within the blue module, resulting in the identification of 22 key genes: CLIC3, LY75, TNFAIP3, TNFAIP6, STAT1, GBP1, TNFSF13B, CD69, IFITM1, WLS, CNRIP1, LY86, ICAM4, NMRK2, DNASE2B, AMDHD1, TUBB2A, DEXI, TPD52L1, ASRGL1, CECR6, and FAM213B. Moreover, positive correlations were observed among these genes. The findings of this study hold the potential to enhance our understanding of the molecular mechanisms underlying AS and provide potential targets for accurate diagnosis and treatment. Tumor necrosis factor-alpha-inducible protein 3 (TNFAIP3) acts as an endogenous negative regulator of NF-κB signal transduction and has been implicated in various autoimmune and inflammatory diseases [ 28 , 29 ]. A recent study indicated a close relationship between the gene polymorphism of TNFAIP3 rs10499194 and reduced AS risk [ 30 ]. Furthermore, down-regulated expression of TNFAIP3 in AS patients' tissues holds diagnostic and predictive significance for AS [ 31 , 32 ]. Signal transducer and activator of transcription 1 (STAT1) plays a critical role in mediating gamma-interferon (IFNγ) signaling [ 33 , 34 ] and has been associated with the onset and progression of AS [ 35 ]. By utilizing WGCNA and bioinformatics analysis, this study successfully identified gene modules strongly correlated with AS. Furthermore, the intersection of differentially expressed genes with genes within the blue module revealed 22 key genes strongly associated with AS. Consequently, the results of this study provide essential target molecules for further exploration of the pathogenesis and treatment of AS. Declarations Acknowledgements ： We thank Huimin Wang for valuable discussion. Authors' contributions :Liyi Yuan analyzed and interpreted the data and Identification of Key Genes Related to Ankylosing Spondylitis. Zeqian Liang were responsible for data presentation.Ronghai Wu was a major contributor in writing the manuscript. All authors read and approved the finalmanuscript. Availability of data and materials ： All data generated or analyzed during this study are included in this publishedarticle. Competing interests:Competing interests. The authors declare that they have no competing interests, and All authors should confirm its accuracy. Funding ： No funding wasreceived Ethics approval and consent to participate ： Not applicable Patient consent for publication ： Not applicable References Wang T, Wang T, Meng S, Meng S, Chen P, Chen P, Wei L, Wei L, Liu C and Liu C. Comprehensive analysis of differentially expressed mRNA and circRNA in Ankylosing spondylitis patients' platelets. Experimental Cell Research 2021; 409: 112895-. Zhu W, He X, Cheng K, Zhang L, Chen D, Wang X, Qiu G, Cao X and Weng X. Ankylosing spondylitis: etiology, pathogenesis, and treatments. Bone Res 2019; 7: 22. 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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-3219142","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":223303768,"identity":"9b09f84d-cce6-43fb-9d55-12c913b251d2","order_by":0,"name":"Liyi Yuan","email":"","orcid":"","institution":"Beihang University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Liyi","middleName":"","lastName":"Yuan","suffix":""},{"id":223303769,"identity":"3f4328a2-bf05-4232-b184-a2fb07c7c132","order_by":1,"name":"Zeqian Liang","email":"","orcid":"","institution":"University of Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zeqian","middleName":"","lastName":"Liang","suffix":""},{"id":223303770,"identity":"2629b4a9-8e49-473b-a6fd-f379f68b0e54","order_by":2,"name":"Ronghai Wu","email":"data:image/png;base64,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","orcid":"","institution":"","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ronghai","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2023-07-31 04:29:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3219142/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3219142/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":41182155,"identity":"32696d59-8311-45f1-89c4-ea107815d1e7","added_by":"auto","created_at":"2023-08-07 13:57:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":155512,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eIdentification of DEGs. 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A, The correlation analysis between modules. B, The topological overlap matrix (TOM) between all modules.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-3219142/v1/0cb70c196152a852c101089c.png"},{"id":41182151,"identity":"764aeb5c-f200-4645-acfd-8dc0efb4d589","added_by":"auto","created_at":"2023-08-07 13:57:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":305492,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCorrelation between gene expression and sample groups. A, The association between gene modules and the phenotype of the sample. B, The correlation between gene significance (GS) and module membership (MM) in the blue module.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-3219142/v1/c53bcde4050646a1b9690a19.png"},{"id":41183341,"identity":"9e1deb01-227f-4091-b8e2-e50f604f1f66","added_by":"auto","created_at":"2023-08-07 14:05:15","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":135815,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAnalysis of key genes. Based on the intersection of genes in the blue module and DEGs, 22 key genes related to AS were obtained.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-3219142/v1/4487c4b17bcc304eb271374d.png"},{"id":41959855,"identity":"5a90a05b-55da-4884-bc7c-3a585d1a67db","added_by":"auto","created_at":"2023-08-22 21:07:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1603989,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3219142/v1/cce8f56c-f0bf-4800-9125-7eba44ab2edb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of Key Genes Related to Ankylosing Spondylitis Using WGCNA and Bioinformatics Analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cem\u003eAnkylosing spondylitis (AS) is an immune-mediated chronic inflammatory disease primarily affecting the spine and sacroiliac joints\u003c/em\u003e [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. \u003cem\u003eThe most prevalent symptoms of AS include back pain and progressive spinal ankylosis\u003c/em\u003e [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. \u003cem\u003eAS can result in joint injury and fusion of the major sacroiliac joints, and in severe cases, it may lead to the formation of bamboo ridges in spondylosis\u003c/em\u003e [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. \u003cem\u003eMoreover, the disease manifests symptoms in other areas such as the knees, hands, and feet, causing pain and swelling that significantly impact patients' health and overall quality of life\u003c/em\u003e [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. \u003cem\u003eDespite extensive research, the exact cause of AS remains unclear, emphasizing the necessity for further molecular investigations into its etiology.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eBioinformatics analysis, utilizing gene microarray data, is a practical approach for evaluating gene expression profiles and can contribute to the study of the occurrence and progression of etiological mechanisms\u003c/em\u003e [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. \u003cem\u003eAmong the various bioinformatics techniques, weighted gene co-expression network analysis (WGCNA) stands out as a widely utilized method to explore complex gene regulatory networks\u003c/em\u003e [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. \u003cem\u003eBy incorporating data from multiple samples and genes, WGCNA enables effective prediction of candidate biological biomarkers or therapeutic targets\u003c/em\u003e [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. \u003cem\u003eFurthermore, WGCNA allows for the investigation of associations between microarray data and clinical features, facilitating the identification of specific genes linked to diseases based on patient prognosis\u003c/em\u003e [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003eThis study employed WGCNA to investigate gene expression profiles obtained from the Gene Expression Omnibus (GEO) database, a repository provided by the National Biotechnology Information Center (NCBI). The primary objective was to identify critical genes associated with ankylosing spondylitis. By utilizing this approach, we aim to establish a reference foundation for comprehending the occurrence and development of AS, as well as identifying potential biomarkers or therapeutic targets for this condition. The findings of this study hold the potential to enhance our understanding of AS at a molecular level, thereby opening avenues for improved diagnostic and therapeutic strategies.\u003c/em\u003e \u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Collection and Preprocessing\u003c/h2\u003e \u003cp\u003e \u003cem\u003eThe gene expression profile GSE11886 contained AS data downloaded from the GEO database\u003c/em\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] \u003cem\u003e(\u003c/em\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cem\u003e). GSE26712 consists of 9 normal samples and 7 AS samples.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Screening of differentially expressed genes (DEGs)\u003c/h2\u003e \u003cp\u003e \u003cem\u003eThe limma package in R language was used to standardize chip data and filter DEGs,Set |log2Fold Change| \u0026ge; 1 and adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0. 05 to analyze and identify genes with significant differences in expression.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Construction of WGCNA\u003c/h2\u003e \u003cp\u003e \u003cem\u003eWGCNA was performed to explore the key modules and genes associated with AS\u003c/em\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. \u003cem\u003eThe WGCNA package in R language was used to analyze the data of GSE11886. The optimal value of the weighted parameters of the adjacent functions was obtained by the pickSoftThreshold function in WGCNA package, which was used as a soft threshold for network construction. Using the similarity function of the topological overlap matrix (TOM), the adjacency value was transformed into TOM matrix with the appropriate function value. The genes were divided into different modules. Calculated the correlation of clinical characteristics of the module, selected the module with the highest correlation with hypertension and analyzed the genes in this module for further investigation.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Analysis of Key genes\u003c/h2\u003e \u003cp\u003e \u003cem\u003eThe essential genes were obtained by integrating the DEGs and genes in WGCNA trait-related module, and further correlation analysis was carried out.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Identification of DEGs\u003c/h2\u003e \u003cp\u003e \u003cem\u003eThe data of GSE 11886 were divided into two groups: the normal control group and AS group, and the screening conditions were set: |1og2FC|\u0026gt;0.5 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. A total of 279 DEGs were screened out by a volcano map, including 123 up-regulated and 156 down-regulated genes (\u003c/em\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cem\u003e).\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Data screening for WGCNA\u003c/h2\u003e \u003cp\u003e \u003cem\u003eThe expression matrix of genes and samples was generated by normalization and gene ID transformation. The top 1000 genes with the highest average expression were selected to construct the gene co-expression module. Outliers were detected after processing the data set, and the results showed no apparent outliers in the data set, so the following step analysis was carried out directly to analyze the gene clustering module and clinical grouping phenotype (\u003c/em\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cem\u003e).\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Calculation of soft -thresholding power\u003c/h2\u003e \u003cp\u003e \u003cem\u003eThrough the algorithm of the WGCNA packet, β\u0026thinsp;=\u0026thinsp;15 is selected as the soft threshold to build a scale-free network, as shown in\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Construction of WGCNA\u003c/h2\u003e \u003cp\u003e \u003cem\u003eThe hierarchical clustering tree was constructed using the dynamic hybrid cutting method (\u003c/em\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea\u003cem\u003e), and 12 modules were obtained (\u003c/em\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb\u003cem\u003e).\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Correlation Analysis between modules\u003c/h2\u003e \u003cp\u003e \u003cem\u003eThe gene modules obtained by hierarchical clustering were evaluated by correlation analysis. Then, we observed the correlation between modules; the darker the color, the higher the correlation between modules (\u003c/em\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea\u003cem\u003e). According to the degree of topological overlap, all genes were selected to make a heat map (\u003c/em\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb\u003cem\u003e). The heat map described the topological overlap matrix (TOM) between all modules included in the analysis; light colors indicated lower overlap, while darker red indicated increased overlap.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Correlation between gene expression and sample groups\u003c/h2\u003e \u003cp\u003e \u003cem\u003eAccording to the Pearson correlation analysis, the association between gene modules and the sample phenotype was calculated, and the gene module's heat map and the sample's phenotype were plotted. The blue module (blue, cor\u0026thinsp;=\u0026thinsp;0.62, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) was the module with the highest correlation to ankylosing spondylitis (\u003c/em\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea\u003cem\u003e). We also found a good correlation between gene significance (GS) and module membership (MM) in the blue module (cor\u0026thinsp;=\u0026thinsp;0.66, P\u0026thinsp;\u0026lt;\u0026thinsp;0. 01), as shown in\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb. \u003cem\u003eTherefore, the blue module might be used as a hub module closely related to AS for further investigation.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Analysis of key genes\u003c/h2\u003e \u003cp\u003e \u003cem\u003eBased on the intersection of genes in the blue module and DEGs, 22 key genes related to AS were obtained. They were CLIC3, LY75, TNFAIP3, TNFAIP6, STAT1, GBP1, TNFSF13B, CD69, IFITM1, WLS, CNRIP1, LY86, ICAM4, NMRK2, DNASE2B, AMDHD1, TUBB2A, DEXI, TPD52L1, ASRGL1, CECR6, and FAM213B. Furthermore, they were positively correlated, as shown in\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e \u003cem\u003eAS is a complex disease influenced by various factors, and its etiology remains uncertain, resulting in the absence of a cure\u003c/em\u003e [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. \u003cem\u003eConsequently, there is an urgent need in clinical practice to unravel the molecular mechanisms underlying AS, identify potential therapeutic targets, and enhance treatment options. Recent studies have indicated that AS is a multifaceted autoimmune disorder involving multiple genes\u003c/em\u003e [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. \u003cem\u003eThus, there is a necessity to develop novel biomarkers and explore potential molecular targets to prevent and treat AS effectively.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eWGCNA is an unsupervised hierarchical clustering method that can identify gene modules associated with disease phenotypes\u003c/em\u003e [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003cem\u003eWGCNA enables the integration of highly interconnected genes in primary biological pathways\u003c/em\u003e [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], \u003cem\u003efacilitating the identification of gene modules with significant biological implications\u003c/em\u003e [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. \u003cem\u003eBy combining this analysis with clinical information, it is possible to identify key modules and potential hub genes related to the pathogenesis of the disease\u003c/em\u003e [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. \u003cem\u003eIn this study, we employed WGCNA to analyze the GSE11886 dataset and explore modules associated with the clinical characteristics of AS. Through data mining and advanced bioinformatics analysis methods, we provide new research insights and potential candidate target molecules for investigating the pathogenesis of AS.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eIn the present study, we constructed 22 gene co-expression modules and identified one module, the blue module, highly associated with AS. Subsequently, we intersected the differentially expressed genes obtained from differential gene expression (DEG) analysis with the genes within the blue module, resulting in the identification of 22 key genes: CLIC3, LY75, TNFAIP3, TNFAIP6, STAT1, GBP1, TNFSF13B, CD69, IFITM1, WLS, CNRIP1, LY86, ICAM4, NMRK2, DNASE2B, AMDHD1, TUBB2A, DEXI, TPD52L1, ASRGL1, CECR6, and FAM213B. Moreover, positive correlations were observed among these genes. The findings of this study hold the potential to enhance our understanding of the molecular mechanisms underlying AS and provide potential targets for accurate diagnosis and treatment.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eTumor necrosis factor-alpha-inducible protein 3 (TNFAIP3) acts as an endogenous negative regulator of NF-κB signal transduction and has been implicated in various autoimmune and inflammatory diseases\u003c/em\u003e [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. \u003cem\u003eA recent study indicated a close relationship between the gene polymorphism of TNFAIP3 rs10499194 and reduced AS risk\u003c/em\u003e [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. \u003cem\u003eFurthermore, down-regulated expression of TNFAIP3 in AS patients' tissues holds diagnostic and predictive significance for AS\u003c/em\u003e [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. \u003cem\u003eSignal transducer and activator of transcription 1 (STAT1) plays a critical role in mediating gamma-interferon (IFNγ) signaling\u003c/em\u003e [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] \u003cem\u003eand has been associated with the onset and progression of AS\u003c/em\u003e [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003eBy utilizing WGCNA and bioinformatics analysis, this study successfully identified gene modules strongly correlated with AS. Furthermore, the intersection of differentially expressed genes with genes within the blue module revealed 22 key genes strongly associated with AS. Consequently, the results of this study provide essential target molecules for further exploration of the pathogenesis and treatment of AS.\u003c/em\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003cem\u003e：\u003c/em\u003e\u003cem\u003eWe thank Huimin Wang for valuable discussion.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors' contributions :Liyi Yuan analyzed and interpreted the data and Identification of Key Genes Related to Ankylosing Spondylitis. Zeqian Liang were responsible for data presentation.Ronghai Wu was a major contributor in writing the manuscript. All authors read and approved the finalmanuscript.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003cem\u003e：\u003c/em\u003e\u003cem\u003eAll data generated or analyzed during this study are included in this publishedarticle.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests:Competing interests. The authors declare that they have no competing interests, and All authors should confirm its accuracy.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003cem\u003e：\u003c/em\u003e\u003cem\u003eNo funding wasreceived\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003cem\u003e：\u003c/em\u003e\u003cem\u003eNot applicable\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePatient consent for publication\u003c/em\u003e\u003cem\u003e：\u003c/em\u003e\u003cem\u003eNot applicable\u003c/em\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWang T, Wang T, Meng S, Meng S, Chen P, Chen P, Wei L, Wei L, Liu C and Liu C. Comprehensive analysis of differentially expressed mRNA and circRNA in Ankylosing spondylitis patients\u0026apos; platelets. 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Identification of key genes in Ankylosing spondylitis. Immunology Letters 2018; 23:208-209.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ankylosing spondylitis, WGCNA, bioinformatics analysis, key modules, differentially expressed genes","lastPublishedDoi":"10.21203/rs.3.rs-3219142/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3219142/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eObjective:\u003c/em\u003e Ankylosing spondylitis (AS) is a chronic inflammatory disease characterized by the inflammation of the spine and sacroiliac joints. Understanding the underlying immune cells and key genes associated with AS is crucial for unraveling its pathogenesis. In this study, we employed weighted gene co-expression network analysis (WGCNA) to identify immune cells and key genes involved in AS. The GSE11886 dataset, obtained from the GEO database, was utilized for the analysis of differentially expressed genes (DEGs). Subsequently, the WGCNA package was applied to screen for key modules and genes that correlated with clinical characteristics of AS. The intersection of DEGs obtained from the analysis and genes within the blue module led to the identification of key genes, which were further subjected to correlation analysis. Our findings revealed a total of 279 DEGs, including 123 up-regulated and 156 down-regulated genes, as determined by a volcano map. Additionally, WGCNA analysis unveiled a key module strongly associated with AS. Within this module, we identified 22 key genes, namely CLIC3, LY75, TNFAIP3, TNFAIP6, STAT1, GBP1, TNFSF13B, CD69, IFITM1, WLS, CNRIP1, LY86, ICAM4, NMRK2, DNASE2B, AMDHD1, TUBB2A, DEXI, TPD52L1, ASRGL1, CECR6, and FAM213B. The discovery of these modules and key genes provides a theoretical foundation for further exploration of the mechanisms underlying the development and progression of AS.\u003c/p\u003e","manuscriptTitle":"Identification of Key Genes Related to Ankylosing Spondylitis Using WGCNA and Bioinformatics Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-08-07 13:57:11","doi":"10.21203/rs.3.rs-3219142/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":"81bf2e7c-7e6b-494c-b546-b559efb93b4f","owner":[],"postedDate":"August 7th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":23731112,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":23731113,"name":"Biological sciences/Genetics"},{"id":23731114,"name":"Health sciences/Biomarkers"}],"tags":[],"updatedAt":"2023-08-22T20:59:20+00:00","versionOfRecord":[],"versionCreatedAt":"2023-08-07 13:57:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3219142","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3219142","identity":"rs-3219142","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}