Dexamethasone-Induced MerTK+/high M2c Macrophages Exhibit a Preference for Downregulated Gene Expression Profiles

Multiple sclerosis (MS) and inflammatory bowel disease (IBD) are both autoimmune disorders caused by dysregulated immune responses. Still, there is a growing awareness of the comorbidity between MS and IBD. However, the shared pathophysiological mechanisms between these two diseases are still lacking. RNA sequencing datasets (GSE126124, GSE9686, GSE36807, GSE21942) were analyzed to identify the shared differential expressed genes (DEGs) for IBD and experimental allergic encephalomyelitis (EAE). Other datasets (GSE17048, GSE75214, and GSE16879) were downloaded for further verification and analysis. Shared pathways and regulatory networks were explored based on these DEGs. The single-cell transcriptome of central nervous system (CNS) immune cells sequenced from EAE brains and the public datasets of IBD (PRJCA003980) were analyzed for the immune characteristics of the shared DEGs. Mass cytometry by time-of-flight (CyTOF) of peripheral blood mononuclear cells (PBMCs) was performed for the systematic immune response in the EAE model. Machine learning algorithms were also used to identify the diagnostic biomarkers of MS. We identified 74 common DEGs from the selected RNA sequencing datasets, and single-cell RNA data of the intestinal tissues of IBD patients showed that 56 of 74 DEGs were highly enriched in IL1B+ macrophages. These 56 DEGs, defined as inflammation-related DEGs (IRGs), were also highly expressed in pro-inflammatory macrophages of EAE mice and MS patients. The abundance of systematic CD14+ monocytes was validated by CyTOF data. These IRGs were highly enriched in immune response, NOD-like receptor signaling pathway, IL-18 signaling pathway, and other related pathways. In addition, ‘AddModuleScore_UCell’ analysis further validated that these IRGs (such as IL1B, S100A8, and other inflammatory factors) are highly expressed mainly in pro-inflammatory macrophages, which play an essential role in pro-inflammatory activation in IBD and multiple sclerosis, such as IL-17 signaling pathway, NF-kappa B signaling pathway, and TNF signaling pathway. Finally, suppressors of cytokine signaling 3(SOCS3) and formyl peptide receptor 2(FPR2) were identified as potential biomarkers by machine learning. Two genes were highly expressed in pro-inflammatory macrophages of IBD and MS disease compared to control, and other datasets and experiments further revealed that SOCS3 and FPR2 were highly expressed in IBD and EAE samples. These shared IRGs, which encode inflammatory cytokines, exhibit high expression levels in inflammatory macrophages in IBD and may play a significant role in the inflammatory cytokine storm in MS patients. Two potential biomarkers, SOCS3 and FPR2, were screened out with great diagnostic value for MS and IBD.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10753-024-02195-z.

Inflammatory bowel diseases (IBDs), including ulcerative colitis (UC) and Crohn's disease (CD), are chronic inflammatory disorders caused by genetic influences, the immune system and environmental factors. However, the underlying pathogenesis of IBDs and the pivotal molecular interactions remain to be fully elucidated. The aim of the present study was to identify genetic signatures in patients with IBDs and elucidate the potential molecular mechanisms underlying IBD subtypes. The gene expression profiles of the GSE75214 datasets were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified in UC and CD patients compared with controls using the GEO2R tool. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of DEGs were performed using DAVID. Furthermore, protein-protein interaction (PPI) networks of the DEGs were constructed using Cytoscape software. Subsequently, significant modules were selected and the hub genes were identified. In the GO and KEGG pathway analysis, the top enriched pathways in UC and CD included Staphylococcus aureus infection, rheumatoid arthritis, complement and coagulation cascades, PI3K/Akt signaling pathway and osteoclast differentiation. In addition, the GO terms in the category biological process significantly enriched by these genes were inflammatory response, immune response, leukocyte migration, cell adhesion, response to molecules of bacterial origin and extracellular matrix (ECM) organization. However, several other biological processes (GO terms) and pathways (e.g., ‘chemotaxis’, ‘collagen catabolic process’ and ‘ECM-receptor interaction’) exhibited significant differences between the two subtypes of IBD. The top 10 hub genes were identified from the PPI network using respective DEGs. Of note, the hub genes G protein subunit gamma 11 (GNG11), G protein subunit beta 4 (GNB4), Angiotensinogen (AGT), Phosphoinositide-3-kinase regulatory subunit 3 (PIK3R3) and C-C motif chemokine receptor 7 (CCR7) are disease-specific and may be used as biomarkers for differentiating UC from CD. Furthermore, module analysis further confirmed that common significant pathways involved in the pathogenesis of IBD subtypes were associated with chemokine-induced inflammation, innate immunity, adapted immunity and infectious microbes. In conclusion, the present study identified DEGs, key target genes, functional pathways and enrichment analysis of IBDs, enhancing the understanding of the pathogenesis of IBDs and also advancing the clarification of the underlying molecular mechanisms of UC and CD. Furthermore, these results may provide potential molecular targets and diagnostic biomarkers for UC and CD.

Interleukin-17 Promotes Early Allograft Inflammation

IntroductionColonoscopy remains the gold standard for diagnosing inflammatory bowel disease (IBD) even though it is an invasive and costly procedure. To enable non-invasive diagnosis, we aimed to identify blood-based transcriptomic biomarkers that specifically distinguish IBD from healthy and inflammatory controls.MethodsPublic microarray and RNA-seq datasets from whole blood of IBD, rheumatoid arthritis (RA), and control subjects were analyzed. RA was included as a positive control for systemic inflammation to filter out non-IBD-specific gene signatures. Differentially expressed genes (DEGs) were identified, followed by immune cell deconvolution (CIBERSORTx), pathway and network analysis, and diagnostic model construction using LASSO and SVM. A real-life cohort (36 IBD patients, 30 controls) was recruited for qRT-PCR validation.ResultsIBD blood transcriptomes exhibited altered immune profiles, including increased M0 macrophages, Tregs, and CD4 naïve T cells, and decreased memory B and activated NK cells. After excluding RA-overlapping DEGs, 25 IBD-specific DEGs with |log2FC| > 0.5 were prioritized. LASSO and SVM identified a three-mRNA panel—IL4R, EIF5A, and SLC9A8—which achieved 84% diagnostic accuracy in the discovery cohort and 99% accuracy in the real-life cohort. Network analysis highlighted NDUFB2 as a key downregulated hub gene linked to mitochondrial complex I dysfunction and oxidative phosphorylation disruption. Elevated oxidative stress in IBD was confirmed by increased Total Oxidant Status (TOS) levels in patient plasma.DiscussionOur findings support the use of peripheral blood transcriptomics for IBD diagnosis and demonstrate that a focused three-gene panel can achieve high diagnostic accuracy. The inclusion of RA as an inflammatory control enabled the identification of IBD-specific markers, minimizing confounding from general immune activation. These results provide a practical foundation for developing non-invasive diagnostic tools for clinical use.

BackgroundOsteoarthritis (OA) and rheumatoid arthritis (RA) were two major joint diseases with similar clinical phenotypes. This study aimed to determine the mechanistic similarities and differences between OA and RA by integrated analysis of multiple gene expression data sets.MethodsMicroarray data sets of OA and RA were obtained from the Gene Expression Omnibus (GEO). By integrating multiple gene data sets, specific differentially expressed genes (DEGs) were identified. The Gene Ontology (GO) functional annotation, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and protein–protein interaction (PPI) network analysis of DEGs were conducted to determine hub genes and pathways. The “Cell Type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT)” algorithm was employed to evaluate the immune infiltration cells (IICs) profiles in OA and RA. Moreover, mouse models of RA and OA were established, and selected hub genes were verified in synovial tissues with quantitative polymerase chain reaction (qPCR).ResultsA total of 1116 DEGs were identified between OA and RA. GO functional enrichment analysis showed that DEGs were enriched in regulation of cell morphogenesis involved in differentiation, positive regulation of neuron differentiation, nuclear speck, RNA polymerase II transcription factor complex, protein serine/threonine kinase activity and proximal promoter sequence-specific DNA binding. KEGG pathway analysis showed that DEGs were enriched in EGFR tyrosine kinase inhibitor resistance, ubiquitin mediated proteolysis, FoxO signaling pathway and TGF-beta signaling pathway. Immune cell infiltration analysis identified 9 IICs with significantly different distributions between OA and RA samples. qPCR results showed that the expression levels of the hub genes (RPS6, RPS14, RPS25, RPL11, RPL27, SNRPE, EEF2 and RPL19) were significantly increased in OA samples compared to their counterparts in RA samples (P < 0.05).ConclusionThis large-scale gene analyses provided new insights for disease-associated genes, molecular mechanisms as well as IICs profiles in OA and RA, which may offer a new direction for distinguishing diagnosis and treatment between OA and RA.

BackgroundThe disability rate associated with rheumatoid arthritis (RA) ranks high among inflammatory joint diseases. However, the cause and potential molecular events are as yet not clear. Here, we aimed to identify differentially expressed genes (DEGs), pathways and immune infiltration involved in RA utilizing integrated bioinformatics analysis and investigating potential molecular mechanisms.Materials and methodsThe expression profiles of GSE55235, GSE55457, GSE55584 and GSE77298 were downloaded from the Gene Expression Omnibus database, which contained 76 synovial membrane samples, including 49 RA samples and 27 normal controls. The microarray datasets were consolidated and DEGs were acquired and further analyzed by bioinformatics techniques. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were performed using R (version 3.6.1) software, respectively. The protein-protein interaction (PPI) network of DEGs were developed utilizing the STRING database. Finally, the CIBERSORT was used to evaluate the infiltration of immune cells in RA.ResultsA total of 828 DEGs were recognized, with 758 up-regulated and 70 down-regulated. GO and KEGG pathway analyses demonstrated that these DEGs focused primarily on cytokine receptor activity and relevant signaling pathways. The 30 most firmly related genes among DEGs were identified from the PPI network. The principal component analysis showed that there was a significant difference between the two tissues in infiltration immune.ConclusionThis study shows that screening for DEGs, pathways and immune infiltration utilizing integrated bioinformatics analyses could aid in the comprehension of the molecular mechanisms involved in RA development. Besides, our study provides valuable data related to DEGs, pathways and immune infiltration of RA and may provide new insight into the understanding of molecular mechanisms.

Rheumatoid arthritis (RA) is an autoimmune condition that causes severe joint deformities and impaired functionality, affecting the well-being and daily life of individuals. Consequently, there is a pressing demand for identifying viable therapeutic targets for treating RA. This study aimed to explore the molecular mechanisms of osteoclast differentiation in PBMC from patients with RA through transcriptome sequencing and bioinformatics analysis. Blood samples were collected from 20 patients with RA, including 15 females and 5 males. Peripheral blood mononuclear cells (PBMCs) were isolated by density gradient centrifugation. Osteoclast differentiation was induced using a medium containing RANKL and M-CSF for 14 days, with medium changes every 2 days. After 14 days, osteoclasts were identified by TRAP staining, and multinucleated TRAP-positive cells were counted as osteoclasts. Subsequently, transcriptome sequencing was performed using the Illumina Novaseq 6000 platform, and differential expression analysis was conducted using the DESeq2 package in R. Differentially expressed genes were selected with a significance threshold of p < 0.05 and a fold change ≥ 2 (|Log2FC|≥ 1). Bioinformatics analysis was performed using R, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. TRAP staining showed successful induction of PBMCs into osteoclasts. Transcriptome sequencing revealed a significant number of differentially expressed genes (DEGs) in the induced groups compared with the control group. GO analysis showed that these DEGs were predominantly associated with biological processes related to the transmission of chemokine signals, reactions to living organisms, and bolstering neutrophil-driven defense mechanisms. KEGG analysis showed that these DEGs were enriched by primary signaling pathways, including interactions between cytokines and their receptors, chemokine signaling pathway, cell cycle regulation, neutrophil extracellular trap formation, and TNF signaling pathway. Osteoclast differentiation of PBMC from patients with RA involves various gene alterations, multiple biological processes, and signaling pathways, providing insight into the potential mechanism of PBMC osteoclast differentiation in RA. Key Points • A total of 1841 DEGs were obtained between the induced group and the normal group. • These DEGs were involved in multiple biological processes and signaling pathways.

Background: Rheumatoid arthritis (RA) and osteoarthritis (OA) are two major types of joint diseases. The present study aimed to identify hub genes involved in the pathogenesis and further explore the potential treatment targets of RA and OA.Methods: The gene expression profile of GSE12021 was downloaded from Gene Expression Omnibus (GEO). Total 31 samples (12 RA, 10 OA and 9 NC samples) were used. The differentially expressed genes (DEGs) in RA versus NC, OA versus NC and RA versus OA groups were screened using limma package. We also verified the DEGs in GSE55235 and GSE100786. Functional annotation and protein–protein interaction (PPI) network construction of OA‐ and RA‐specific DEGs were performed. Finally, the candidate small molecules as potential drugs to treat RA and OA were predicted in CMap database.Results: 165 up-regulated and 163 down-regulated DEGs between RA and NC samples, 73 up-regulated and 293 down-regulated DEGs between OA and NC samples, 92 up-regulated and 98 down-regulated DEGs between RA and OA samples were identified. Immune response and TNF signaling pathway were significantly enriched pathways for RA‐ and OA‐specific DEGs, respectively. The hub genes were mainly associated with ‘Primary immunodeficiency’ (RA vs. NC group), ‘Ribosome’ (OA vs. NC group), and ‘Chemokine signaling pathway’ (RA vs. OA group). Arecoline and Cefamandole were the most promising small molecule to reverse the RA and OA gene expression.Conclusion: Our findings suggest new insights into the underlying pathogenesis of RA and OA, which may improve the diagnosis and treatment of these intractable chronic diseases.

Rheumatoid arthritis (RA) and osteoarthritis (OA) were two major types of joint diseases. This study aimed to explore the mechanism underlying OA and RA and analyze their difference by integrated analysis of multiple gene expression data sets. Gene expression data sets of RA and OA were downloaded from The Gene Expression Omnibus. Shared and specific differentially expressed genes (DEGs) in RA and OA were identified by integrated analysis of multiple gene expression data sets. Functional annotation and protein-protein interaction (PPI) network construction of OA- and RA-specific DEGs were performed to further explore the molecular mechanisms underlying RA and OA and analyze the mechanism differences between them. Compared with normal controls, 3757 and 2598 DEGs were identified in RA and OA, respectively. Among them, 2176 DEGs were RA-specific DEGs and 1017 DEGs were OA-specific DEGs. Moreover, the expression of 17 DEGs played opposite pattern in RA and OA compared with normal controls. Chemokine signaling pathway and oxidative phosphorylation were significantly enriched pathways for RA- and OA-specific DEGs, respectively. BIRC2 and CSNK1E were respective hub genes of RA- and OA-specific PPI network. Our findings provided clues for the specific mechanism and developing specific biomarkers for RA and OA.

PurposeThis study aimed to provide a comprehensive understanding of the genome-wide expression patterns in the synovial tissue samples of patients with rheumatoid arthritis (RA) to investigate the potential mechanisms regulating RA occurrence and development.MethodsTranscription profiles of the synovial tissue samples from nine patients with RA and 15 patients with osteoarthritis (OA) (control) from the East Asian population were generated using RNA sequencing (RNA-seq). Gene set enrichment analysis (GSEA) was used to analyze all the detected genes and the differentially expressed genes (DEGs) were identified using DESeq. To further analyze the DEGs, the Gene Ontology (GO) functional enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed. The protein–protein interaction (PPI) network of the DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and the hub genes were identified by topology clustering with the Molecular Complex Detection (MCODE)-Cytoscape. The most important hub genes were validated using quantitative real-time PCR (qRT-PCR).ResultsOf the 17,736 genes detected, 851 genes were identified as the DEGs (474 upregulated and 377 downregulated genes) using the false discovery rate (FDR) approach. GSEA revealed that the significantly enriched gene sets that positively correlated with RA were CD40 signaling overactivation, Th1 cytotoxic module, overactivation of the immune response, adaptive immune response, effective vs. memory CD8+ T cells (upregulated), and naïve vs. effective CD8+ T cells (downregulated). Biological process enrichment analysis showed that the DEGs were significantly enriched for signal transduction (P = 3.01 × 10−6), immune response (P = 1.65 × 10−24), and inflammatory response (P = 5.76 × 10−10). Molecule function enrichment analysis revealed that the DEGs were enriched in calcium ion binding (P = 1.26 × 10−5), receptor binding (P = 1.26 × 10−5), and cytokine activity (P = 2.01 × 10−3). Cellular component enrichment analysis revealed that the DEGs were significantly enriched in the plasma membrane (P = 1.91 × 10−31), an integral component of the membrane (P = 7.39 × 10−13), and extracellular region (P = 7.63 × 10−11). The KEGG pathway analysis showed that the DEGs were enriched in the cytokine–cytokine receptor interaction (P = 3.05 × 10−17), chemokine signaling (P = 3.50 × 10−7), T-cell receptor signaling (P = 5.17 × 10−4), and RA (P = 5.17 × 10−4) pathways. We confirmed that RA was correlated with the upregulation of the PPI network hub genes, such as CXCL13, CXCL6, CCR5, CXCR5, CCR2, CXCL3, and CXCL10, and the downregulation of the PPI network hub gene such as SSTR1.ConclusionThis study identified and validated the DEGs in the synovial tissue samples of patients with RA, which highlighted the activity of a subset of chemokine genes, thereby providing novel insights into the molecular mechanisms of RA pathogenesis and identifying potential diagnostic and therapeutic targets for RA.

Identification of potential biomarkers of gout through competitive endogenous RNA network analysis

Rheumatoid arthritis (RA) and osteoarthritis (OA) are the common joints disorder in the world. Although they have showed the analogous clinical manifestation and overlapping cellular and molecular foundation, the pathogenesis of RA and OA were different. The pathophysiologic mechanisms of arthritis in RA and OA have not been investigated thoroughly. Thus, the aim of study is to identify the potential crucial genes and pathways associated with RA and OA and further analyze the molecular mechanisms implicated in genesis. First, we compared gene expression profiles in synovial tissue between RA and OA from the National Center of Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. Gene Expression Series (GSE) 1919, GSE55235, and GSE36700 were downloaded from the GEO database, including 20 patients of OA and 21 patients of RA. Differentially expressed genes (DEGs) including "CXCL13," "CD247," "CCL5," "GZMB," "IGKC," "IL7R," "UBD///GABBR1," "ADAMDEC1," "BTC," "AIM2," "SHANK2," "CCL18," "LAMP3," "CR1," and "IL32." Second, Gene Ontologyanalyses revealed that DEGs were significantly enriched in integral component of extracellular space, extracellular region, and plasma membrane in the molecular function group. Signaling pathway analyses indicated that DEGs had common pathways in chemokine signaling pathway, cytokine-cytokine receptor interaction, and cytosolic DNA-sensing pathway. Third, DEGs showed the complex DEGs protein-protein interactionnetwork with the Coexpression of 83.22%, Shared protein domains of 8.40%, Colocalization of 4.76%, Predicted of 2.87%, and Genetic interactions of 0.75%. In conclusion, the novel DEGs and pathways between RA and OA identified in this study may provide new insight into the underlying molecular mechanisms of RA.

In the present study, we investigated the relationship between rheumatoid arthritis (RA) and knee osteoarthritis (OA) using bioinformatics, aiming to identify the differentially expressed genes (DEGs) of RA and explore the possible mechanism of RA. The GSE55584 and GSE153015 microarray datasets for RA and OA gene expression profiles were acquired from the Gene Expression Omnibus (GEO) database. The DEGs of the two datasets were obtained by R language processing and analysis. The intersecting DEGs were obtained using the Venny 2.1 platform. Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genome (KEGG) enrichment analyses were performed using the DAVID platform, and the microbubble map was drawn online by importing the microbubble generation platform. All the obtained DEGs and the intersecting DEGs were imported into the STRING platform to obtain a protein–protein interaction network (PPI) and then into Cytoscape 3.9.1 software to screen core genes (hub genes). A total of 665 DEGs were obtained from the GSE55584 and GSE153015 datasets, including 324 upregulated and 341 downregulated DEGs. GO enrichment analysis showed that the biological processes in which DEGs were mainly enriched included signal transduction, immune response, inflammatory response, adaptive immune response, and G protein-coupled receptor signalling pathway. KEGG enrichment analysis of the DEGs identified the following enriched pathways: cytokine–cytokine receptor interaction; chemokine signalling pathway; viral protein interaction with cytokines and cytokine receptors; and apoptosis. Ten core genes (hub genes) were screened out, namely, CD3D, CD27, KLRB1, CCL5, GZMB, GZMA, GZMK, GNLY, CD2, and NKG7. Among them, CD3D, CD27, KLRB1, CCL5, and GZMB were most significantly correlated with the occurrence and development of RA. In the present study, bioinformatics analysis provided supporting evidence for the biological process and key genes of RA.

The aim of the present study was to identify potential key genes associated with the progression and prognosis of colorectal cancer (CRC). Differentially expressed genes (DEGs) between CRC and normal samples were screened by integrated analysis of gene expression profile datasets, including the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was conducted to identify the biological role of DEGs. In addition, a protein-protein interaction network and survival analysis were used to identify the key genes. The profiles of GSE9348, GSE22598 and GSE113513 were downloaded from the GEO database. A total of 405 common DEGs were identified, including 236 down- and 169 upregulated. GO analysis revealed that the downregulated DEGs were mainly enriched in ‘detoxification of copper ion’ [biological process, (BP)], ‘oxidoreductase activity, acting on CH-OH group of donors, NAD or NADP as acceptor’ [molecular function, (MF)] and ‘brush border’ [cellular component, (CC)]. Upregulated DEGs were mainly involved in ‘nuclear division’ (BP), ‘snoRNA binding’ (MF) and ‘nucleolar part’ (CC). KEGG pathway analysis revealed that DEGs were mainly involved in ‘mineral absorption’, ‘nitrogen metabolism’, ‘cell cycle’ and ‘caffeine metabolism’. A PPI network was constructed with 268 nodes and 1,027 edges. The top one module was selected, and it was revealed that module-related genes were mainly enriched in the GO terms ‘sister chromatid segregation’ (BP), ‘chemokine activity’ (MF), and ‘condensed chromosome (CC)’. The KEGG pathway was mainly enriched in ‘cell cycle’, ‘progesterone-mediated oocyte maturation’, ‘chemokine signaling pathway’, ‘IL-17 signaling pathway’, ‘legionellosis’, and ‘rheumatoid arthritis’. DNA topoisomerase II-α (TOP2A), mitotic arrest deficient 2 like 1 (MAD2L1), cyclin B1 (CCNB1), checkpoint kinase 1 (CHEK1), cell division cycle 6 (CDC6) and ubiquitin conjugating enzyme E2 C (UBE2C) were indicated as hub genes. Furthermore, survival analysis revealed that TOP2A, MAD2L1, CDC6 and CHEK1 may serve as prognostic biomarkers in CRC. The present study provided insights into the molecular mechanism of CRC that may be useful in further investigations.

Rheumatoid arthritis (RA) is a multi-systemic inflammatory autoimmune disease involving peripheral joints, and the pathogenesis is not clear. Studies showed that DNA methylation and expression might also be involved in the pathogenesis of RA. This study integrated three expression profile datasets (GSE55235, GSE12021, and GSE55457) and one methylation profile dataset GSE111942 to elucidate the potential essential candidate genes and pathways in RA. Differentially expressed genes (DEGs) and differentially methylation genes (DMGs) were identified by R programming software, using Limma package and ChAMP package, respectively. DAVID performed gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs. Functional annotation and construction of a protein–protein interaction (PPI) network and the Molecular Complex Detection Algorithm (MCODE) were analysed by STRING and Cystoscope, respectively. Then the connection analysis of DEGs and DMGs was carried out, and further to analyse the relationship between methylation and gene expression, aiming to screen out the potential genes. In this study, 288 DEGs and 228 DMGs were identified, and the majority of DEGs were up-regulated. Enrichment analysis represented that DEGs mainly involved immune response and participated in the Cytokine–cytokine receptor interaction signal pathway. 282 nodes were identified from DEGs PPI network and MCODE, filtering the most significant 2 modules, 23 core node genes were identified and most of them are involved in the T cell receptor signalling pathway and chemokine-mediated signalling pathway. Cross-analysis revealed 4 genes [KNTC1 (cg 01277763), LRRC8D (cg 07600884), DHRS9 (cg 05961700), and UCP2 (cg 05205664)] that exhibited differential expression and methylation in RA simultaneously. Therefore, the four genes could be used as the target for RA.