Identification of Potential Biomarkers in Synovial Tissue of Rheumatoid Arthritis via Integrated Microarray Analysis. Identification of Biomarkers of Rheumatoid Arthritis

doi:10.21203/rs.3.rs-700226/v1

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Identification of Potential Biomarkers in Synovial Tissue of Rheumatoid Arthritis via Integrated Microarray Analysis. Identification of Biomarkers of Rheumatoid Arthritis

https://doi.org/10.21203/rs.3.rs-700226/v1

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Background: Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by synovitis and subsequent destruction of cartilage and bone. This study aimed to explore RA-related gene markers and the underlying molecular mechanism.

Material and Methods: The expression profiles of GSE77298, GSE55235 and GSE12021 were obtained from the Gene Expression Omnibus database. Then, the differential gene expression analysis was conducted between GSE77298 and GSE55235 datasets. Limma package and a Venn diagram were utilized to screen the overlapping differentially expressed genes (DEGs), and Functional enrichment and pathway analysis were performed by using DAVID database. Subsequently, a protein-protein interaction (PPI) network was established, and candidate hub genes were recognized by using STRING and Cytoscape software. Finally, another dataset (GSE12021) was used for the validation of diagnostic value of the candidate hub genes and to identify real hub genes by using receiver operating characteristic (ROC) curves.

Results: A total of 385 DEGs were detected, which include 19 downregulated genes and 366 upregulated genes. GO and KEGG pathway analysis showed that DEGs was mainly enriched in various immune and inflammatory response-related functions and pathways. The PPI network was composed of 374 nodes and 767 edges. A total of 8 real hub genes (HLA-DRA, HLA-DRB1, LCK, VAV1, HLA-DPA1, HLA-DPB1, C3AR1 and CD3D) which displayed an excellent diagnostic value for RA were identified.

Conclusion: these findings may provide novel and reliable biomarkers for RA, which have some interesting implications for early diagnosis, prognosis and targeted therapy.

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Rheumatoid arthritis (RA) is a chronic autoimmune disease with unspecified origin, which is characterized by synovitis, synovial hyperplasia and subsequent destruction of cartilage and bone. Environmental and genetic components have been recognized to play a vital role in the pathogenesis of RA[1, 2]. The clinical manifestations of RA are peripheral multi-joint inflammatory lesions. As the disease progresses, other organs such as lung and kidney are affected as well[3]. Based on the epidemiological survey, the estimated prevalence rate of RA is approximately 0.3–1.1% in North America and Europe[4]. Moreover, RA also brings heavy burden to personal health and socioeconomic costs. Despite considerable progress in early diagnosis and therapy, RA remains incurable[5]. So, it is very necessary to find out mechanisms underlying RA and developing treatment methods for RA. Accelerating studies have confirmed that synovitis is involved in the pathogenesis of RA[6]. Fibroblast-like synoviocytes (FLS) is one of the major components of synovium, which may contribute to synovitis directly via producing proinflammatory cytokines, or indirectly by recruiting and activating other immune cells[7]. The inhibition of FLS may decrease degradation of cartilage or inflammation in RA[8, 9]. A better understanding of the cellular and molecular mechanisms related to RA will lead us to discover more effective therapeutic strategies and potential biomarkers of the disease.

Microarray analyses is a powerful discovery tool to better identify biological pathways and candidate genes which are correlated with multiple diseases[10, 11]. Recently, several studies on data analysis of gene expression profiles in RA have been carried out, and many important differentially expressed genes (DEGs) relevant to RA have been identified[12, 13]. However, the exact origin and pathogenesis of RA has been still incompletely understood.

In this study, we downlinked and analyzed two original microarray datasets (GSE77298 and GSE55235) in Gene Expression Omnibus (GEO) database to gain overlapping DEGs between synovial tissues of RA and healthy controls. By doing so, data of the overlapping DEGs derived from distinct microarrays were more representative. Subsequently, functional enrichment analyses and the protein-protein interaction (PPI) network analyses for overlapping DEGs were conducted. Subsequently, ten candidate hub genes were recognized in the PPI network. The dataset of GSE12021 were used to evaluate diagnostic value of the candidate hub genes for RA patients. In this work, we aimed to explore disease-related gene biomarkers and the underlying molecular mechanism of RA.

Microarray data

Three Datasets of GSE55235, GSE77298 and GSE12021 were downlinked from the GEO database (http://www.ncbi.nlm.nih.gov/geo/). All datasets used were collected in synovial tissues from RA patients and healthy controls. The microarray data of GSE55235 and GSE12021 were based on GPL96 Platforms (Affymetrix Human Genome U133A Array). GSE77298 was based on GPL570 Platforms (Affymetrix Human Genome U133 Plus 2.0 Array). The dataset of GSE55235 comprised 10 normal synovial tisssues from healthy controls and 10 synovial tissues from RA patients. The dataset of GSE77298 included 7 normal synovial tisssues from healthy controls and 16 synovial tissues from RA patients. The dataset of GSE12021 were used to evaluate diagnostic value of the candidate hub genes which were screened out from the first two datasets for RA patients.

Identification of DEGs

All microarray datasets processing and analyses were carried out in R software (version 4.0.2; https://www.r-project.org/). A robust multi-array average (RMA) algorithm using the Affy packages were employed to normalize the data[14]. Synovial membrane differential gene expression analysis between RA patients and healthy controls was calculated through the R package LIMMA (http://www.bioconductor.org/packages/release/bioc/html/limma.html)[15]. The following is the screening criteria: |log2FC| ≥ 1, P-value < 0.05 and adjust P-value < 0.05 were statistically significant for the DEGs. To make the DEGs between the two groups be more intuitive, the volcano plots and heatmaps were generated from R software(version 4.0.2). And Venn diagrams (Venn 2.1; http://bioinfogp.cnb.csic.es/tools/venny/index.html) were applied to search for the overlapping DEGs between GSE55235 dataset and GSE77298 dataset to enhance the credibility of the subsequent results.

Functional enrichment analysis of DEGs

Gene Ontology (GO) enrichment analysis (http://www.geneontology.org/) is a powerful bioinformatics search tool for the biological function annotation and biological process analysis of the target genes. The Kyoto Encyclopedia of Genes and Genomes ( KEGG) database (KEGG; https://www .kegg.jp/) is the resource for understanding advanced functions and effects of biological systems from large-scale molecular data which were produced by high-throughput experimental techniques. The Database for Annotation, Visualization and Integrated Discovery (DAVID; david.abcc.ncifcrf.gov/) online tool was utilized for GO and KEGG pathway enrichment analysis, P values of less than 0.05 was regarded as statistically significant.

PPI network construction and real hub genes screening

STRING (https://string-db.org) was applied for predicting the protein-protein interaction network of overlapping DEGs. Confidence score ≥ 0.9 was fit as the threshold value. PPI network of overlapping DEGs was visualized and analyzed by Cytoscape 3.8.0. And Cytoscape plugin cytohubba was utilized to screen top 10 candidate hub genes in the network. ROC curves were used for evaluation of diagnostic accuracy of candidate hub genes in RA patients. Then we calculated the area under curve (AUC) and distinguished for real hub genes with an AUC > 0.8 and P < 0.05. Finally, GO and KEGG pathway enrichment analysis was used to explore the potential biological functions of real hub genes.

Identification of differentially expressed genes in RA

In search of the DEGs between RA and healthy controls, we download two transcription profile data from NCBI GEO datasets, including GSE77298 and GSE55235. A total of 26 RA and 17 healthy control synovial tissue samples of the two datasets were included in the study. After analyzing by R package Limma, we identified 2177 and 884 DEGs from GSE77298 and GSE55235 (Fig. 2C). Among them, 1965 and 655 genes were identified as the upregulated DEGs from GSE77298 and GSE55235 datasets, respectively (Fig. 1A-B). Furthermore, 212 and 229 genes were identified as the downregulated DEGs from GSE77298 and GSE55235 datasets, respectively. (Fig. 1A-B) Remarkably, 366 upregulated (Fig. 2A) and 19 downregulated DEGs (Fig. 2B) in RA patients were identified as co-expression genes in both datasets. So the 385 overlapping DEGs were put into follow-up study.

Functional enrichment analysis

To obtain further insight into the biological functions of the co-expression genes, they were submitted into DAVID database for GO and KEGG enrichment analyses. The analysis results of these differentially expressed overlapping genes are summarized in Fig. 3. The results of the biological process (BP) term showed that the DEGs mainly clustered in the regulation of both adaptive immune response and innate immune response, and the positive modulation of tumor necrosis factor production (Table 1). The results of the cellular component (CC) term demonstrated that the DEGs were mainly related to the extracellular exosome, external side of plasma membrane, extracellular space, integral component of plasma membrane and plasma membrane (Table 1). At the same time, the results of the molecular function (MF) term illustrated the DEGs were mainly involved in the activity of serine-type endopeptidase, transmembrane signaling receptor, MHC class II receptor and chemokine (Table 1). In addition, the KEGG enrichment analysis suggested that the DEGs were primarily enriched in leishmaniasis, staphylococcus aureus infection, tuberculosis, phagosome, rheumatoid arthritis, chemokine signaling pathway, cytokine-cytokine receptor interaction, osteoclast differentiation, inflammatory bowel disease, graft-versus-host disease (Table 2).

Table 1

GO analysis for overlapping DEGs between rheumatoid arthritis and healthy controls was performed using DAVID database. (The top 5 GO terms are shown based on the P value)
Category	ID	Description	Gene count	P value
BP	GO:0006955	immune response	62	1.04E-33
	GO:0006954	inflammatory response	47	3.36E-22
	GO:0002250	adaptive immune response	22	5.11E-12
	GO:0045087	innate immune response	36	8.02E-12
	GO:0032760	positive regulation of tumor necrosis factor production	13	1.72E-10
CC	GO:0070062	extracellular exosome	119	6.12E-16
	GO:0009897	external side of plasma membrane	27	2.02E-13
	GO:0005615	extracellular space	70	4.89E-13
	GO:0005887	integral component of plasma membrane	68	3.90E-11
	GO:0005886	plasma membrane	139	5.40E-11
MF	GO:0004252	serine-type endopeptidase activity	23	1.42E-08
	GO:0004888	transmembrane signaling receptor activity	19	4.36E-07
	GO:0008009	chemokine activity	9	6.09E-06
	GO:0032395	MHC class II receptor activity	6	8.79E-06
	GO:0004872	receptor activity	17	1.04E-05
GO, gene ontology; BP, biological process; CC, cellular component; MF, molecular function; DEG, differentially expressed gene.

Table 2

KEGG pathway enrichment analysis for overlapping DEGs between rheumatoid arthritis and healthy controls was performed using DAVID database (top 10 KEGG pathways are shown based on the P value)
ID	Description	Gene count	P value
hsa05140	Leishmaniasis	18	1.49E-11
hsa05150	Staphylococcus aureus infection	16	2.42E-11
hsa05152	Tuberculosis	25	3.32E-10
hsa04145	Phagosome	23	4.12E-10
hsa05323	Rheumatoid arthritis	18	5.65E-10
hsa04062	Chemokine signaling pathway	23	2.52E-08
hsa04060	Cytokine-cytokine receptor interaction	26	4.56E-08
hsa04380	Osteoclast differentiation	17	1.45E-06
hsa05321	Inflammatory bowel disease (IBD)	12	2.34E-06
hsa05332:	Graft-versus-host disease	9	3.97E-06
KEGG, Kyoto Encyclopedia of Genes and Genome; DEG, differentially expressed gene.

PPI network construction and analysis

To further explore the potential interactions among the DEGs,protein–protein interaction (PPI) network analysis was conducted using the STRING online database and Cytoscape software. Based on the overlapping genes, the PPI network was established which consisted of 374 nodes and 767 edges. The results were shown in Fig. 5A. Subsequently, the top 10 candidate hub genes were screened from the PPI network by utilizing the Degree method in plugin Cytohubba. These 10 genes are HLA-DRA, HLA-DRB1, LCK, GNAI2, CXCL1, VAV1, HLA-DPA1, HLA-DPB1, C3AR1 and CD3D (Fig. 5B). The top 10 candidate hub genes which were selected by cytoHubba plugin could be used to distinguish RA from healthy controls. To further verify whether our selected top 10 candidate hub genes have reliable diagnostic value for distinguishing RA and healthy controls, we applied ROC curve analysis based on the GSE12021 dataset to assess the diagnostic value of the 10 candidate hub genes. The results of the ROC curve analysis were shown in Fig. 6. The AUC of eight candidate hub genes (HLA-DRA, HLA-DRB1, LCK, VAV1, HLA-DPA1, HLA-DPB1, C3AR1 and CD3D) were shown with greater than 0.8 and P < 0.05. But, other two candidate hub genes including the GNAI2 and CXCL1 were showed as AUC less than 0.8 and P > 0.05, suggesting that there were no significant differences. Therefore, the eight genes would be considered to be real hub genes for discerning RA patients from healthy controls. Subsequently, GO and KEGG enrichment analyses of these hub genes were then performed (Fig. 4). GO enrichment analysis showed that the hub genes were mainly accumulated in T cell costimulation, T cell receptor signaling and MHC class II receptor activity (Table 3). Meanwhile, results of the KEGG pathway enrichment analysis presented that the hub genes mainly gathered in staphylococcus aureus infection, HTLV-I infection and Asthma (Table 4).

Table 3

GO enrichment analysis for hub genes (top 3 according to P value).
Category	ID	Description	Gene count	P value
BP	GO:0031295	T cell costimulation	7	5.75E-14
	GO:0050852	T cell receptor signaling pathway	6	1.03E-09
	GO:0002504	antigen processing and presentation of peptide or polysaccharide antigen via MHC class II	4	3.01E-08
CC	GO:0042613	MHC class II protein complex	4	5.33E-08
	GO:0071556	integral component of lumenal side of endoplasmic reticulum membrane	4	1.26E-07
	GO:0030658	transport vesicle membrane	4	2.91E-07
MF	GO:0032395	MHC class II receptor activity	4	1.98E-08
	GO:0042605	peptide antigen binding	4	1.42E-07
	GO:0023026	MHC class II protein complex binding	2	0.0067
GO, gene ontology; BP, biological process; CC, cellular component; MF, molecular function.

Table 4

Result of KEGG pathway analysis for hub genes (top 3 according to P value).
ID	Description	Gene count	P value
hsa05150	Staphylococcus aureus infection	5	1.17E-07
hsa05166	HTLV-I infection	6	1.30E-06
hsa05310	Asthma	4	2.59E-06
KEGG, Kyoto Encyclopedia of Genes and Genome.

RA) is a chronic inflammatory autoimmune disease, which can result in irreversible cartilage and bone damage and disability. The activation of synovial fibroblasts and the overproduction of several cytokines and chemokines are key players in the pathogenesis and progression of RA[16, 17]. However, the exact origin and pathogenesis of RA still incompletely understood. An improved understanding of the molecular mechanisms underlying RA will lead us to discover more effective therapeutic strategies for RA patients.

In this study, we sought to identify the target genes which were related to synovitis by comparing synovial tissue gene expression profiles between RA patients and healthy control groups. Through bioinformatics analysis, a total of 385 overlapping genes were identified, including 366 upregulated and 19 downregulated genes. Subsequently, GO and KEGG enrichment analyses of overlapping genes were carried out. With respect to GO analysis, the DEGs was mainly enriched in various immune and inflammatory response-related functions and pathways. These results were consistent with previous works demonstrated that immune and inflammation response played an important role in pathogenic process underlying RA[18, 19]. KEGG pathway analysis showed that the DEGs of our results were primarily enriched in cytokine-cytokine receptor interaction, tuberculosis and chemokine signaling pathway. The cytokine-cytokine receptor interaction and chemokine signaling pathway has been regarded as

to be relevant to multiple autoimmune diseases[20, 21]. Consistent with our results, these pathway was also found to be of great significance in other related RA studies[22]. Furthermore, it’s widely accepted that RA may be manifested with an increased risk of tuberculosis[23]. Previous studies by Loreto et al. also demonstrated that the risk of subsequent tuberculosis infections increased approximately four fold in RA patients[24]. Therefore, prevention of tuberculosis should be considered during the routine management and treatment of patients with RA.

Based on PPI network construction and hub gene screening, ten candidate hub genes were obtained. Additionally, the top 10 candidate hub genes have been validated through area of the ROC curve based on the GSE12021 dataset. Eight of these 10 genes had favourable diagnostic effect and were accredited as hub genes. They are HLA-DRA, HLA-DRB1, LCK, VAV1, HLA-DPA1, HLA-DPB1, C3AR1 and CD3D. Functional enrichment analysis revealed that all of these hub genes were important immune-related genes. The HLA-DRA, HLA-DRB1, HLA-DPA1 and HLA-DPB1 belong to MHC class II molecules. MHC class II molecules are heterodimeric glycoproteins on the cell surface which are mapped on human chromosome 6 and are encoded by MHC gene locus.

Moreover, the MHC class II molecules play an essential role in various immune response[25, 26]. Research has shown that HLA-DRA expression can accurately reflect immune status of patients and were associated with abnormal T cell differentiation[27]. Moreover, HLA-DRA was reported as a DEG and its level was upregulated in primary Sjögren's syndrome[28]. HLA-DRA was also shown to be one of the hub genes associated gene module in peripheral blood mononuclear cells of RA [29]. In the present study HLA-DRA was screened out as one of hub genes in RA synovial tissue. Accumulating studies have revealed that HLA-DRB1 is closely associated with RA. A previous study from Biljana et al. demonstrated the presence of HLA-DRB1 gene-specific variation possibly contribute to the increased risk of RA[30]. Jamil et al. had similar results in their study of RA patients[31]. In addition, one study also showed that HLA-DRB1 may be associated with drug resistance and drug efficacy in RA patients treated with adalimumab[32]. HLA-DP was also reported to be strongly associated with several autoimmune diseases, including ankylosing spondylitis and systemic lupus erythematosus[33, 34]. It was also demonstrated that HLA-DPB1 might be an related factor of susceptibility to RA and there were association between HLA-DPB1 and serum anti-CCP antibodies levels [35]. Moreover, there were also other studies suggesting that the polymorphisms of HLA-DPB1 may be as a protective factor for RA[36]. The present study identified HLA-DPA1 as a key gene correlated with RA, which are similar to the findings of Li et al[37]. However, another study showed that HLA-DPB1 or HLA-DPA1 had no correlation with the disease severity or susceptibility in RA patients[38]. Therefore, additional studies will be needed to clarify the role of HLA-DPB1 and HLA-DPA1 in RA and the underlying mechanisms. The lymphocyte-specific protein tyrosine kinase (LCK) is a member of the Src family, which was shown to participated in the pathogenesis of multiple autoimmune diseases through TCR signaling pathway[39]. It has been reported that LCK-deficient mice appeared severe deficiency of T cell development in the thymus[40]. Remarkably, LCK play a critical role in T lymphocytes hyporesponsiveness present in the synovial fluid of RA patients[41]. VAV1 is a GDP/GTP nucleotide exchange factor (GEF) specific to the hematopoietic system, the function of which was regulated by tyrosine phosphorylation after the end of T cell antigen receptor (TCR) stimulation[42]. And some scholars showed that VAV1 gene Polymorphisms were directly linked with RA, especially in anti-CCP negative rheumatoid arthritis[43]. As a member of G-protein coupled receptor family, the complement component 3a receptor 1 (C3aR1) was confirmed to be involved in the regulation of both innate and adaptive immune responses[44]. Previous study has shown that C3aR-knock out MRL/lpr lupus mice suffered faster renal injury, suggesting a protective role of C3aR in lupus nephritis[45]. In contrast, other studies showed that C3aR expression strongly associated with disease activity in patients with lupus nephritis[46]. CD3D mainly functions by forming T cell receptor (TCR) -CD3 complex, which could affect the outcome of T cell response[47]. It was reported that CD3D was mainly involved in immune activation, particularly being strongly linked with CD8⁺T cell activation in colon cancer [48]. However, there are few studies about the role of C3aR1 and CD3D in RA. Thus, these will be studied in our future works.

However, there are some limitations in our study. Firstly, our results are required to further validate by more studies with larger sample size. Additionally, to better comprehend the roles of DEGs reported by us in RA pathogenesis, molecular experiments are imperative. In all, integrated bioinformatics analysis of multiple gene expression datasets obtained from GEO database identified a total number of eight hub genes which might be associated with the pathogenesis of RA. Our results also revealed that the eight hub genes may be applied in the diagnosis of RA. Furthermore, our findings may contribute to increase the understanding of the underlying mechanisms of RA.

As a result, these findings may provide novel and reliable biomarkers for RA, which might have some interesting implications for early diagnosis, prognosis and targeted therapy.

RA: Rheumatoid arthritis; FLS: Fibroblast-like synoviocytes; DEGs: differentially expressed genes; GEO: Gene Expression Omnibus database; PPI: protein-protein interaction (PPI) network; RMA: A robust multi-array average algorithm; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; DAVID: Visualization and Integrated Discovery; AUC: the area under curve;

Acknowledgements

We thank all the study participants.

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All data are included in the article.

Funding

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Authors’ contributions

Li Tao and ChaoLiang Xiong contributed to the designation and writing of the manuscript. Xue Li revised the article. All authors reviewed and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

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Not applicable.

Competing interests

All authors declare no conflict of interests

Author details

¹ Department of Clinical Laboratory, The Second Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China.

Alamanos Y, Drosos AA. Epidemiology of adult rheumatoid arthritis. Autoimmun Rev. 2005;4:130–6.
Scherer HU, Häupl T, Burmester GR. The etiology of rheumatoid arthritis. J Autoimmun. 2020;110:102400.
Zhou S, Zou H, Chen G, Huang G. Synthesis and Biological Activities of Chemical Drugs for the Treatment of Rheumatoid Arthritis. Top Curr Chem (Cham). 2019;377:28.
Tobón GJ, Youinou P, Saraux A. The environment, geo-epidemiology, and autoimmune disease: Rheumatoid arthritis. Autoimmun Rev. 2010;9:A288-92.
Chaichian Y, Genovese MC. The road to rheumatoid arthritis prevention: challenges opportunities. 2020;39:1379–81.
Xu Q, Yin S, Yao Y, et al. MAST3 modulates the inflammatory response and proliferation of fibroblast-like synoviocytes in rheumatoid arthritis. Int Immunopharmacol. 2019;77:105900.
Chang SK, Gu Z, Brenner MB. Fibroblast-like synoviocytes in inflammatory arthritis pathology: the emerging role of cadherin-11. Immunol Rev. 2010;233:256–66.
Xue M, McKelvey K, Shen K, et al. Endogenous MMP-9 and not MMP-2 promotes rheumatoid synovial fibroblast survival, inflammation and cartilage degradation. Rheumatology. 2014;53:2270–9.
Liu YR, Yan X, Yu HX, et al. NLRC5 promotes cell proliferation via regulating the NF-κB signaling pathway in Rheumatoid arthritis. Mol Immunol. 2017;91:24–34.
Liao H, Liao M, Xu L, et al. Integrative analysis of h-prune as a potential therapeutic target for hepatocellular carcinoma. EBioMedicine. 2019;41:310–9.
Chen L, Lu D, Sun K, et al. Identification of biomarkers associated with diagnosis and prognosis of colorectal cancer patients based on integrated bioinformatics analysis. Gene. 2019;692:119–25.
Liu FQ. Analysis of differentially expressed genes in rheumatoid arthritis and osteoarthritis by integrated microarray analysis. J Cell Biochem. 2019;120:12653–64.
He P, Zhang Z, Liao W, Xu D, Fu M, Kang Y. Screening of gene signatures for rheumatoid arthritis and osteoarthritis based on bioinformatics analysis. Mol Med Rep. 2016;14:1587–93.
Irizarry RA, Hobbs B, Collin F, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 2003;4:249–64.
Ritchie ME, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47.
Smolen JS, Aletaha D, McInnes IB. Rheumatoid arthritis Lancet. 2016;388:2023–38.
Schönfeld C, Pap T, Neumann E. and U. Müller-Ladner, [Fibroblasts as pathogenic cells in rheumatic inflammation]. Z Rheumatol. 2015;74:33–8.
O'Neil LJ, Kaplan MJ. Neutrophils in Rheumatoid Arthritis: Breaking Immune Tolerance and Fueling Disease. Trends Mol Med. 2019;25:215–27.
Hodge JA, Kawabata TT, Krishnaswami S, et al. The mechanism of action of tofacitinib - an oral Janus kinase inhibitor for the treatment of rheumatoid arthritis. Clin Exp Rheumatol. 2016;34:318–28.
Zhu ZQ, Tang JS, Cao XJ. Transcriptome network analysis reveals potential candidate genes for ankylosing spondylitis. Eur Rev Med Pharmacol Sci. 2013;17:3178–85.
Yang H, Lian D, Zhang X, Li H, Xin G. Key Genes and Signaling Pathways Contribute to the Pathogensis of Diabetic Nephropathy. Iran J Kidney Dis. 2019;13:87–97.
Huang Y, Zheng S, Wang R, Tang C, Zhu J, Li J, CCL5 and related genes might be the potential diagnostic biomarkers for the therapeutic strategies of rheumatoid arthritis. 2019;38:2629–2635.
Gardam M, Iverson K. Rheumatoid arthritis and tuberculosis: time to take notice. J Rheumatol. 2003;30:1397–9.
Carmona L, Hernández-García C, Vadillo C, et al. Increased risk of tuberculosis in patients with rheumatoid arthritis. J Rheumatol. 2003;30:1436–9.
Turesson C. Endothelial expression of MHC class II molecules in autoimmune disease. Curr Pharm Des. 2004;10:129–43.
Unanue ER, Turk V, Neefjes J. Variations in MHC Class II Antigen Processing and Presentation in Health and Disease. Annu Rev Immunol. 2016;34:265–97.
Xu J, Li J, Xiao K, et al. Dynamic changes in human HLA-DRA gene expression and Th cell subsets in sepsis: Indications of immunosuppression and associated outcomes. Scand J Immunol. 2020;91:e12813.
Zhang L, Xu P. Identification of differentially expressed genes in primary Sjögren's syndrome. 2019;120:17368–77.
Xiao X, Hao J, Wen Y, Wang W, Guo X, Zhang F. Genome-wide association studies and gene expression profiles of rheumatoid arthritis: An analysis. Bone Joint Res. 2016;5:314–9.
Klimenta B, Nefic H. Association of biomarkers of inflammation and HLA-DRB1 gene locus with risk of developing rheumatoid arthritis in. females. 2019;39:2147–57.
Mourad J, Monem F. HLA-DRB1 allele association with rheumatoid arthritis susceptibility and severity in Syria. Rev Bras Reumatol. 2013;53:47–56.
Liu M, Degner J, Davis JW, et al. Identification of HLA-DRB1 association to adalimumab immunogenicity. PLoS One. 2018;13:e0195325.
Díaz-Peña R, Aransay AM, Bruges-Armas J, et al. Fine mapping of a major histocompatibility complex in ankylosing spondylitis: association of the HLA-DPA1 and HLA-DPB1 regions. Arthritis Rheum. 2011;63:3305–12.
Zhang J, Zhan W, Yang B, et al. Genetic Polymorphisms of rs3077 and rs9277535 in HLA-DP associated with Systemic lupus erythematosus in a Chinese population. Sci Rep. 2017;7:39757.
Huang Z, Niu Q, Yang B, et al. Genetic polymorphism of rs9277535 in HLA-DP associated with rheumatoid arthritis and anti-CCP production in. a Chinese population. 2018;37:1799–805.
Jiang L, Jiang D, Han Y, Shi X, Ren C. Association of HLA-DPB1 polymorphisms with rheumatoid arthritis: A systemic review and meta-analysis. Int J Surg. 2018;52:98–104.
Li X, Yang Y, Sun G, et al. Promising targets and drugs in rheumatoid arthritis: a module-based and cumulatively scoring approach. Bone Joint Res. 2020;9:501–14.
Hutchings CJ, Hillarby MC, McMahon MJ, Ollier WE, Grennan DM, HLA-DPA1 and HLA-DPB1 in rheumatoid arthritis and its subsets. Dis Markers, 1993;11:37–44.
Kumar Singh P, Kashyap A, Silakari O. Exploration of the therapeutic aspects of Lck: A kinase target in inflammatory mediated pathological conditions. Biomed Pharmacother. 2018;108:1565–71.
Molina TJ, Kishihara K, Siderovski DP, et al. Profound block in thymocyte development in mice lacking p56lck. Nature. 1992;357:161–4.
Romagnoli P, Strahan D, Pelosi M, Cantagrel A, van Meerwijk JP. A potential role for protein tyrosine kinase p56(lck) in rheumatoid arthritis synovial fluid T lymphocyte hyporesponsiveness. Int Immunol. 2001;13:305–12.
Tybulewicz VL, Ardouin L, Prisco A, Reynolds LF. Vav1: a key signal transducer downstream of the TCR. Immunol Rev. 2003;192:42–52.
Guerreiro-Cacais AO, Norin U, Gyllenberg A, et al. VAV1 regulates experimental autoimmune arthritis and is associated with anti-CCP negative rheumatoid arthritis. Genes Immun. 2017;18:48–56.
Peng Q, Li K, Sacks SH, Zhou W. The role of anaphylatoxins C3a and C5a in regulating innate and adaptive immune responses. Inflamm Allergy Drug Targets. 2009;8:236–46.
Wenderfer SE, Wang H, Ke B, Wetsel RA, Braun MC. C3a receptor deficiency accelerates the onset of renal injury in the MRL/lpr mouse. Mol Immunol. 2009;46:1397–404.
Mizuno M, Blanchin S, Gasque P, Nishikawa K, Matsuo S. High levels of complement C3a receptor in the glomeruli in lupus nephritis. Am J Kidney Dis. 2007;49:598–606.
Ngoenkam J, Schamel WW, Pongcharoen S. Selected signalling proteins recruited to the T-cell receptor-CD3 complex. Immunology. 2018;153:42–50.
Yang Y, Zang Y, Zheng C, et al. CD3D is associated with immune checkpoints and predicts favorable clinical outcome in colon cancer. Immunotherapy. 2020;12:25–35.

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Identification of Potential Biomarkers in Synovial Tissue of Rheumatoid Arthritis via Integrated Microarray Analysis. Identification of Biomarkers of Rheumatoid Arthritis

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Abstract

Figures

Background

Material And Methods

Microarray data

Identification of DEGs

Functional enrichment analysis of DEGs

PPI network construction and real hub genes screening

Results

Identification of differentially expressed genes in RA

Functional enrichment analysis

PPI network construction and analysis

Discussion

Conclusion

Abbreviations

Declarations

References

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