Identification of three-gene signature to diagnose rheumatoid arthritis through WGCNA and machine learning methods

Identification of three-gene signature to diagnose rheumatoid arthritis through WGCNA and machine learning methods | 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 Identification of three-gene signature to diagnose rheumatoid arthritis through WGCNA and machine learning methods Ying Kai WU, Cai-De Liu, Chao liu, ying kai WU, Zong-Gang Xie This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3969525/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 Rheumatoid arthritis(RA)is a systemic immune-related disease characterized by synovial inflammation and destruction of joint cartilage.The pathogenesis of RA remains unclear,and there is an urgent need to discover new diagnostic markers with high sensitivity and specificity.The aim of this study was to identify new potential biomarkers in the synovium for diagnosing rheumatoid arthritis and to investigate their association with immune infiltration. Method We downloaded four datasets containing 51 RA and 36 healthy synovium samples from the Gene Expression Omnibus(GEO)database.Differentially expressed genes(DEGs)were identified with the help of R program.Then various enrichment analysis were conducted.Subsequently,WGCNA,random forest(RF),support vector machine-recursive feature elimination(SVM-RFE),least absolute shrinkage and selection operator(LASSO)were used to identify the hub genes for RA diagnosis.Receiver operating characteristic curves(ROC)and nomogram models were used to validate the specificity and sensitivity of hub genes.Additionally,we analyzed the infiltration levels of 28 immune cells in the expression profile and their relationship with hub genes using single-sample gene set enrichment analysis (ssGSEA). Results Three hub genes(RRM2,DLGAP5 and KIF11)were identified through WGCNA,Lasso,SVM-RFE and RF algorithms.These hub genes showed the strong correlation with T cells,Natural killer cells and Macrophage cells indicated by the analysis of immune cell infiltration. Conclusion A nomogram model for the diagnosis of RA based on RRM2,DLGAP5 and KIF11 has been established,providing diagnosis and treatment targets of RA. Rheumatoid arthritis(RA) Hub genes Machine learning Immune cell infiltration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 1. Introduction RA is a systemic autoimmune disease characterized by chronic inflammatory proliferation of synovial membranes and cartilage destruction,which can have a serious impact on the physical and mental health of patient[ 1 ].Although RA does not directly lead to patient mortality,its systemic inflammatory damage can affect the function of organs such as the heart,lungs and kidneys,reducing the quality of life for patient[ 2 , 3 ].The pathogenesis of RA is complex and involves multiple aspects such as genetics,environment and etabolism,what , s more the mechanisms has not yet been stematically elucidated[ 4 , 5 ]. According to recent research, different types of immune cells such as B cells, T cells, and macrophages are closely linked to the development of RA[ 6 ]. Other important immune cells, including natural killer cells (NK cells), mast cells, and dendritic cells (DCs), also play a role in the development or advancement of RA[ 7 – 9 ].Currently, research on the treatment and pathogenesis of RA is increasing, but there is still a lack of highly specific and sensitive biomarkers for early diagnosis of RA. Bioinformatics is an emerging discipline that combines biology, mathematics, and information technology, showing its outstanding performance in disease detection, biomarker screening, identification of high-risk patients, and prognosis assessment[ 10 ]. WCGNA is currently a commonly used method for screening disease biomarkers and treatment targets. Machine learning algorithms, as a subset of artificial intelligence, can allow computers to learn from data to predict disease-related gene features, and are also widely used in research on biomarkers, disease mechanisms, and treatment targets[ 11 ]. The article comprehensively uses bioinformatics and various machine learning algorithms to integrate and analyze multiple gene datasets, in order to explore and discover more accurate diagnostic and therapeutic targets for RA, providing new directions for subsequent experimental research. A total of 4 synovial microarray datasets of RA were downloaded, using bioinformatics approaches to obtain intersection of DEGs and key genes of WGCNA, then using LASSO, SVM-REF, and Randomforest machine learning algorithms identify potential RA diagnostic markers and validate their diagnostic ability for RA. Additionally,we analyzed the infiltration levels of 28 immune cells in the expression profile and their relationship with hub genes using ssGSEA. 2. MATERIALS AND METHODS 2.1. Data Collection and Preprocessing The process depicted in Fig. 1.Firstly,obtaining gene expression datasets for RA synovial samples (GSE77298, GSE55457, GSE55235, GSE12021) from the GEO database ( https://www.ncbi.nlm.nih.gov/geo/ )[ 12 ]. These datasets totally included 87 synovial samples, comprising 36 healthy control samples and 51 RA samples (refer to Table 1 ). GSE55457 was utilized as a validation dataset, while the other datasets were consolidated for data analysis. Additionally, we retrieved platform annotation information and converted gene probes to gene symbols. We then employed the "sva" R package to merge GSE77298, GSE55457 and GSE55235 datasets using R software (version 4.3.1)[ 13 ]. The common genes across each dataset were identified for further analysis. Table 1 Information of datasets obtained from GEO Datasets Platform Total Sample Number Normal Sample Number RA Sample Number GSE55235 GLP96 30 10 10 GSE77298 GLP96 23 7 16 GSE12021 GLP96 21 9 12 GSE55457 GLP570 33 10 13 2.2. Identification of DEGs and enrichment analyses The DEGs were identified with the help of “limma” package.|log FC| ≥ 1 and adjust.p.value > 0.05 were used as the cut-off for screening DEGs[ 14 ]. Heatmaps and volcano maps were displayed by "pheatmap" and "ggplot2" packages. The Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was conducted with a cut-off of p < 0.05[ 15 ]. The gene set variation analysis (GSVA) was performed to calculate the normalized Enrichment score (NES) under the background of the hallmark gene set (c2.cp.kegg.v7.2) by the ‘GSVA’ R package, the threshold of the p-value and FDR were set as 0.05 and 0.25 respectivility[ 16 ].We also used Gene set enrichment analysis (GSEA) to identify the biological attribute and gene function of all genes in the train set by R package ‘clusterProfiler’, and the significant threshold were seted as p-value < 0.05 and FDR < 0.25[ 17 ]. 2.3. Construction of co-expression network Using the WGCNA package to construct a weighted gene co-expression network and identify hub genes[ 18 ]. First, calculate the expression correlation coefficients of all genes and construct a similarity matrix. Convert the similarity matrix into a topological matrix and use the topological overlap measure (TOM) to describe the association between genes. Determine the soft threshold (power) based on the correlation between genes and construct a gene clustering tree. Identify expression modules based on gene expression correlation coefficients, with a minimum of 30 genes per module and a module correlation coefficient set to 0.9. Hub genes are determined by the connectivity of genes within the module. Calculate the Pearson correlation coefficient based on gene expression trends and select the genes with the module membership (MM)>0.8 within the module as candidate hub genes. 2.4. Screening of hub genes Using the Venn package to obtain the intersection genes of DEGs and WGCNA candidate hub genes in characteristic module. The glmnet package of R software for LASSO algorithm, the e1071 package for SVM-RFE, and the random forest algorithm of the RandomForest package were conducted based on the intersection genes. Ultimately, hub genes were obtained by identifying the overlapping genes derived from the three machine learning methods using a Venn diagram. 2.5. Constructing Nomogram model and validation of hub genes A nomogram model for predicting RA was constructed using the rms package[ 19 ]. The predictive power of the nomogram model was assessed using the calibration curve. A decision curve was used to assess the clinical utility of the nomogram model. A receiver operating characteristic (ROC) curve was created using the R package pROC function to determine the diagnostic value of the hub genes and the nomogram model for RA in the training and validation sets. 2.6. Correlation between immune cell infiltration and hub genes The relative infiltration levels of 28 immune cells in the train set were quantified using the single-sample GSEA (ssGSEA) algorithm[ 20 ]. Barplot were used to show the differential expression levels of 28 immune-infiltrating cells. Spearman correlations of 28 immune-infiltrating cells with hub genes were calculated and then visualized using the "ggplot2" software package. 2.7. Co-expression network of identified hub genes We used the onlinetool GeneMANIA ( https://genemania.org )[ 21 ]to create hub genes , co-expression network. 2.8. Functional enrichment analysis of hub genes The online tool Enrichr[ 22 ] ( https://maayanlab.cloud/Enrichr/ ) was ued to elucidate the Biological process (BP), cellular component (CC) and molecular function (MF), KEGG ,WikiPathways, Reactome enrichment analysis of 3 hub genes [ 19 ]. The significant threshold of adj.P.value was < 0.05 2.9. Transcription factor and miRNA of the 3 hub genes JASPAR database was used to find the transcription factors (TFs) that frequently bind to 3 hub genes.MicroRNAs (miRNAs) that interacted with our hub genes were obtained from online platform MirTarbase. 2.10. Extraction potential small molecules of RA We got access to DSigDB database through the Enrichr platform,and got the top 10 small molecules that could suppress the expression of hub genes. 3. Results 3.1.Identifying the DEGs A total of 575 DEGs(including 383 up-regulate genes and 192 down-regulate genes) were identified between RA and normal individuals. The volcano plot of the DEGs were visualized with the top 10 upregulated and downregulated DEGs demonstrated(Fig. 2.A). In addition the expression level of the top 50 up-regulated and top 50 down-regulated genes were shown in the heatmap.(Fig. 2.B). 3.2. Functional enrichment analysis GSEA was used to depict the signal pathways involved in RA. The top five enriched pathways were chemokine signaling pathway,cytokine-cytokine receptor interaction,intestinal immune network fo IgA production,rheumatoid arthritis and viral protein interaction with cytokine and cytokine(Fig. 3.A). The results of GSVA also indicated that immunity and inflammation pathways, such as chemokine singaling pathway,natural killer cell-mediated immunity, B-cell receptor signaling pathway,primary immunodeficiency and intestinal immune network for IgA production were enriched in RA group(Fig. 3.B). The BP of GO showed that DEGs were enriched in mononuclear cell differentiation, leukocyte cell–cell and immune response-regulating cell surface receptor signaling pathway; in the MF term, DEGs were mostly related to antigen binding,immune receptor activity and chemokine activity;the CC terms emphasized external side of plasma membrane and clathrin-coated vesicle membrane(Fig. 4.A). KEGG pathway analysis showed that DEGs were enriched in cytokine-cytokine receptor interaction, chemokine signaling pathway and rheumatoid arthritis which were almost similar to the GSEA analysis(Fig. 4.B). 3.3. WGCNA construction and hub module identification Using the "WGCNA" package, samples in train set were clustered.Subsequently, unscaled connectivity index and average connectivity analysis were performed. When the soft threshold β = 8, the network reached an unscaled topological threshold of 0.9 (Figure.5.A). By dynamic tree cutting and calculation, 11gene modules were obtained(Figure.5.B). Correlation analysis was performed between the 11 modules with the normal and RA group, resulting in a correlation heatmap (Fig. 5.C). The salmon module had the strongest correlation with the RA (r = 0.73, P < 0.001), was identified as the key module for RA. Based on filtering criteria we identified 17 candidate hub genes in the salmon module (Fig. 5.D). 3.4. Screening of hub genes By intersecting the DEGs and candidate hub genes,13 intersection genes were obtained(Fig. 6.A). The 13 intersection genes were then submitted into three machine-learning algorithms including LASSO,SVM-RFE and RandomForest.LASSO resulting in four hub genes (DLGAP5,KIF11,MXRA5 and RRM2)(Fig. 6.B,6.C). SVM identified 5 hub genes(DLGAP5,KIF11,RRM2,TOP2A and PBK)(Fig. 6.D),RandomForest identified 7 hub genes(DLGAP5,KIF11,RRM2,MXRA5,COL5A1,PBK and ASPM)(Fig. 6.E,6.F)Finally, we got 3 hub genes—DLGAP5,RRM2 and KIF11 by overlapping from these three machine learning methods(Fig. 6.G). 3.5. Constructing Nomogram model and validation A nomogram model was then constructed based on 3 hub genes in the train set to predict the risk of RA (Fig. 7.A). The nomogram model was testified to have best predictive and clinical efficiency for RA by calibration curves (Fig. 7.B) and decision curve analysis (DCA) (Fig. 7.C) respectively.The AUC of the nomogram model and 3 hub genes were also calculated and showed(Fig. 7.D,7.E). Next,we constructed all those procedure in the validation set,which showed a perfect match with the results in train set(Fig. 8). 3.6. Correlation between immune cell infiltration and hub genes The distribution of 28 immune cells in thetrain set was demonstrated in Fig. 9.A. The results demonstrated a significantly higher infiltration of activated CD4 T cells, activied B cells,and activated dendritic cells in RA,indicating the important role playing in RA. (Fig. 9.B). Correlation analysis of the 28 immune cells with hub genes demonstrated various T cells,B cells,natural killer cells and macrophage cells were positively correlated with those 3 hub genes(Fig. 9.C). 3.8. Function analysis of hub genes In order to elucidate the biological functions of the identified hub genes, we constructed a comprehensive gene interaction network utilizing data from the geneMANIA database (Fig. 10). This network comprised physical interactions, co -expression relationships, predicted interactions, co-localization patterns, genetic interactions, pathway interactions, and shared protein domains. Our findings indicated that these hub genes are primarily associated with the mitotic nuclear division,spindle,microtuble cytoskeleton organization involved in mitosis and spindle organization. Furthermore, to discern specific biological roles of these 3 hub genes, we conducted an enrichment analysis. Figure 11.A, 11.B, and 11.C illustrate the most enriched terms in the cellular component, biological process and molecular function analyses of Gene Ontology terms. Additionally, Fig. 12.A, 12.B, 12.C depict the most significant pathways based on data from Reactome ,WikiPathways, and KEGG batabases, respectively. 3.9. Identification of Regulatory Signatures The interplay between the 3 hub genes and transcription factor (TF) regulators is depicted in Fig. 13.A, while the relationships between the hub genes and microRNA (miRNA) regulators are illustrated in Fig. 13.B. In total, we identified 18 TFs and 17 miRNAs as regulatory signatures through the analysis of TF-gene and miRNA-gene interaction networks. 3.10. Discovery of Potential Small Molecules We generated potential small molecule findings based on odds ratios. Table 2 presents the top 10 potential small molecules targeting the hub genes sourced from the DSigDB database. Table 2 Top 10 small molecule drugs for RA Term Overlap P-value Adjusted P-value Odds Ratio Combined Score Genes LUCANTHONE CTD 00006227 3/213 1.19E-06 1.86E-04 59361 809723.5033 RRM2;KIF11;DLGAP5 0173570-0000 PC3 DOWN 2/43 1.35E-05 5.35E-04 973.4634146 10913.3726 KIF11;DLGAP5 Phytoestrogens CTD 00007437 2/48 1.69E-05 5.35E-04 867.4347826 9531.856143 RRM2;DLGAP5 etoposide MCF7 DOWN 2/48 1.69E-05 5.35E-04 867.4347826 9531.856143 KIF11;DLGAP5 methotrexate MCF7 DOWN 2/52 1.99E-05 5.35E-04 797.88 8638.620968 KIF11;DLGAP5 piroxicam CTD 00006571 3/549 2.06E-05 5.35E-04 58353 629719.0634 RRM2;KIF11;DLGAP5 troglitazone CTD 00002415 3/651 3.43E-05 7.65E-04 58047 596691.1364 RRM2;KIF11;DLGAP5 apigenin MCF7 DOWN 2/87 5.60E-05 0.0010851 468.5176471 4587.228583 RRM2;KIF11 pyrvinium MCF7 DOWN 2/92 6.26E-05 0.0010851 442.3777778 4281.648144 RRM2;KIF11 resveratrol MCF7 DOWN 2/104 8.01E-05 0.001249052 390.0980392 3679.655006 KIF11;DLGAP5 4. Discussion RA is a chronic inflammatory disease that currently lacks early diagnostic indicators[ 6 ]. Recent studies have highlighted the close association of various immune cells, such as B cells, T cells, and macrophages, with the pathogenesis of RA[ 23 ]. Therefore, the exploration of new diagnostic biomarkers and their relationship with immune cell infiltration patterns holds significant implications for advancing our understanding of RA's pathophysiology.To address this, we gathered four RA synovial microarray datasets from the GEO database and identified 575 differentially expressed genes (DEGs) between RA and healthy controls (HC). Enrichment analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO), Gene Set Enrichment Analysis (GSEA) and GSVA revealed a robust correlation between RA and the immune response. Furthermore, through the utilization of WGCNA and machine learning algorithms, we identified RRM2, DLAGP5 and KIF11 as potential diagnostic markers for RA. Our findings unveiled that macrophages play an important role in immune cells infiltrating the synovium. In contrast to tissue-resident macrophages (TRM), infiltrating macrophages may originate from various monocyte subpopulations in the blood and possess a high level of adaptability. For instance, in mice, they can arise from classical Ly6C + or patrolling Ly6C- monocytes[ 24 , 25 ]. A recent comprehensive analysis of immune cell status in RA patients, utilizing single-cell RNA-seq, Bulk RNA-seq, and mass spectrometry flow cytometry, identified 18 distinct synoviocyte populations, including four monocyte/macrophage populations denoted as SC-M1 to SC-M4[ 26 ]. This analysis demonstrated that the activation of different cytokines promoted the expansion of diverse macrophage subpopulations in the RA synovium. Furthermore, as the primary orchestrators of the immune response, dendritic cells (DCs) have the capacity to secrete chemokines that facilitate the activation of inflammatory T cells, thereby attracting proinflammatory immune cells such as macrophages and neutrophils[ 27 – 29 ]. In vitro, RA synovial DCs have the potential to induce regulatory T cells (Treg) through prolonged engagement of the programmed cell death 1 (PD-1) receptor[ 30 , 31 ]. While Treg in the peripheral blood of RA patients retain inhibitory capacity, this function is compromised in local Treg, suggesting that the inflammatory cytokine environment may contribute to Treg dysfunction[ 32 ]. Ribonucleotide reductase M2 (RRM2) plays a critical role in controlling the production of deoxyribonucleotides, which is essential for DNA repair and synthesis[ 33 ]. Blocking RRM2 has a substantial impact on reducing cellular growth and triggering cell death[ 34 , 35 ]. Recently, other study had demonstrated RRM2 could increase the levels of apoptosis and inhibit the proliferation of RAFLS through regulating TGF-βand IL-6[ 36 ]. While several bioinformatics methodologies have been employed to investigate potential biomarkers for rheumatoid arthritis (RA), there is limited literature regarding the involvement of DLGAP5 in the pathophysiology of this condition[ 37 , 38 ]. Previous investigations have examined the structure and function of DLGAP5 across various species, considering both physiological and clinicopathological perspectives. These studies have revealed that DLGAP5 plays a crucial role in facilitating cell growth, proliferation, and migration[ 39 , 40 ].So, this presents an opportunity to further investigate the potential of DLGAP5 in diagnosing and differentially diagnosing rheumatoid arthritis, as well as its role in the pathophysiology of the disease. KIF11 encodes a motor protein belonging to the kinesin-like protein family, which is recognized for its involvement in diverse spindle dynamics. The gene product's role encompasses chromosome positioning, centrosome separation, and the establishment of a bipolar spindle during cell mitosis[ 41 ].However, literature on the role of KIF11 in the RA joint microenvironment is seldom.But in this study,KIF11 along with other 2 hub genes perform a perfect sentivity and specificity in diagnoising RA. This study has several limitations. Firstly, the dataset obtained from the GEO database lacks comprehensive patient information, including serological and imaging indicators. As a result, we were unable to evaluate the correlation of biomarkers or immune cells with clinical characteristics such as hematological indicators, degree of joint destruction, and treatment status in RA patients. More detailed data are necessary for further exploration of the clinical significance of biomarkers in the future. Secondly, the biomarker discovery was based on the GEO database. Despite satisfactory performance of our biomarkers in both test and validation datasets, additional in vitro and in vivo experiments are required to validate our findings and elucidate the mechanisms underlying significant immunological changes during RA. 5. Conclusion Using LASSO, SVM-RFE, and RF algorithms in conjunction with bioinformatic analyses, we identified a three-gene signature (RRM2,DLGAP5 and KIF11) implicated in the progression of RA. Furthermore, immune infiltration analyses revealed that the identified hub genes exhibited the strongest correlation with various T cells,B cells,natural killer cells and macrophage cells. To identify diagnostic markers with high sensitivity and specificity for RA, future studies should conduct prospective, large-sample investigations with experimental validation. Declarations Author Contributions Ying-Kai Wu,Cai-De Liu,Chao Liu designed the study, did data analysis, and drafted the manuscript. Jun Wu combined the data and performed the analysis. Zong-Gang Xie conceived the study and revised the manuscript. All authors read and approved the final manuscript. Funding This work was supported by the Shandong Province medicine and health development plan(202205010700);Linyi Natural Science Foundation(2022YX0053) Availability of data and materials The datasets utilized in this study are available in online repositories. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Data availability statement The datasets utilized in this study are available in online repositories. 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Bioscience reports 2020, 40 (9). Additional Declarations No competing interests reported. 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-3969525","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273869861,"identity":"0d6d285d-10aa-4fbb-965a-5d86bc2ec093","order_by":0,"name":"Ying Kai WU","email":"","orcid":"","institution":"The Second Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"Kai","lastName":"WU","suffix":""},{"id":273869862,"identity":"7fa9bb2f-a5cd-406f-bcde-f3276e2229ec","order_by":1,"name":"Cai-De Liu","email":"","orcid":"","institution":"Affiliated Hospital of Weifang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Cai-De","middleName":"","lastName":"Liu","suffix":""},{"id":273869863,"identity":"8e95ccfe-2110-4019-9108-0342f3cea5f0","order_by":2,"name":"Chao liu","email":"","orcid":"","institution":"Ningyang County maternal and child health hospital","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"liu","suffix":""},{"id":273869864,"identity":"b03b00a3-c095-4226-a61d-d27e436cc504","order_by":3,"name":"ying kai WU","email":"","orcid":"","institution":"LinYi People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"ying","middleName":"kai","lastName":"WU","suffix":""},{"id":273869865,"identity":"27cbf52a-bf91-491d-b108-7b7a97eb2619","order_by":4,"name":"Zong-Gang Xie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqElEQVRIiWNgGAWjYFACNmZmBgYbBjZStaSRruUwCRoMbqQlGxf8OW/PJ938gOFHxTaitBxOntl2O7FN5pgBY8+Z24S1mN1Ibz7M23A7gU0iwYCZsY1YLTx/ztmzSaR/IFYL0GE8bAcY2yRyiLTF/syzZOOZbcmJQC0FB4nyi2R7mrF0wR87e/kZ6Rsf/KggQguDQAKCfYAI9UDAT6S6UTAKRsEoGMEAAOLnOdtUB2fGAAAAAElFTkSuQmCC","orcid":"","institution":"The Second Affiliated Hospital of Soochow University","correspondingAuthor":true,"prefix":"","firstName":"Zong-Gang","middleName":"","lastName":"Xie","suffix":""}],"badges":[],"createdAt":"2024-02-19 09:00:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3969525/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3969525/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51467985,"identity":"ca2e94b1-6cf6-49f5-84a4-71f33696ec6b","added_by":"auto","created_at":"2024-02-22 06:55:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":208075,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3969525/v1/8597877e247eac8c11afeb6c.png"},{"id":51467988,"identity":"39e2e621-7bfe-405e-b535-cc383b72cc60","added_by":"auto","created_at":"2024-02-22 06:55:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":295354,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3969525/v1/f11b76e62c55f439288ad928.png"},{"id":51467993,"identity":"99004262-63e2-4bc2-b286-eadbf650bc44","added_by":"auto","created_at":"2024-02-22 06:55:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":553758,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3969525/v1/c9159876912a150aa5e8d4ac.png"},{"id":51467987,"identity":"15f37cf5-bfd3-4971-89dd-b12aaac23eab","added_by":"auto","created_at":"2024-02-22 06:55:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":137559,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3969525/v1/1efc356588ca1878eb67d5a8.png"},{"id":51467992,"identity":"5773593d-4ac3-4f65-8dbd-fa399d617ae7","added_by":"auto","created_at":"2024-02-22 06:55:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":327696,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3969525/v1/07bb45ad81518f2ee88d3252.png"},{"id":51467995,"identity":"d87856b2-e2ad-458d-a07e-c77e0ab6a01e","added_by":"auto","created_at":"2024-02-22 06:55:03","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":233131,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3969525/v1/ad37a32f444a7b5bf27b2e6d.png"},{"id":51468268,"identity":"a5bcf64f-ba20-4531-a84a-0843280cca44","added_by":"auto","created_at":"2024-02-22 07:03:03","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":364590,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-3969525/v1/ec66e01883a1b62ef6329ec8.png"},{"id":51468269,"identity":"6ff2f521-dfff-4126-a723-ad2137999eab","added_by":"auto","created_at":"2024-02-22 07:03:03","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":252111,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-3969525/v1/cea71d8c180636f981ae8aa7.png"},{"id":51467997,"identity":"b0e053e0-3d7c-4fe6-8d0a-c6416f165439","added_by":"auto","created_at":"2024-02-22 06:55:03","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":611703,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-3969525/v1/e887135b2cd8baaccc8dae02.png"},{"id":51467986,"identity":"d909a9d2-0b0b-4efc-8209-5d9eb70d2d54","added_by":"auto","created_at":"2024-02-22 06:55:03","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":309144,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-3969525/v1/68bef94d1aa58229524c4a76.png"},{"id":51467990,"identity":"95d6d2e0-20fe-48cd-b8fb-c5cfa1f6a62b","added_by":"auto","created_at":"2024-02-22 06:55:03","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":384430,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-3969525/v1/49b924745d462092bd3ef1aa.png"},{"id":51467998,"identity":"d854cceb-be32-4335-a166-4904b064bc8f","added_by":"auto","created_at":"2024-02-22 06:55:03","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":280205,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-3969525/v1/602c11b780155dac38db2b60.png"},{"id":51467991,"identity":"54fd7270-d58e-4736-8dfc-5c897802ae91","added_by":"auto","created_at":"2024-02-22 06:55:03","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":111786,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-3969525/v1/0a754b0bf0214d89120f1987.png"},{"id":68657379,"identity":"7f14ecca-47cb-40f1-9b0f-48f98db8cc45","added_by":"auto","created_at":"2024-11-10 14:47:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5777233,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3969525/v1/ff8cfe8f-f0ba-4b00-a1cf-5cfb79d050f2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of three-gene signature to diagnose rheumatoid arthritis through WGCNA and machine learning methods","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRA is a systemic autoimmune disease characterized by chronic inflammatory proliferation of synovial membranes and cartilage destruction,which can have a serious impact on the physical and mental health of patient[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].Although RA does not directly lead to patient mortality,its systemic inflammatory damage can affect the function of organs such as the heart,lungs and kidneys,reducing the quality of life for patient[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].The pathogenesis of RA is complex and involves multiple aspects such as genetics,environment and etabolism,what\u003csup\u003e,\u003c/sup\u003es more the mechanisms has not yet been stematically elucidated[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. According to recent research, different types of immune cells such as B cells, T cells, and macrophages are closely linked to the development of RA[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Other important immune cells, including natural killer cells (NK cells), mast cells, and dendritic cells (DCs), also play a role in the development or advancement of RA[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].Currently, research on the treatment and pathogenesis of RA is increasing, but there is still a lack of highly specific and sensitive biomarkers for early diagnosis of RA. Bioinformatics is an emerging discipline that combines biology, mathematics, and information technology, showing its outstanding performance in disease detection, biomarker screening, identification of high-risk patients, and prognosis assessment[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. WCGNA is currently a commonly used method for screening disease biomarkers and treatment targets. Machine learning algorithms, as a subset of artificial intelligence, can allow computers to learn from data to predict disease-related gene features, and are also widely used in research on biomarkers, disease mechanisms, and treatment targets[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The article comprehensively uses bioinformatics and various machine learning algorithms to integrate and analyze multiple gene datasets, in order to explore and discover more accurate diagnostic and therapeutic targets for RA, providing new directions for subsequent experimental research. A total of 4 synovial microarray datasets of RA were downloaded, using bioinformatics approaches to obtain intersection of DEGs and key genes of WGCNA, then using LASSO, SVM-REF, and Randomforest machine learning algorithms identify potential RA diagnostic markers and validate their diagnostic ability for RA. Additionally,we analyzed the infiltration levels of 28 immune cells in the expression profile and their relationship with hub genes using ssGSEA.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS ","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data Collection and Preprocessing\u003c/h2\u003e \u003cp\u003eThe process depicted in Fig.\u0026nbsp;1.Firstly,obtaining gene expression datasets for RA synovial samples (GSE77298, GSE55457, GSE55235, GSE12021) from the GEO database (\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)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These datasets totally included 87 synovial samples, comprising 36 healthy control samples and 51 RA samples (refer to Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). GSE55457 was utilized as a validation dataset, while the other datasets were consolidated for data analysis. Additionally, we retrieved platform annotation information and converted gene probes to gene symbols. We then employed the \"sva\" R package to merge GSE77298, GSE55457 and GSE55235 datasets using R software (version 4.3.1)[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The common genes across each dataset were identified for further analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInformation of datasets obtained from GEO\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDatasets\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlatform\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal Sample Number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNormal Sample Number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRA Sample Number\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE55235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGLP96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE77298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGLP96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE12021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGLP96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE55457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGLP570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Identification of DEGs and enrichment analyses\u003c/h2\u003e \u003cp\u003eThe DEGs were identified with the help of \u0026ldquo;limma\u0026rdquo; package.|log FC| \u0026ge; 1 and adjust.p.value\u0026thinsp;\u0026gt;\u0026thinsp;0.05 were used as the cut-off for screening DEGs[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Heatmaps and volcano maps were displayed by \"pheatmap\" and \"ggplot2\" packages. The Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was conducted with a cut-off of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The gene set variation analysis (GSVA) was performed to calculate the normalized Enrichment score (NES) under the background of the hallmark gene set (c2.cp.kegg.v7.2) by the \u0026lsquo;GSVA\u0026rsquo; R package, the threshold of the p-value and FDR were set as 0.05 and 0.25 respectivility[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].We also used Gene set enrichment analysis (GSEA) to identify the biological attribute and gene function of all genes in the train set by R package \u0026lsquo;clusterProfiler\u0026rsquo;, and the significant threshold were seted as p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.25[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. \u003cb\u003eConstruction of co-expression network\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eUsing the WGCNA package to construct a weighted gene co-expression network and identify hub genes[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. First, calculate the expression correlation coefficients of all genes and construct a similarity matrix. Convert the similarity matrix into a topological matrix and use the topological overlap measure (TOM) to describe the association between genes. Determine the soft threshold (power) based on the correlation between genes and construct a gene clustering tree. Identify expression modules based on gene expression correlation coefficients, with a minimum of 30 genes per module and a module correlation coefficient set to 0.9. Hub genes are determined by the connectivity of genes within the module. Calculate the Pearson correlation coefficient based on gene expression trends and select the genes with the module membership (MM)\u0026gt;0.8 within the module as candidate hub genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Screening of hub genes\u003c/h2\u003e \u003cp\u003eUsing the Venn package to obtain the intersection genes of DEGs and WGCNA candidate hub genes in characteristic module. The glmnet package of R software for LASSO algorithm, the e1071 package for SVM-RFE, and the random forest algorithm of the RandomForest package were conducted based on the intersection genes. Ultimately, hub genes were obtained by identifying the overlapping genes derived from the three machine learning methods using a Venn diagram.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Constructing Nomogram model and validation of hub genes\u003c/h2\u003e \u003cp\u003eA nomogram model for predicting RA was constructed using the rms package[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The predictive power of the nomogram model was assessed using the calibration curve. A decision curve was used to assess the clinical utility of the nomogram model. A receiver operating characteristic (ROC) curve was created using the R package pROC function to determine the diagnostic value of the hub genes and the nomogram model for RA in the training and validation sets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Correlation between immune cell infiltration and hub genes\u003c/h2\u003e \u003cp\u003eThe relative infiltration levels of 28 immune cells in the train set were quantified using the single-sample GSEA (ssGSEA) algorithm[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Barplot were used to show the differential expression levels of 28 immune-infiltrating cells. Spearman correlations of 28 immune-infiltrating cells with hub genes were calculated and then visualized using the \"ggplot2\" software package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Co-expression network of identified hub genes\u003c/h2\u003e \u003cp\u003eWe used the onlinetool GeneMANIA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://genemania.org\u003c/span\u003e\u003cspan address=\"https://genemania.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]to create hub genes\u003csup\u003e,\u003c/sup\u003e co-expression network.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Functional enrichment analysis of hub genes\u003c/h2\u003e \u003cp\u003eThe online tool Enrichr[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://maayanlab.cloud/Enrichr/\u003c/span\u003e\u003cspan address=\"https://maayanlab.cloud/Enrichr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was ued to elucidate the Biological process (BP), cellular component (CC) and molecular function (MF), KEGG ,WikiPathways, Reactome enrichment analysis of 3 hub genes [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The significant threshold of adj.P.value was \u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9. Transcription factor and miRNA of the 3 hub genes\u003c/h2\u003e \u003cp\u003eJASPAR database was used to find the transcription factors (TFs) that frequently bind\u003c/p\u003e \u003cp\u003eto 3 hub genes.MicroRNAs (miRNAs) that interacted with our hub genes were obtained from online platform MirTarbase.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10. Extraction potential small molecules of RA\u003c/h2\u003e \u003cp\u003eWe got access to DSigDB database through the Enrichr platform,and got the top 10 small molecules that could suppress the expression of hub genes.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1.Identifying the DEGs\u003c/h2\u003e \u003cp\u003eA total of 575 DEGs(including 383 up-regulate genes and 192 down-regulate genes) were identified between RA and normal individuals. The volcano plot of the DEGs were visualized with the top 10 upregulated and downregulated DEGs demonstrated(Fig.\u0026nbsp;2.A). In addition the expression level of the top 50 up-regulated and top 50 down-regulated genes were shown in the heatmap.(Fig.\u0026nbsp;2.B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Functional enrichment analysis\u003c/h2\u003e \u003cp\u003eGSEA was used to depict the signal pathways involved in RA. The top five enriched pathways were chemokine signaling pathway,cytokine-cytokine receptor interaction,intestinal immune network fo IgA production,rheumatoid arthritis and viral protein interaction with cytokine and cytokine(Fig.\u0026nbsp;3.A). The results of GSVA also indicated that immunity and inflammation pathways, such as chemokine singaling pathway,natural killer cell-mediated immunity, B-cell receptor signaling pathway,primary immunodeficiency and intestinal immune network for IgA production were enriched in RA group(Fig.\u0026nbsp;3.B). The BP of GO showed that DEGs were enriched in mononuclear cell differentiation, leukocyte cell\u0026ndash;cell and immune response-regulating cell surface receptor signaling pathway; in the MF term, DEGs were mostly related to antigen binding,immune receptor activity and chemokine activity;the CC terms emphasized external side of plasma membrane and clathrin-coated vesicle membrane(Fig.\u0026nbsp;4.A). KEGG pathway analysis showed that DEGs were enriched in cytokine-cytokine receptor interaction, chemokine signaling pathway and rheumatoid arthritis which were almost similar to the GSEA analysis(Fig.\u0026nbsp;4.B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3. WGCNA construction and hub module identification\u003c/h2\u003e \u003cp\u003eUsing the \"WGCNA\" package, samples in train set were clustered.Subsequently,\u003c/p\u003e \u003cp\u003eunscaled connectivity index and average connectivity analysis were performed.\u003c/p\u003e \u003cp\u003eWhen the soft threshold β\u0026thinsp;=\u0026thinsp;8, the network reached an unscaled topological threshold\u003c/p\u003e \u003cp\u003eof 0.9 (Figure.5.A). By dynamic tree cutting and calculation, 11gene modules were\u003c/p\u003e \u003cp\u003eobtained(Figure.5.B). Correlation analysis was performed between the 11 modules\u003c/p\u003e \u003cp\u003ewith the normal and RA group, resulting in a correlation heatmap (Fig.\u0026nbsp;5.C). The\u003c/p\u003e \u003cp\u003esalmon module had the strongest correlation with the RA (r\u0026thinsp;=\u0026thinsp;0.73, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), was\u003c/p\u003e \u003cp\u003eidentified as the key module for RA. Based on filtering criteria we identified 17\u003c/p\u003e \u003cp\u003ecandidate hub genes in the salmon module (Fig.\u0026nbsp;5.D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Screening of hub genes\u003c/h2\u003e \u003cp\u003eBy intersecting the DEGs and candidate hub genes,13 intersection genes were\u003c/p\u003e \u003cp\u003eobtained(Fig.\u0026nbsp;6.A). The 13 intersection genes were then submitted into three\u003c/p\u003e \u003cp\u003emachine-learning algorithms including LASSO,SVM-RFE and RandomForest.LASSO\u003c/p\u003e \u003cp\u003eresulting in four hub genes (DLGAP5,KIF11,MXRA5 and RRM2)(Fig.\u0026nbsp;6.B,6.C). SVM\u003c/p\u003e \u003cp\u003eidentified 5 hub genes(DLGAP5,KIF11,RRM2,TOP2A and PBK)(Fig.\u0026nbsp;6.D),RandomForest\u003c/p\u003e \u003cp\u003eidentified 7 hub genes(DLGAP5,KIF11,RRM2,MXRA5,COL5A1,PBK and\u003c/p\u003e \u003cp\u003eASPM)(Fig.\u0026nbsp;6.E,6.F)Finally, we got 3 hub genes\u0026mdash;DLGAP5,RRM2 and KIF11\u003c/p\u003e \u003cp\u003eby overlapping from these three machine learning methods(Fig.\u0026nbsp;6.G).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Constructing Nomogram model and validation\u003c/h2\u003e \u003cp\u003eA nomogram model was then constructed based on 3 hub genes in the train set to\u003c/p\u003e \u003cp\u003epredict the risk of RA (Fig.\u0026nbsp;7.A). The nomogram model was testified to have best\u003c/p\u003e \u003cp\u003epredictive and clinical efficiency for RA by calibration curves (Fig.\u0026nbsp;7.B) and decision\u003c/p\u003e \u003cp\u003ecurve analysis (DCA) (Fig.\u0026nbsp;7.C) respectively.The AUC of the nomogram model and 3\u003c/p\u003e \u003cp\u003ehub genes were also calculated and showed(Fig.\u0026nbsp;7.D,7.E). Next,we constructed all\u003c/p\u003e \u003cp\u003ethose procedure in the validation set,which showed a perfect match with the results\u003c/p\u003e \u003cp\u003ein train set(Fig.\u0026nbsp;8).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Correlation between immune cell infiltration and hub genes\u003c/h2\u003e \u003cp\u003eThe distribution of 28 immune cells in thetrain set was demonstrated in Fig.\u0026nbsp;9.A. The\u003c/p\u003e \u003cp\u003eresults demonstrated a significantly higher infiltration of activated CD4 T cells,\u003c/p\u003e \u003cp\u003eactivied B cells,and activated dendritic cells in RA,indicating the important role\u003c/p\u003e \u003cp\u003eplaying in RA. (Fig.\u0026nbsp;9.B). Correlation analysis of the 28 immune cells with hub genes\u003c/p\u003e \u003cp\u003edemonstrated various T cells,B cells,natural killer cells and macrophage cells were\u003c/p\u003e \u003cp\u003epositively correlated with those 3 hub genes(Fig.\u0026nbsp;9.C).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.8. Function analysis of hub genes\u003c/h2\u003e \u003cp\u003eIn order to elucidate the biological functions of the identified hub genes, we\u003c/p\u003e \u003cp\u003econstructed a comprehensive gene interaction network utilizing data from the\u003c/p\u003e \u003cp\u003egeneMANIA database (Fig.\u0026nbsp;10). This network comprised physical interactions, co\u003c/p\u003e \u003cp\u003e-expression relationships, predicted interactions, co-localization patterns, genetic\u003c/p\u003e \u003cp\u003einteractions, pathway interactions, and shared protein domains. Our findings\u003c/p\u003e \u003cp\u003eindicated that these hub genes are primarily associated with the mitotic nuclear\u003c/p\u003e \u003cp\u003edivision,spindle,microtuble cytoskeleton organization involved in mitosis and spindle\u003c/p\u003e \u003cp\u003eorganization. Furthermore, to discern specific biological roles of these 3 hub genes,\u003c/p\u003e \u003cp\u003ewe conducted an enrichment analysis. Figure\u0026nbsp;11.A, 11.B, and 11.C illustrate the most\u003c/p\u003e \u003cp\u003eenriched terms in the cellular component, biological process and molecular function\u003c/p\u003e \u003cp\u003eanalyses of Gene Ontology terms. Additionally, Fig.\u0026nbsp;12.A, 12.B, 12.C depict the most\u003c/p\u003e \u003cp\u003esignificant pathways based on data from Reactome ,WikiPathways, and KEGG\u003c/p\u003e \u003cp\u003ebatabases, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.9. Identification of Regulatory Signatures\u003c/h2\u003e \u003cp\u003eThe interplay between the 3 hub genes and transcription factor (TF) regulators is depicted in Fig.\u0026nbsp;13.A, while the relationships between the hub genes and microRNA (miRNA) regulators are illustrated in Fig.\u0026nbsp;13.B. In total, we identified 18 TFs and 17 miRNAs as regulatory signatures through the analysis of TF-gene and miRNA-gene interaction networks.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.10. Discovery of Potential Small Molecules\u003c/h2\u003e \u003cp\u003eWe generated potential small molecule findings based on odds ratios. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the top 10 potential small molecules targeting the hub genes sourced from the DSigDB database.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 10 small molecule drugs for RA\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverlap\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCombined Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLUCANTHONE CTD 00006227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3/213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.86E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e809723.5033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRRM2;KIF11;DLGAP5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0173570-0000 PC3 DOWN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.35E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.35E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e973.4634146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10913.3726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKIF11;DLGAP5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhytoestrogens CTD 00007437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.69E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.35E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e867.4347826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9531.856143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRRM2;DLGAP5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eetoposide MCF7 DOWN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.69E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.35E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e867.4347826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9531.856143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKIF11;DLGAP5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emethotrexate MCF7 DOWN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.99E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.35E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e797.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8638.620968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKIF11;DLGAP5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epiroxicam CTD 00006571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3/549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.06E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.35E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e629719.0634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRRM2;KIF11;DLGAP5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etroglitazone CTD 00002415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3/651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.43E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.65E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e596691.1364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRRM2;KIF11;DLGAP5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eapigenin MCF7 DOWN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.60E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0010851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e468.5176471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4587.228583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRRM2;KIF11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epyrvinium MCF7 DOWN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.26E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0010851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e442.3777778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4281.648144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRRM2;KIF11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eresveratrol MCF7 DOWN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.01E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001249052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e390.0980392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3679.655006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKIF11;DLGAP5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eRA is a chronic inflammatory disease that currently lacks early diagnostic indicators[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Recent studies have highlighted the close association of various immune cells, such as B cells, T cells, and macrophages, with the pathogenesis of RA[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Therefore, the exploration of new diagnostic biomarkers and their relationship with immune cell infiltration patterns holds significant implications for advancing our understanding of RA's pathophysiology.To address this, we gathered four RA synovial microarray datasets from the GEO database and identified 575 differentially expressed genes (DEGs) between RA and healthy controls (HC). Enrichment analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO), Gene Set Enrichment Analysis (GSEA) and GSVA revealed a robust correlation between RA and the immune response.\u003c/p\u003e \u003cp\u003eFurthermore, through the utilization of WGCNA and machine learning algorithms, we identified RRM2, DLAGP5 and KIF11 as potential diagnostic markers for RA.\u003c/p\u003e \u003cp\u003eOur findings unveiled that macrophages play an important role in immune cells infiltrating the synovium. In contrast to tissue-resident macrophages (TRM), infiltrating macrophages may originate from various monocyte subpopulations in the blood and possess a high level of adaptability. For instance, in mice, they can arise from classical Ly6C\u0026thinsp;+\u0026thinsp;or patrolling Ly6C- monocytes[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. A recent comprehensive analysis of immune cell status in RA patients, utilizing single-cell RNA-seq, Bulk RNA-seq, and mass spectrometry flow cytometry, identified 18 distinct synoviocyte populations, including four monocyte/macrophage populations denoted as SC-M1 to SC-M4[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This analysis demonstrated that the activation of different cytokines promoted the expansion of diverse macrophage subpopulations in the RA synovium.\u003c/p\u003e \u003cp\u003eFurthermore, as the primary orchestrators of the immune response, dendritic cells (DCs) have the capacity to secrete chemokines that facilitate the activation of inflammatory T cells, thereby attracting proinflammatory immune cells such as macrophages and neutrophils[\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In vitro, RA synovial DCs have the potential to induce regulatory T cells (Treg) through prolonged engagement of the programmed cell death 1 (PD-1) receptor[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. While Treg in the peripheral blood of RA patients retain inhibitory capacity, this function is compromised in local Treg, suggesting that the inflammatory cytokine environment may contribute to Treg dysfunction[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRibonucleotide reductase M2 (RRM2) plays a critical role in controlling the production of deoxyribonucleotides, which is essential for DNA repair and synthesis[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Blocking RRM2 has a substantial impact on reducing cellular growth and triggering cell death[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Recently, other study had demonstrated RRM2 could increase the levels of apoptosis and inhibit the proliferation of RAFLS through regulating TGF-βand IL-6[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile several bioinformatics methodologies have been employed to investigate potential biomarkers for rheumatoid arthritis (RA), there is limited literature regarding the involvement of DLGAP5 in the pathophysiology of this condition[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Previous investigations have examined the structure and function of DLGAP5 across various species, considering both physiological and clinicopathological perspectives. These studies have revealed that DLGAP5 plays a crucial role in facilitating cell growth, proliferation, and migration[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].So, this presents an opportunity to further investigate the potential of DLGAP5 in diagnosing and differentially diagnosing rheumatoid arthritis, as well as its role in the pathophysiology of the disease.\u003c/p\u003e \u003cp\u003eKIF11 encodes a motor protein belonging to the kinesin-like protein family, which is recognized for its involvement in diverse spindle dynamics. The gene product's role encompasses chromosome positioning, centrosome separation, and the establishment of a bipolar spindle during cell mitosis[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].However, literature on the role of KIF11 in the RA joint microenvironment is seldom.But in this study,KIF11 along with other 2 hub genes perform a perfect sentivity and specificity in diagnoising RA.\u003c/p\u003e \u003cp\u003eThis study has several limitations. Firstly, the dataset obtained from the GEO database lacks comprehensive patient information, including serological and imaging indicators. As a result, we were unable to evaluate the correlation of biomarkers or immune cells with clinical characteristics such as hematological indicators, degree of joint destruction, and treatment status in RA patients. More detailed data are necessary for further exploration of the clinical significance of biomarkers in the future. Secondly, the biomarker discovery was based on the GEO database. Despite satisfactory performance of our biomarkers in both test and validation datasets, additional in vitro and in vivo experiments are required to validate our findings and elucidate the mechanisms underlying significant immunological changes during RA.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eUsing LASSO, SVM-RFE, and RF algorithms in conjunction with bioinformatic\u003c/p\u003e \u003cp\u003eanalyses, we identified a three-gene signature (RRM2,DLGAP5 and KIF11) implicated\u003c/p\u003e \u003cp\u003ein the progression of RA. Furthermore, immune infiltration analyses revealed that\u003c/p\u003e \u003cp\u003ethe identified hub genes exhibited the strongest correlation with various T cells,B\u003c/p\u003e \u003cp\u003ecells,natural killer cells and macrophage cells. To identify diagnostic markers with\u003c/p\u003e \u003cp\u003ehigh sensitivity and specificity for RA, future studies should conduct prospective,\u003c/p\u003e \u003cp\u003elarge-sample investigations with experimental validation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eYing-Kai Wu,Cai-De Liu,Chao Liu designed the study, did data analysis, and drafted the manuscript. Jun Wu combined the data and performed the analysis. Zong-Gang Xie conceived the study and revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Shandong Province medicine and health\u003c/p\u003e\n\u003cp\u003edevelopment plan(202205010700);Linyi Natural Science Foundation(2022YX0053)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets utilized in this study are available in online repositories.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets utilized in this study are available in online repositories.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLi Z, Chen Y, Zulipikaer M, Xu C, Fu J, Deng T, Hao LB, Chen JY: \u003cstrong\u003eIdentification of PSMB9 and CXCL13 as Immune-related Diagnostic Markers for Rheumatoid Arthritis by Machine Learning\u003c/strong\u003e. \u003cem\u003eCurr Pharm Des \u003c/em\u003e2022, \u003cstrong\u003e28\u003c/strong\u003e(34):2842-2854.\u003c/li\u003e\n\u003cli\u003eJang S, Kwon EJ, Lee JJ: \u003cstrong\u003eRheumatoid Arthritis: Pathogenic Roles of Diverse Immune Cells\u003c/strong\u003e. \u003cem\u003eInternational journal of molecular sciences \u003c/em\u003e2022, 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\u003cstrong\u003e40\u003c/strong\u003e(9).\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":"Rheumatoid arthritis(RA), Hub genes, Machine learning, Immune cell infiltration","lastPublishedDoi":"10.21203/rs.3.rs-3969525/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3969525/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eRheumatoid arthritis(RA)is a systemic immune-related disease characterized by synovial inflammation and destruction of joint cartilage.The pathogenesis of RA remains unclear,and there is an urgent need to discover new diagnostic markers with high sensitivity and specificity.The aim of this study was to identify new potential biomarkers in the synovium for diagnosing rheumatoid arthritis and to investigate their association with immune infiltration.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eWe downloaded four datasets containing 51 RA and 36 healthy synovium samples from the Gene Expression Omnibus(GEO)database.Differentially expressed genes(DEGs)were identified with the help of R program.Then various enrichment analysis were conducted.Subsequently,WGCNA,random forest(RF),support vector machine-recursive feature elimination(SVM-RFE),least absolute shrinkage and selection operator(LASSO)were used to identify the hub genes for RA diagnosis.Receiver operating characteristic curves(ROC)and nomogram models were used to validate the specificity and sensitivity of hub genes.Additionally,we analyzed the infiltration levels of 28 immune cells in the expression profile and their relationship with hub genes using single-sample gene set enrichment analysis (ssGSEA).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThree hub genes(RRM2,DLGAP5 and KIF11)were identified through WGCNA,Lasso,SVM-RFE and RF algorithms.These hub genes showed the strong correlation with T cells,Natural killer cells and Macrophage cells indicated by the analysis of immune cell infiltration.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eA nomogram model for the diagnosis of RA based on RRM2,DLGAP5 and KIF11 has been established,providing diagnosis and treatment targets of RA.\u003c/p\u003e","manuscriptTitle":"Identification of three-gene signature to diagnose rheumatoid arthritis through WGCNA and machine learning methods","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-22 06:54:56","doi":"10.21203/rs.3.rs-3969525/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 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