Research Article Marker Genes Change of Synovial Fibroblasts in Rheumatoid Arthritis Patients

Hindawi BioMed Research International Volume 2021, Article ID 5544264, 17 pages https://doi.org/10.1155/2021/5544264

Lifen Liao,1 Ke Liang,2 Lan Lan,1 Jinheng Wang,1 and Jun Guo 3

1Department of Laboratory, Aﬃliated Hospital of Guilin Medical University, Xiufeng District, Guilin, 541001 Guangxi, China 2Department of Laboratory, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Xiangshan District, Guilin, 541002 Guangxi, China 3Department of Hematology, Rizhao People’s Hospital, Donggang District, Rizhao, 276800 Shandong, China

Correspondence should be addressed to Jun Guo; [email protected]

Received 27 February 2021; Revised 14 May 2021; Accepted 24 May 2021; Published 7 June 2021

Academic Editor: Tao Huang

Copyright © 2021 Lifen Liao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background. Rheumatoid arthritis (RA) is a chronic condition that manifests as inflammation of synovial joints, leading to joint destruction and deformity. Methods. We identified single-cell RNA-seq data of synovial fibroblasts from RA and osteoarthritis (OA) patients in GSE109449 dataset. RA- and OA-specific cellular subpopulations were identified, and enrichment analysis was performed. Further, key genes for RA and OA were obtained by combined analysis with differentially expressed genes (DEGs) between RA and OA in GSE56409 dataset. The diagnostic role of key genes for RA was predicted using receiver operating characteristic (ROC) curve. Finally, we identified differences in immune cell infiltration between RA and OA patients, and utilized flow cytometry, qRT-PCR, and Western blot were used to examine the immune cell and key genes in RA patients. Results. The cluster 0 matched OA and cluster 3 matched RA and significantly enriched for neutrophil-mediated immunity and ECM receptor interaction, respectively. We identified 478 DEGs. In the top 20 degrees of connection in the PPI network, the key genes for RA were obtained by comparing with the gene markers of cluster 0 and cluster 3, respectively. ROC curve showed that CCL2 and MMP13 might be diagnostic markers for RA. We found aberrant levels of CD8+T, neutrophil, and B cells in RA fibroblasts, which were validated in clinical samples. Importantly, we also validated the differential expression of key genes between RA and OA. Conclusion. High expression of CCL2 and MMP13 in RA may be a diagnostic and therapeutic target.

1. Introduction The etiology of RA is complex and includes not only genetic and epigenetic factors but also smoking, infection, Rheumatoid arthritis (RA) is a chronic inflammatory disease microbiota, and others [8]. Although the etiology of RA is that affects joint synovial tissue, causing joint pain and disabil- not fully understood, its autoimmune properties have been ity [1]. RA is characterized by infiltration of synovium with widely recognized. Autoreactive CD4+T cells will stimulate inflammatory cells, hyperplasia of synovial fibroblasts, and pro- macrophages and synovial fibroblasts to secrete cytokines, gressive inflammation of the joint, leading to cartilage destruc- including TNF, IL-1, and IL-17, which contribute to invasive tion, bone erosion, and disability [2, 3]. A large population vasculitis through recruitment of immune cells and expan- studies found that in RA patients, the mortality was increased sion of synovial fibroblasts [9, 10]. In addition, RA has a withyearscomparedtothegeneralpopulation[4].Within10 specific tissue response characterized by an aggressive proin- years of RA onset, at least 50% of patients in developed coun- flammatory phenotype of local fibroblasts with stromal regu- tries are unable to take full-time jobs, probably due to the latory, osteoclastogenic, and invasive properties [11]. In RA, resulting disability [5]. Patients with RA represent approxi- stable reprogramming of synovial fibroblasts disrupts their mately 0.5%-1% of the world’s population and have regional protective regulatory processes, promotes their survival, variations [6]. The prevalence is higher in women aged between and increases their production of proinflammatory agents 35 and 50 years than in age-matched men [7]. and proteases [12]. Rheumatoid synovial fibroblasts are able 2 BioMed Research International to generate and support a sustained leukocyte infiltration Table 1: The primers of this study. [13]. Therefore, a deep and extensive understanding of synovial fibroblasts and their potential roles in the pathogenesis of Genes Primers RA is of great importance for the prevention and treatment F: 5′-TGACCGTCGGAGTCAGGGATTT-3′ GAPDH of RA. R: 5′-GCCAACGAATTTGCCATGGGTGG-3′ With the continuous development of medical standards, ′ ′ conventional treatments can alleviate the condition of RA F: 5 -TGCAAGAAGATAGCCAACCAAT-3 ICAM1 patients, but cannot completely cure them [14]. Notably, R: 5′-GTACACGGTGAGGAAGGTTTTA-3′ patient awareness of RA, the willingness to seek medical F: 5′-AAGAACATCCAAAGTGTGAACG-3′ treatment, the time to receive appropriate treatment, and CXCL1 the diagnostic capability of physicians all influence the treat- R: 5′-CACTGTTCAGCATCTTTTCGAT-3′ ment and prognosis of RA [15]. Early diagnosis and treat- F: 5′-AGATTCTACATGCGCACAAATC-3′ ment often delay and prevent joint deformities, improve the MMP1 ′ ′ quality and life span of patients, and are a prerequisite for R: 5 -CCTTTGAAAAACCGGACTTCAT-3 early detection of patients. F: 5′-GTGCTTGCTCTACCTGTCGG-3′ Therefore, it is very important to explore the pathogene- ITGA6 R: 5′-GCTCCCGGGGTCTCCATATT-3′ sis of RA to develop accurate treatments and new drug targets. This study broadens the candidate list of therapeutic F: 5′-GGGTCTCTTTCACTCAGCCAACAC-3′ MMP3 targets for RA, as well as the underlying mechanisms of R: 5′-ACAGGCGGAACCGAGTCAGG-3′ inflammatory infiltration, by exploring the differences in ff F: 5′-ACCAGCAGCAAGTGTCCCAAAG-3′ gene expression and the di erent biological functions CCL2 between RA and OA patients. It was further clarified that R: 5′-TTTGCTTGTCCAGGTGGTCCATG-3′ chemokines and matrix metalloproteinases (MMPs) signal- F: 5′-TTTGACATCTTTGAACTCACCG-3′ ing were involved in the pathological process of RA associ- THBS1 ′ ′ ated with immune cell infiltration. R: 5 -AGAAGGAGGAAACCCTTTTCTG-3 F: 5′-CACTTTATGCTTCCTGATGACG-3′ MMP13 2. Materials and Methods R: 5′-TCTGGCGTTTTTGGATGTTTAG-3′ 2.1. Rheumatoid Arthritis Data Collection. Data used in this study were obtained from the gene expression omnibus (GEO) public database (https://www.ncbi.nlm.nih.gov/geo). markers of clusters was performed using Enrichr online tool GSE109449 included gene expression profile of 192 single [28]. The P value < 0.05 was considered significantly synovial fibroblasts from 2 rheumatoid arthritis (RA) enriched. patients and 192 single synovial fibroblasts from 2 osteoar- ff ff thritis (OA) patients through single-cell RNA-seq (scRNA- 2.4. Di erence Analysis. Di erential expression analysis seq) based on GPL18573 [16]. between RA and OA in GSE56409 was performed using the GSE56409 included gene expression profile of fibroblasts limma R package [21, 29]. Genes with an |log2(FoldChange)| > P < fi ff which were isolated from synovium, bone marrow, or skin 1 and value 0.05 were identi ed as di erentially tissue samples of 12 rheumatoid arthritis patients and 6 oste- expressed genes (DEGs). oarthritis patients at the time of knee or hip replacement sur- 2.5. Generation of Protein-Protein Interaction (PPI) Network. gery based on GPL570 of array [17]. The raw data was The PPI network of common genes was identified through the processed and normalized using the Robust Multiarray Aver- Search Tool for the Retrieval of Interacting Genes (STRING) aging (RMA) methodology. (http://string-db.org/) database. The combined score >0.7 was considered significant. The PPI network was visualized 2.2. Processing of the scRNA-Seq Data. The quality control, by the Gephi software [30]. The PPI network genes were statistical analysis, and exploration of the scRNA-seq data ranked based on their degree of connectivity with other genes. for GSE109449 were performed using the Seurat R package [18, 19]. Principal component analysis (PCA) was used to 2.6. Identification of Immune Cell Infiltration. The marker fi P < identify signi cantly available dimensions with a value gene sets of different immune cell types were obtained from 0.05. The uniform manifold approximation and projection Bindea et al [31]. We used the single-sample gene set enrich- (UMAP) algorithm [20] was used for the visualization of ment analysis (ssGSEA) in R package GSVA [32] to derive ff unsupervised clustering. The di erential expression analysis the enrichment scores of each immune cell. The ssGSEA fi was used the limma R package [21, 22]. Set the ltering applies gene signatures expressed by immune cell popula- P < ff threshold value 0.05. Di erent cell clusters were anno- tions to individual samples. A threshold value of 0.05 was tated by the singleR package [23]. established for P values < 0.05.

2.3. Enrichment Analysis. Biological process (BP) in Gene 2.7. Sample Collection. Synovial tissue and peripheral blood Ontology (GO) and Kyoto Encyclopedia of Genes and samples from 5 patients of RA and 5 patients of OA were Genomes (KEGG) enrichment analysis [24–27] of gene collected from the Nanxishan hospital of Guangxi Zhuang BioMed Research International 3

Fibroblasts of RA and OA in scRNA-seq Quality control PCA GSE109449

Cell annotations UMAP

Biological Marker genes Diferential 4 clusters function of 4 clusters analysis

Diferentially GSE56409 expressed genes

Top 20 Quantifcation PPI network of immune cells degree

Correlation Key genes ROC curve analysis

qRT-PCR and Flow cytometry western blot

Figure 1: The ﬂowchart of this study. Using single-cell sequencing data and transcriptome data to identify gene signatures and potential diagnostic markers of RA patient. OA: osteoarthritis; PCA: principal component analysis; PPI: protein-protein interaction; RA: rheumatoid arthritis; ROC: receiver operating characteristic curve; UMAP: uniform manifold approximation and projection.

Autonomous Region. All subjects read and signed the 2.10. Flow Cytometry. Peripheral blood samples were surface- informed consent form. The study was in conformance labeled with anti-CD19 FITC (BD, California, USA), anti- with the guidelines of the 1975 Declaration of Helsinki CD3 PC5.5 (BD, California, USA), anti-CD8-PE antibody and was approved by the ethics committee of the Nan- (BD, California, USA), or anti-CD45 PC7 (BD, California, xishan hospital of Guangxi Zhuang Autonomous Region USA) for 10 min at room temperature. The red blood cells (2021NXSYYEC-001). in the blood were lysed with red blood cell lysate (BD, Cali- fornia, USA), then washed with PBS twice, and detected on fl 2.8. Quantitative Real-Time Polymerase Chain Reaction the Dx ex Flow cytometry (Beckman, California USA). The (qRT-PCR). The total RNA was isolated from synovial tissue results were analyzed using the Kaluza v2.1.1 software. samples by using Trizol (Thermo, California, USA). After 2.11. Statistical Analysis. Data analysis was used SPSS 20.0 uniform quality between groups, total RNA was reverse tran- software. Data were presented as mean ± standard scribed to complementary DNA (cDNA) using PrimeScript™ deviations (SD) [34, 35]. Student’s t-test was used to compare RT Master Mix (TaKaRa, Tokyo, Japan). The qRT-PCR was the differences between two groups [36]. The P value < 0.05 performed using the SYBR Green Master Mix (Thermo, Cal- was considered statistically significant. Test level α =0:05 ifornia, USA) using cDNA according to the manufacturer. (two-sided). The primer sequence of genes was shown in Table 1. Genes were normalized to GAPDH. Relative expression of mRNA –ΔΔ was calculated through the 2 CT method [33]. 3. Results 3.1. The mRNA Signatures in Fibroblast of Synovial Tissue. 2.9. Western Blot. The synovial tissue samples of RA and OA The article flow chart is shown in Figure 1. First, we analyzed were lysed on ice for 40 min in radio immunoprecipitation scRNA-seq data from fibroblasts of 2 RA and 2 OA patients assay (RIPA) buffer (Beyotime, Shanghai, China). Proteins (GSE109449). Based on quality control and normalization were loaded and separated by 10% SDS-polyacrylamide gel of the data, 31654 genes were found in the 384 cells electrophoresis (SDS-PAGE) and then transferred onto polyvi- (Figure 2(a)). The number of detected genes was significantly nylidene fluoride (PVDF) membranes. The membranes were correlated with sequencing depth (Figure 2(b)). Among incubated with the primary antibodies (all antibodies were pur- 31654 corresponding genes, the variant analysis revealed chased from Abcam) after blocking with skim milk. Protein 2000 highly variable genes (Figure 2(c)). In addition, princi- bands were then incubated with corresponding secondary anti- pal component analysis (PCA) results showed significant bodies and detected by enhanced chemiluminescence (ECL) separation between fibroblasts from RA and OA patients reagents. GAPDH protein was used as an internal reference (Figure 2(d)). To identify the available dimensions and protein. screen the related genes by PCA, we finally selected 13 4 BioMed Research International

nFeature_RNA nCount_RNA 0.68 1.2e+07

12000 9.0e+06 12000

6.0e+06 8000 8000 nFeature_RNA 3.0e+06 4000 4000

0.0e+00 OA RA OA RA Identity Identity 0.0e+00 3.0e+06 6.0e+06 9.0e+06 1.2e+07 nCount_RNA

Identity OA RA (a) (b)

8 UNC13D Disease

HMGCR CSN1S1 10 CHI3L1 6 C3 MFAP5 POSTN MMP1 MMP13 RPL32P3 0 4 PC_2

Standardized variance −10 2

0 −20

1e−08 1e−04 1e+00 1e+04 −10 0 10 20 Average expression PC_1

Non-variable count: 29654 OA RA Variable count: 2000 (c) (d)

Figure 2: Continued. BioMed Research International 5

0.3 8

0.2 7

0.1 5 Standard deviation Standard Teoretical [runif (1000)] Teoretical

4 0.0

0.000 0.025 0.050 0.075 0.100 5 10 15 20 Empirical PC

PC: p value PC 1: 3.25e-108 PC 9: 0.0132 PC 2: 3.73e-43 PC 10: 0.000293 PC 3: 9.83e-15 PC 11: 0.0005 PC 4: 1.07e-07 PC 12: 0.0232 PC 5: 3.83e-13 PC 13: 0.00759 PC 6: 1.07e-07 PC 14: 0.0735 PC 7: 0.000857 PC 15: 0.133 PC 8: 1.21e-05 (e)

Figure 2: RA-related principal component genes were screened based on single-cell sequencing. (a) Quality control of synovial fibroblasts in RA. There were 31654 genes in 384 cells. RA: rheumatoid arthritis; OA: osteoarthritis. (b) The depth of sequencing was significantly correlated with the number of genes detected. Pearson’s correlation coefficient was 0.68. (c) The variance diagram of gene expression for all fibroblast. The red dots represent 2000 highly variable genes, and the black dots represent nonvariable genes. (d) PCA demonstrates significant separations of cells between RA and OA. PC: principal components; RA: rheumatoid arthritis; OA: osteoarthritis. (e) PCA identified the 13 significant PCs. PC: principal components. principal components (PCs) to travel further analysis (P trophil degranulation (Figure 4(a)). Gene markers of cluster value < 0.05) (Figure 2(e)). 3 were significantly enriched in BP of extracellular matrix organization, collagen fibril organization, and skeletal system 3.2. Cell Subpopulations in Fibroblast of RA and OA. To development (Figure 4(b)). KEGG enrichment results determine whether fibroblast subpopulations differ between showed that gene markers of cluster 0 were significantly RA and OA, we performed clustering analysis for the cells. enriched in protein processing in protein processing in the Through the uniform manifold approximation and projec- endoplasmic reticulum, glycolysis/gluconeogenesis, and pro- tion (UMAP) algorithm, we clustered fibroblasts into 4 sepa- teoglycans in cancer (Figure 4(c)). While gene markers of rate clusters (Figure 3(a)). When these cell subpopulations cluster 3 were significantly enriched in focal adhesion, ECM were compared with the clinical phenotypes, we found that receptor interaction, and phagosome (Figure 4(d)). cluster 0 matched the OA group, and cluster 3 matched the RA group (Figure 3(b)). Next, we performed differential 3.4. Gene Expression in Fibroblasts of RA. Afterwards, we expression analysis, identifying 1561 marker genes in the obtained 478 differentially expressed genes (DEGs) using four clusters (Figure 3(c)). Gene markers of cluster 0 were gene expression data in fibroblasts from RA patients and more highly expressed in OA than in RA, whereas gene OA patients (Figure 5(a), Table S1). The 294 upregulated markers of cluster 3 were more highly expressed in RA than DEGs and 184 downregulated DEGs were included in OA (Figure 3(d)). These clusters were annotated as cell (Figure 5(b)). The PPI network of DEGs with interactions types based on the score by singleR. However, different clus- was acquired through a string database (Figure 5(c)). We ters were not annotated as different cell fibroblast subpopula- screened the top 20 greatest degree genes of connection in tions for RA and OA (Figure 3(e)). the PPI network as candidates (Table 2). Comparing with the gene markers of clusters, we found that MMP3, ITGA6, 3.3. Different Biological Function of Cell Subpopulations. To MMP1, and CXCL1were the intersection genes for cluster identify distinct biological roles for subpopulations of RA 0, and THBS1, CCL2, MMP13, and ICAM1 were the and OA patient fibroblasts, we performed enrichment analy- intersection genes for cluster 3 (Figure 5(d)). Therefore, we sis of gene markers of cluster 0 and cluster 3. It was found considered that these 8 genes might be associated with that gene markers of cluster 0 were significantly enriched in arthritis and were defined as key genes. THBS1, CCL2, biological process (BP) of neutrophil activation involved in MMP13, and ICAM1 may be potential markers for RA. immune response, neutrophil-mediated immunity, and neu- Among the differential results, THBS1, CCL2, MMP13, 6 BioMed Research International

5.0 5.0 Disease

0 2.5 2.5

0.0 0.0

UMAP_2 3 UMAP_2

2 −2.5 1 −2.5

−5.0 −2.5 0.0 2.5 −5.0 −2.5 0.0 2.5 UMAP_1 UMAP_1 0 2 OA 1 3 RA (a) (b) 0123 Expression HTRA1 2 TWISTNB MMP3 GPR1 CSN1S1 MMP1 DCN VCAN IGFBP4 1 CXCL12 SFRP1 IGF1 C3 ASPN MFAP5 CHI3L1 0 CTHRC1 POSTN TGFBI COL1A1 OGN SLC40A1 COMP −1 MMP13

Identity

0 2 1 3 (c)

Figure 3: Continued. BioMed Research International 7

OA RA Expression HTRA1 2 TWISTNB MMP3 GPR1 CSN1S1 MMP1 DCN VCAN IGFBP4 1 CXCL12 SFRP1 IGF1 C3 ASPN MFAP5 CHI3L1 0 CTHRC1 POSTN TGFBI COL1A1 OGN SLC40A1 COMP −1 MMP13 Identity OA RA (d)

Scores

Clusters Labels Chondrocytes Higher Fibroblasts Smooth_muscle_cells MSC Osteoblasts Tissue_stem_cells iPS_cells Neurons Endothelial_cells Astrocyte Neuroepithelial_cell Embryonic_stem_cells Epithelial_cells Hepatocytes Macrophage DC HSC_−G−CSF Neutrophils BM Myelocyte Gametocytes Pre−B_cell_CD34− Pro−Myelocyte HSC_CD34+ NK_cell Monocyte Platelets B_cell MEP CMP T_cells BM & Prog. Erythroblast Pro−B_cell_CD34+ Keratinocytes GMP Lower 0 1 2 3

Clusters Labels

0 Chondrocytes 1 2 3 (e)

Figure 3: Identification of four cell subpopulations for fibroblast. (a) The UMAP algorithm reduced the dimensionality of 13 PCs and clustered into four cell clusters. UMAP: uniform manifold approximation and projection. (b) The cell clusters were matched to the sample types for RA or OA. RA: rheumatoid arthritis; OA: osteoarthritis; UMAP: uniform manifold approximation and projection. (c) Differential analysis identified 1561 gene markers. The top six gene markers for each cell cluster were listed on the left of heatmap. The colors from purple to yellow indicate the gene expression levels in each cell sample from low to high. (d) The expression of genes was matched to the sample types for RA or OA. RA: rheumatoid arthritis; OA: osteoarthritis. The colors from purple to yellow indicate the gene expression levels in each cell sample from low to high. (e) All clusters of cells were annotated according to the scores of singleR. The colors from blue to yellow indicate the gene expression levels from low to high. 8 BioMed Research International

neutrophil activation involved in immune response (GO:0002283)

neutrophil mediated immunity (GO:0002446)

neutrophil degranulation (GO:0043312)

Golgi vesicle transport (GO:0048193)

ER to Golgi vesicle-mediated transport (GO:0006888)

regulated exocytosis (GO:0045055)

platelet degranulation (GO:0002576)

cellular protein metabolic process (GO:0044267)

glycolytic process through glucose-6-phosphate (GO:0061620)

canonical glycolysis (GO:0061621) (a)

extracellular matrix organization (GO:0030198)

collagen fbril organization (GO:0030199)

skeletal system development (GO:0001501)

protein complex subunit organization (GO:0071822)

neutrophil degranulation (GO:0043312)

neutrophil activation involved in immune response (GO:0002283)

neutrophil mediated immunity (GO:0002446)

regulation of cell migration (GO:0030334)

negative regulation of apoptotic process (GO:0043066)

regulation of apoptotic process (GO:0042981) (b)

Protein processing in endoplasmic reticulum

Glycolysis / Gluconeogenesis

Proteoglycans in cancer

Bacterial invasion of epithelial cells

Regulation of actin cytoskeleton

Legionellosis

Pathogenic Escherichia coli infection

Focal adhesion

Viral myocarditis

Phagosome (c)

Focal adhesion

ECM-receptor interaction

Phagosome

Proteoglycans in cancer

Protein digestion and absorption

Human papillomavirus infection

AGE-RAGE signaling pathway in diabetic complications

PI3K-Akt signaling pathway

Kaposi sarcoma-associated herpesvirus infection

Apoptosis (d)

Figure 4: Enrichment analysis of gene markers of cell subpopulations matched to RA or OA. (a) Significantly enriched biological processes of cell subpopulations for cluster 0 who matched the OA patients. (b) Significantly enriched KEGG pathway of cell subpopulations for cluster 0. (c) Significantly enriched biological processes of cell subpopulations for cluster 3 who matched the RA patients. (d) Significantly enriched KEGG pathway of cell subpopulations for cluster 3. BioMed Research International 9

Group 4 6

2 4 ( P value) 10 –log 2 0

0 −2 0 2 −2 log2 (Fold Change) Group Down-regulated Not-signifcant −4 Up-regulated Group OA RA (a) (b)

SPECC1 AKAP10 MASP1 FRMPD4 FAT3 FAM167A FAM19A5 LOXL4 SULT4A1LRRN3 REEP1 MAP7 SPOCD1 CBFA2T2 TMEM35PI16 NAA25 OCIAD2 NEFH KRTAP1-5 NRN1 DPYSL4 SYDE1 CFAP69 GSTT2B XPNPEP3 PRKAR2BVASH2 KCNE4 PCSK1 CPM CRABP2PTPN22 GPM6B KCTD4 APELA ANK3 PMEPA1 CHDH MCTP2 ARID1B XPNPEP2 HLA-DRA TENM2 GREM2 HOXC11LHX9 C1QTNF3 RTN1 NTNG1 CDK15 NRCAM HLA-DRB1 RAB3IP CLEC3B PODXL BAALC TOR4A NRK NXN PAX1 SLIT3 LYPD6B WISP3 MAK PPP1R14C PDPN HLA-DPA1GAP43 CORIN IGFBP7 CDH10 FBN2 NOX4 BHLHE41 FAM132B NRP2 STAT4 KCNS3 NKX3-1 PIEZO2 MSX2 TGM2 STC2 NHSL2 LRRTM2 SEMA5A MME NXPH2 SCG2SERPINA1 PPP2R2CMCOLN2 SEMA3A IRX1 INHBA ACE PLP1 CDO1 LMCD1 SCN1A NCAM1 NR2F1 TMEM255A CP STEAP1 SH3RF2 EFNB2 ICAM1 ELFN1 CEPT1 TEK LIF RCAN1 FGF13 ADCY2 AR TNC ALDH3A1 STEAP2 MYRIP CD34 PAX9 TRPC6 PTGER4 DUOX1 HOXA13 NHSL1 TMEM56 KIT CCL7 CXCL9 CACNA2D3 KAL1 FOXC1 IGFBP5 SOX6 ASPA IL15 CXCL8 PITPNB PPP1R13LBCL11B NPR1 MST4 LRRC17 PTGS2 IL6 ITGA6 ADAM23 LGI1 GDF5 FAM131B GNRH1MMP1 ESCO2 THBS1 PGR PTGER3 RASGRP3 AQP3 IL20RB TNIK LIPH CCL2 ADRA2A ADAMDEC1 STXBP6 ZNF385B ITGB3 CXCL3 MX1 GRIA1 CXCL6 NCALD KANSL1 CILP EDNRB CXCL1 CCL8 HES1 ADAM33 PHKA1 VTI1A PPARGC1A CDCP1 TRH TNFRSF11BMMP3 MMP13 SLC1A2 HPSE LGR5 SLC2A1 MRPL43 DOCK7 RSPO3 NEDD9 PDGFRA MDM2 KIAA1244 CDS1 TNFAIP6 RPS28 PMAIP1 TSLP ITGB8 SLC38A9 NOTCH3 POSTN LRRFIP2 RERG FOLR3GPR4 NAMPT SAMD5 VAMP8 F2RL1 COL11A1 COL4A4 MAOBWNT9A FMO2 GPR116 IFITM1 COL4A5 ENPP1 FRZB KIAA1549L HOPXBAIAP2L1 OLFM4 HTR2A GSDMB BRINP1 CCDC85C BST2 WNT16 HAPLN1 FBLN2 STC1 DEPTOR DCLK1 SHB PTPRE ABCC3 WWC1 WWOX ARAP3 PCBP2 FGL2 NPAS3 VSTM2L RGS4 FABP3 HERC6NEDD4L COL15A1 PARP8 UPP1 TNS3 SAMD4B SLC2A5 PAQR5 SLC7A2 COL8A1 SLC16A6 ST3GAL6 TMEM196 HTRA3 TRIM9 COL13A1LEPREL1 SMAGP GALNT3 SLC22A3 HS3ST3B1 AJAP1 SLC29A4 RNF144B NUAK2CELSR1 CCDC3 (c)

Figure 5: Continued. 10 BioMed Research International

Cluster3 Cluster0 15

360 16 550 10 ⁎⁎ ⁎ ⁎⁎⁎ ⁎ ⁎⁎ ⁎ ⁎⁎ 0

44 5 ⁎⁎

12 0 Top 20 degreee of PPI CCL2 MMP1 MMP3 ITGA6 THBS1 CXCL1 ICAM1 MMP13 RA OA (d) (e)

1.0

0.8

0.6

Sensitivity 0.4

0.2

0.0

0.0 0.2 0.4 0.6 0.8 1.0 1 – specifcity THBS1 AUC 0.74 MMP1 AUC 0.775 ITGA6 AUC 0.798 MMP3 AUC 0.8 ICAM1 AUC 0.725 MMP13 AUC 0.812 CXCL1 AUC 0.754 CCL2 AUC 0.907 (f)

Figure 5: Identification of differentially expressed genes between RA and OA in GSE56409. (a) Heatmap of differential gene expression in RA and OA. RA: rheumatoid arthritis; OA: osteoarthritis. Red are upregulation and blue are downregulation. (b) Volcano plot of differentially expressed genes between RA and OA. Red are upregulation and blue are downregulation. (c) PPI network of differentially expressed genes. The colors from blue to red, representing the greater degree to which genes are connected in the network. (d) Venn diagram of the intersection among cluster 0, cluster 3, and the top 20 degrees in the PPI network. Then obtained eight key genes. (e) Differential expression of key genes between RA and OA in GSE56409. RA: rheumatoid arthritis; OA: osteoarthritis. (f) ROC curve of key genes for predicting diagnosis of RA. AUC: area under ROC curve.

ICAM1, MMP3, MMP1, and CXCL1 showed higher MMP13 had a good predictive diagnostic role for RA expression in RA compared with OA, while ITGA6 showed (Figure 5(f)). CCL2 and MMP13 with the highest area lower expression in RA (Figure 5(e)). Receiver operating under the receiver operating characteristic curve (AUC) characteristic (ROC) curve results showed that CCL2 and values (AUC > 0:8). BioMed Research International 11

Table 2: Top 20 genes with the largest degree in the PPI network. the molecular mechanisms of RA. Transcriptome data were further combined to screen for potential fibroblast-specific Genes Degree markers. Importantly, we utilized molecular experiments to IL6 60 validate key results. These are particularly important, as such CXCL8 40 biomarkers may contribute to the early diagnosis and early CCL2 30 treatment of the disease. ff PTGS2 28 Unfortunately, we did not get di erent annotations for the different cell subpopulations. Of the four subpop- ICAM1 28 ulations identified, cluster 0 may be more representative THBS1 27 for OA, whereas cluster 3 may be representative for RA. CXCL1 25 Gene markers for cluster 3 (MMP13, COMP, SLC40A1, CD34 25 OGN, COL1A1, and TGFBI) play a significant role in NCAM1 24 collagen, fibronectin, and laminin interactions that KIT 22 increase fibroblast migration, invasion, and cell adhesion – fi MMP3 21 [38 40]. In healthy joints, synovial broblasts form a layer in synovial tissue with a thickness of one to two ITGB3 20 cells [41]. In synovial tissue of RA patients, synovial IL15 20 fibroblasts form thicker layers (15-20 cells thick), mainly FGF13 20 due to a higher proliferation rate, and the formation of ITGA6 18 antiapoptotic properties [42]. In addition, these gene MMP13 17 markers are also major drivers of the inflammatory PGR 17 response in RA patients and are identified as potential AR 17 markers for RA [5, 43]. The synovium is a major target of inflammation in RA. CXCL9 17 Here, we first recognize that distinct fibroblast subpop- MMP1 17 ulations differ in their molecular functions. Gene markers for cluster 0 are mainly enriched in neutrophil-mediated 3.5. Immune Cell Changes in RA Patients. Immunoinflamma- immunity, glycolysis/gluconeogenesis. Aberrant neutrophil tion appeared in our enrichment results, especially cluster 0 responses contribute to tissue damage and are associated which matched OA. To compare the differences in immune with arthritic pathological conditions [44, 45]. Aerobic gly- responses between RA and OA patients, we quantified the colysis is manifested by inflammatory signals or rapid cell infiltration of immune cells according to the immune score division, reflecting systemic inflammation [46]. Marker (Figure 6(a)). We found that B cells were significantly genes of cluster 3 were mainly associated with ECM recep- decreased, and CD8+T cells and neutrophil were signifi- tor interaction, collagen fibril organization. Activated cantly increased in RA compared to OA (Figure 6(b)). The synovial fibroblasts produce multiple ECM remodelling results of the correlation analysis with the key genes showed components, such as matrix metalloproteinases, cytokines, that neutrophil was significantly associated with all of the key and chemokines, which actively promote cellular resorp- genes (Figure 6(c)). tion and infiltration of the joint, perpetuating and perpet- Importantly, we validated significant results of our anal- uating joint inflammation [47]. Previous studies have ysis in blood or synovial tissue samples from RA and OA found that collagen fibril organization is associated with patients. Using flow cytometry, we found that the levels of the pathology of RA [48]. The presence of thinner fibers CD8+T cells and neutrophil were significantly higher in RA and high concentrations of collagen cleavage products patients than in OA patients, while the levels of B cells were have been associated with RA events [49]. significantly decreased (Figure 6(d)). QRT-PCR results Among the key genes we identified, chemokine ligand found that the mRNA levels of THBS1, CCL2, MMP13, 2 (CCL2) and matrix metalloproteinases 13 (MMP13) ICAM1, MMP3, MMP1, and CXCL1 were higher in RA were predicted as potential diagnostic markers for RA. compared with OA, and ITGA6 was lower expressed in RA Essential cytokines in the development of RA are IL-6, (Figure 6(e)). The differential expression results of the genes and IL-6 activation of endothelial cells increases adhesion were also validated by Western blot experiments, except molecule expression and CCL2 production [50]. CCL2 ITGA6 (Figure 6(f)). levels are increased in the plasma and synovial fluid of RA patients, closely correlating with increased joint infil- 4. Discussion tration of immune cells, particularly macrophages [51]. Studies have shown that CCL 2 is an effective therapeutic Previous studies have highlighted fibroblasts as potential target for RA patients [52, 53]. MMP13 expression is therapeutic targets for RA [37]. In the present study, we increased in synovial fibroblasts of RA patients [54]. Ele- sought to identify cell subpopulations of RA patient fibro- vated expression of MMP13 in RA patients may promote blasts by comparing the results of single-cell sequencing of fibroblast migration and invasion [55]. MMP13 is also an synovial tissue fibroblasts from RA and OA patients. And effective therapeutic target for multiple drugs in RA describe the contribution of different cell subpopulations to patients [56]. In contrast to OA patients, we observed 12

Group GSM1360956

RA OA GSM1360957 GSM1360958 GSM1360959 GSM1360960 GSM1360961 GSM1360962 GSM1360963 Immune score GSM1360964 0.0 0.2 0.4 0.6 GSM1360965 GSM1360966 GSM1360967 ⁎ OA RA GSM1360968 B cell GSM1360969

Figure GSM1360970 GSM1360971 T cel l CD4+ GSM1360972 GSM1360973 :Continued. 6:

⁎⁎ GSM1360974 (b) (a) GSM1360975 T cel l CD8+ GSM1360976 GSM1360977 ⁎⁎ GSM1360978 Neutrophil GSM1360979 GSM1360980 GSM1360981 Macrophage GSM1360982 GSM1360983 GSM1360984 GSM1360985 Myeloid dendritic cell GSM1360986 GSM1360987 Group Macrophage T cellCD8+ B cell Myeloid dendriticcell T cellCD4+ Neutrophil iMdRsac International Research BioMed −2 −1 0 1 2 BioMed Research International 13

Correlation 1.0 Macrophage −0.86

Neutrophil −0.2 0.02

T cell CD8+ 0.1 0.23 −0.17 0.5 T cell CD4+ −0.83−0.03−0.25 0.26

B cell −0.01−0.29−0.51 0 −0.13

CCL2 −0.41−0.05 0.18 0.7 −0.23 0.13 0.0 MMP13 0.64 −0.22−0.23 0.26 0.39 −0.4 0.24

MMP3 0.64 0.66 −0.5 −0.09 0.22 0.58 −0.45 0.37

MMP1 0.64 0.57 0.68 −0.45−0.22 0.46 0.64 −0.2 0.17 −0.5 CXCL1 0.7 0.72 0.37 0.75 −0.44 0 0.12 0.79 −0.32 0.22

ICAM1 0.38 0.44 0.14 0.36 0.62 −0.03 0.19 −0.12 0.55 0.17 −0.32

ITGA6 −0.43−0.23 −0.6 −0.13−0.29−0.41 0.36 0.27 −0.48−0.35−0.24 0.3 −1.0 B cell CCL2 MMP1 MMP3 CXCL1 ICAM1 MMP13 Neutrophil T cell CD4+ T cell CD8+ Macrophage Myeloid dendritic cell (c)

RA OA RA OA RA OA [lym] CD19 FITC-A / CD3 PC5.5-A [lym] CD19 FITC-A / CD3 PC5.5-A [lym] CD8 PE-A / CD3 PC5.5-A [lym] CD8 PE-A / CD3 PC5.5-A [Ungated] CD45 PC7-A / SSC-A [Ungated] CD45 PC7-A / SSC-A 2500 2500 ) ) C–+ C++ C–+ C++ B–+ CD8+ : 22.94% B–+ CD8+ : 16.87% 3 Neutrophil : 48.26% 3 Neutrophil : 40.95% 6 106 106 106 10 2000 2000 5 5 105 10 105 10 1500 1500 4 4 104 10 104 10

B–– B+– B–– B+– SSC-A1000 (10 SSC-A1000 (10 3 C–– 3 C–– 3 3 CD3 PC5.5-A CD3 PC5.5-A 10 10 CD3 PC5.5-A 10

CD3 PC5.5-A 10

2 2 2 500 500 102 10 10 10

B cell : 4.85% 1 B cell : 7.99% 1 101 10 101 10 0 0 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 2 3 4 5 6 101 102 103 104 105 106 10 10 10 10 10 10 101 102 103 104 105 106 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 CD19 FITC-A CD19 FITC-A CD8 PE-A CD8 PE-A CD45 PC7-A CD45 PC7-A (d)

Figure 6: Continued. 14 BioMed Research International

3 2.5 2.0 2.5 ⁎⁎⁎ ⁎⁎⁎ ⁎⁎ 2.0 2.0 1.5 2 1.5 1.5 1.0 1.0 1.0 1 0.5 0.5 0.5 Relative CCL2 mRNA level Relative THBS1 mRNA level Relative ICAM1 mRNA level Relative MMP13 mRNA level 0 0.0 0.0 0.0 OA RA OA RA OA RA OA RA

2.5 1.5 2.5 2.5 ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ 2.0 2.0 2.0 1.0 1.5 1.5 1.5 ⁎⁎ 1.0 1.0 1.0 0.5 0.5 0.5 0.5 Relative MMP1 mRNA level Relative ITGA6 mRNA level Relative MMP3 mRNA level Relative CXCL1 mRNA level 0.0 0.0 0.0 0.0 OA RA OA RA OA RA OA RA (e)

CCL2

ICAM1

THBS1

MMP13

CXCL1

ITGA6

MMP1

MMP3

GAPDH

RA OA (f)

Figure 6: Changes of immune cell and key genes in RA patients. (a) Infiltration levels of immune cells in RA and OA. RA: rheumatoid arthritis; OA: osteoarthritis. Red represents high infiltration and blue represents low infiltration. (b) The difference of infiltration for immune cells in RA compared to OA. RA: rheumatoid arthritis; OA: osteoarthritis. (c) Correlations between immune cells and key genes were analyzed through Pearson correlation. Node color from blue to red represents negative to positive correlation. × P >0:05. (d) The levels of CD8+T cell, neutrophils, and B cell in blood samples of RA and OA patients were detected by flow cytometry. (e) QRT-PCR was used to detect the mRNA levels of key genes in synovial tissue of RA and OA patients. ∗∗P <0:01, ∗∗∗P <0:001. (f) Western blot was used to detect the expression of key genes in synovial tissue of RA and OA patients. upregulation of CCL2, MMP13 in RA patients, suggesting phils were higher in RA fibroblasts than in OA patients, potential novel targets. whereas the levels of B cells were decreased. CD8+T cells Specifically, in the differential immune cell infiltration are activated in RA and produce a large number of chemo- results, we found that the levels of CD8+T cells and neutro- kines and proinflammatory cytokines [57, 58]. Neutrophil BioMed Research International 15 entry into the synovium is an important feature of the RA designed the study and supervised the experimental work, inflammatory response, which is again fueled by an intricate data analysis and interpretation, and manuscript critical network of cytokines [59]. Neutrophils damage cartilage in review. Lifen Liao and Ke Liang contributed equally to this synovial fluid and damage surrounding tissues, leading to a work. state of oxidative stress resulting from the release of reactive oxygen species (ROS) and increasing inflammatory condi- Acknowledgments tions [60]. The number of total B cells in the blood of rheumatoid arthritis patients has been shown to be reduced The work was supported by the self-funded scientific compared to healthy controls [61]. However, it has also been research topic by the Health Selected Committee of Guangxi shown that B cell depletion in RA patients is a potential ther- Zhuang Autonomous Region (No. Z20201192). apeutic intervention [62]. Some limitations are included in this study. The low Supplementary Materials number of samples we analyzed may have biased the interpretation of the results. Whether the identified potential tar- Table S1: the differentially expressed genes between RA gets have clinically significant will requires subsequent in- patients and OA patients in GSE56409. (Supplementary depth exploration. Although we validated the differences in Materials) key genes and immune cells between RA and OA, this has some limitations for interpretation of validation results as References we failed to isolate fibroblast samples for experimentation. [1] G. R. Burmester and J. E. Pope, “Novel treatment strategies in rheumatoid arthritis,” Lancet, vol. 389, no. 10086, pp. 2338– 5. Conclusion 2348, 2017. “ Chemokine and matrix metalloproteinases (MMPs) signal- [2] E. Karouzakis, M. Neidhart, R. E. Gay, and S. Gay, Molecular ” ing plays an important role in RA pathogenesis, as several and cellular basis of rheumatoid joint destruction, Immunol- ogy Letters, vol. 106, no. 1, pp. 8–13, 2006. chemokines and their receptors have been implicated in the “ inflammatory response and immune infiltration in fibro- [3] J. S. Smolen, D. Aletaha, and I. B. McInnes, Rheumatoid arthritis,” Lancet, vol. 388, no. 10055, pp. 2023–2038, 2016. blasts. Therefore, targeting chemokines and MMPs is a suit- fi able approach for the diagnosis and treatment of RA, [4] J. Widdi eld, J. M. Paterson, A. Huang, and S. Bernatsky, “Causes of death in rheumatoid arthritis: how do they compare especially CCL2, and MMP13. The significance of potential to the general population?,” Arthritis Care Res (Hoboken), target genes in RA disease is evaluated herein. This informa- vol. 70, no. 12, pp. 1748–1755, 2018. tion provides a solid background for the development of new [5] H. Y. Yap, S. Tee, M. Wong, S. K. Chow, S. C. Peh, and S. Y. drugs or other treatments. Teow, “Pathogenic role of immune cells in rheumatoid arthritis: implications in clinical treatment and biomarker develop- Data Availability ment,” Cell, vol. 7, no. 10, p. 161, 2018. [6] A. J. Silman and J. E. Pearson, “Epidemiology and genetics of The raw data can be found in GSE109449 and GSE56409. rheumatoid arthritis,” Arthritis Research, vol. 4, Supplement 3, pp. S265–S272, 2002. Ethical Approval [7] K. Areskoug-Josefsson and U. Ãberg, “A literature review of the sexual health of women with rheumatoid arthritis,” Mus- The study was approved by the ethics committee of the Nan- culoskeletal Care, vol. 7, no. 4, pp. 219–226, 2009. xishan hospital of Guangxi Zhuang Autonomous Region [8] C. Croia, R. Bursi, D. Sutera, F. Petrelli, A. Alunno, and (2021NXSYYEC-001). I. Puxeddu, “One year in review 2019: pathogenesis of rheumatoid arthritis,” Clinical and Experimental Rheumatology, Consent vol. 37, no. 3, pp. 347–357, 2019. [9] K. Schinnerling, C. Rosas, L. Soto, R. Thomas, and J. C. Aguil- Written informed consent was obtained from each partici- lón, “Humanized mouse models of rheumatoid arthritis for pant before authors commenced any samples collection. studies on immunopathogenesis and preclinical testing of cell-based therapies,” Frontiers in Immunology, vol. 10, Conflicts of Interest p. 203, 2019. [10] M. Robert and P. Miossec, “IL-17 in rheumatoid arthritis and All authors declare no competing interests. precision medicine: from synovitis expression to circulating bioactive levels,” Frontiers in medicine, vol. 5, p. 364, 2019. Authors’ Contributions [11] Y. J. Lin, M. Anzaghe, and S. Schulke, “Update on the patho- mechanism, diagnosis, and treatment options for rheumatoid Lifen Liao and Ke Liang contributed equally to the manu- arthritis,” Cell, vol. 9, no. 4, p. 880, 2020. script preparation and supervised the experimental work, [12] J. D. Turner and A. Filer, “The role of the synovial fibroblast in data analysis, and interpretation. Lan Lan coordinated the rheumatoid arthritis pathogenesis,” Current Opinion in Rheu- experimental work and contributed to data and manuscript matology, vol. 27, no. 2, pp. 175–182, 2015. preparation. Jinheng Wang contributed to the experimental [13] A. Filer, L. S. C. Ward, S. Kemble et al., “Identification of a work and manuscript preparation. Jun Guo conceived and transitional fibroblast function in very early rheumatoid 16 BioMed Research International

arthritis,” Annals of the Rheumatic Diseases, vol. 76, no. 12, Acta (BBA)-Proteins and Proteomics, vol. 1868, no. 8, article pp. 2105–2112, 2017. 140444, 2020. [14] Q. Fang, J. Ou, and K. S. Nandakumar, “Autoantibodies as [30] J. Groshek, V. de Mees, and R. Eschmann, “Modeling influence diagnostic markers and mediator of joint inflammation in and community in social media data using the digital methods arthritis,” Mediators of Inflammation, vol. 2019, Article ID initiative-twitter capture and analysis toolkit (DMI-TCAT) 6363086, 22 pages, 2019. and Gephi,” MethodsX, vol. 7, article 101164, 2020. [15] Q. Guo, D. Xu, J. Nossent, N. J. Pavlos, J. Xu, and Y. Wang, [31] G. Bindea, B. Mlecnik, M. Tosolini et al., “Spatiotemporal “Rheumatoid arthritis: pathological mechanisms and modern dynamics of intratumoral immune cells reveal the immune pharmacologic therapies,” Bone research, vol. 6, no. 1, p. 15, landscape in human cancer,” Immunity, vol. 39, no. 4, 2018. pp. 782–795, 2013. [16] F. Mizoguchi, K. Slowikowski, K. Wei et al., “Functionally dis- [32] S. Hanzelmann, R. Castelo, and J. Guinney, “GSVA: gene set tinct disease-associated fibroblast subsets in rheumatoid variation analysis for microarray and RNA-seq data,” BMC arthritis,” Nature Communications, vol. 9, no. 1, p. 789, 2018. Bioinformatics, vol. 14, no. 1, p. 7, 2013. [17] A. Filer, P. Antczak, G. N. Parsonage et al., “Stromal transcrip- [33] C. Gu, Z. Huang, X. Chen et al., “TEAD4 promotes tumor tional profiles reveal hierarchies of anatomical site, serum development in patients with lung adenocarcinoma via response and disease and identify disease specific pathways,” ERK signaling pathway,” Biochimica et Biophysica Acta - PLoS One, vol. 10, no. 3, article e0120917, 2015. Molecular Basis of Disease, vol. 1866, no. 12, article [18] R. Satija, J. A. Farrell, D. Gennert, A. F. Schier, and A. Regev, 165921, 2020. “Spatial reconstruction of single-cell gene expression data,” [34] Y. Sun, C. Gu, J. Shi et al., “Reconstruction of mediastinal ves- Nature Biotechnology, vol. 33, no. 5, pp. 495–502, 2015. sels for invasive thymoma: a retrospective analysis of 25 cases,” [19] L. Zhang, X. Shi, C. Gu et al., “Identification of cell-to-cell Journal of Thoracic Disease, vol. 9, no. 3, pp. 725–733, 2017. interactions by ligand-receptor pairs in human fetal heart,” [35] J. Chen, C. Gu, X. Chen et al., “Clinicopathological and prog- Biochimica et Biophysica Acta - Molecular Basis of Disease, nostic analyses of 86 resected pulmonary vol. 1866, no. 12, article 165917, 2020. lymphoepithelioma-like carcinomas,” Journal of Surgical [20] E. Becht, L. McInnes, J. Healy et al., “Dimensionality reduction Oncology, vol. 123, no. 2, pp. 544–552, 2021. for visualizing single-cell data using UMAP,” Nature Biotech- [36] C. Chen, X. Zhang, C. Gu et al., “Surgery performed at night by nology, vol. 37, no. 1, pp. 38–44, 2019. continuously working surgeons contributes to a higher inci- [21] M. E. Ritchie, B. Phipson, D. Wu et al., “limma powers differ- dence of intraoperative complications in video-assisted thora- ential expression analyses for RNA-sequencing and microar- coscopic pulmonary resection: a large monocentric ray studies,” Nucleic acids research, vol. 43, no. 7, p. e47, retrospective study,” European Journal of Cardio-Thoracic 2015. Surgery, vol. 57, no. 3, pp. 447–454, 2020. [22] C. Gu, X. Shi, W. Qiu et al., “Comprehensive analysis of the [37] A. Filer, “The fibroblast as a therapeutic target in rheumatoid prognostic role and mutational characteristics of m6A- arthritis,” Current Opinion in Pharmacology, vol. 13, no. 3, related genes in lung squamous cell carcinoma,” Frontiers in pp. 413–419, 2013. cell and developmental biology, vol. 9, no. 559, 2021. [38] C. C. Tseng, L. Y. Wu, W. C. Tsai et al., “Differential expres- [23] D. Aran, A. P. Looney, L. Liu et al., “Reference-based analysis sion profiles of the transcriptome and miRNA interactome in of lung single-cell sequencing reveals a transitional profibrotic synovial fibroblasts of rheumatoid arthritis revealed by next macrophage,” Nature Immunology, vol. 20, no. 2, pp. 163–172, generation sequencing,” Diagnostics (Basel), vol. 9, no. 3, 2019. p. 98, 2019. [24] C. Gu, J. Chen, X. Dang et al., “Hippo pathway core genes [39] A. Gratchev, P. Guillot, N. Hakiy et al., “Alternatively activated based prognostic signature and immune infiltration patterns macrophages differentially express fibronectin and its splice in lung squamous cell carcinoma,” Frontiers in oncology, variants and the extracellular matrix protein βIG-H3,” Scandi- vol. 11, no. 1486, 2021. navian Journal of Immunology, vol. 53, no. 4, pp. 386–392, [25] C. Gu, X. Shi, X. Dang et al., “Identification of common genes 2001. and pathways in eight fibrosis diseases,” Frontiers in Genetics, [40] K. C. Denninger, T. Marstrand, K. Moller et al., “Kinetics of vol. 11, article 627396, 2021. gene expression and bone remodelling in the clinical phase ” [26] X. Shi, X. Shao, B. Liu et al., “Genome-wide screening of func- of collagen-induced arthritis, Arthritis Research & Therapy, tional long noncoding RNAs in the epicardial adipose tissues vol. 17, no. 1, p. 43, 2015. of atrial fibrillation,” Biochimica et Biophysica Acta - Molecular [41] C. Ospelt, “Synovial fibroblasts in 2017,” RMD Open, vol. 3, Basis of Disease, vol. 1866, no. 7, article 165757, 2020. no. 2, article e000471, 2017. [27] X. Shi, T. Huang, J. Wang et al., “Next-generation sequencing [42] L. Ouboussad, A. Melville, M. H. Buch, and A. N. Burska, identifies novel genes with rare variants in total anomalous “Synovial tissue heterogeneity in rheumatoid arthritis and pulmonary venous connection,” eBioMedicine, vol. 38, changes with biologic and targeted synthetic therapies to pp. 217–227, 2018. inform stratified therapy,” Frontiers in medicine, vol. 6, p. 45, [28] M. V. Kuleshov, M. R. Jones, A. D. Rouillard et al., “Enrichr: a 2019. comprehensive gene set enrichment analysis web server 2016 [43] Y. H. Huh, G. Lee, W. H. Song, J. T. Koh, and J. H. Ryu, update,” Nucleic Acids Research, vol. 44, no. W1, pp. W90– “Crosstalk between FLS and chondrocytes is regulated by W97, 2016. HIF-2α-mediated cytokines in arthritis,” Experimental & [29] C. Gu, X. Shi, Z. Huang et al., “A comprehensive study of con- Molecular Medicine, vol. 47, no. 12, article e197, 2015. struction and analysis of competitive endogenous RNA net- [44] Y. Miyabe, C. Miyabe, T. T. Murooka et al., “Complement C5a works in lung adenocarcinoma,” Biochimica et Biophysica receptor is the key initiator of neutrophil adhesion igniting BioMed Research International 17

immune complex-induced arthritis,” Science immunology, [60] H. B. Ferreira, T. Melo, A. Paiva, and M. . R. Domingues, vol. 2, no. 7, 2017. “Insights in the role of lipids, oxidative stress and inflamma- [45] F. C. Liu, H. P. Yu, P. J. Chen et al., “A novel NOX2 inhibitor tion in rheumatoid arthritis unveiled by new trends in lipido- attenuates human neutrophil oxidative stress and ameliorates mic investigations,” Antioxidants (Basel), vol. 10, no. 1, p. 45, inflammatory arthritis in mice,” Redox Biology, vol. 26, article 2021. 101273, 2019. [61] O. Pala, A. Diaz, B. B. Blomberg, and D. Frasca, “B lympho- [46] S. You, J. H. Koh, L. Leng, W. U. Kim, and R. Bucala, “The cytes in rheumatoid arthritis and the effects of anti–TNF-α tumor-like phenotype of rheumatoid synovium: molecular agents on B lymphocytes: a review of the literature,” Clinical profiling and prospects for precision medicine,” Arthritis & Therapeutics, vol. 40, no. 6, pp. 1034–1045, 2018. Rhematology, vol. 70, no. 5, pp. 637–652, 2018. [62] S. Bugatti, B. Vitolo, R. Caporali, C. Montecucco, and [47] N. Zerrouk, Q. Miagoux, A. Dispot, M. Elati, and A. Niarakis, A. Manzo, “B cells in rheumatoid arthritis: from pathogenic “Identification of putative master regulators in rheumatoid players to disease biomarkers,” BioMed Research Interna- arthritis synovial fibroblasts using gene expression data and tional, vol. 2014, Article ID 681678, 14 pages, 2014. network inference,” Scientific Reports, vol. 10, no. 1, article 16236, 2020. [48] S. Ham, J. B. Bae, S. Lee et al., “Epigenetic analysis in rheumatoid arthritis synoviocytes,” Experimental & Molecular Medi- cine, vol. 51, no. 2, pp. 1–13, 2019. [49] O. Antipova and J. P. Orgel, “Non-enzymatic decomposition of collagen fibers by a biglycan antibody and a plausible mechanism for rheumatoid arthritis,” PLoS One, vol. 7, no. 3, article e32241, 2012. [50] S. Alivernini, B. Tolusso, G. Ferraccioli, E. Gremese, M. Kurowska-Stolarska, and I. B. McInnes, “Driving chronic- ity in rheumatoid arthritis: perpetuating role of myeloid cells,” Clinical and Experimental Immunology, vol. 193, no. 1, pp. 13– 23, 2018. [51] N. M. Elemam, S. Hannawi, and A. A. Maghazachi, “Role of chemokines and chemokine receptors in rheumatoid arthritis,” ImmunoTargets and therapy, vol. 9, pp. 43–56, 2020. [52] J. Bao, W. Liu, and Y. X. Bao, “Recombinant human interleu- kin receptor antagonist influences serum chemokines in patients with rheumatoid arthritis,” Central-European journal of immunology, vol. 39, no. 2, pp. 170–173, 2014. [53] D. L. Boyle, K. Soma, J. Hodge et al., “The JAK inhibitor tofa- citinib suppresses synovial JAK1-STAT signalling in rheumatoid arthritis,” Annals of the Rheumatic Diseases, vol. 74, no. 6, pp. 1311–1316, 2015. [54] B. A. Moore, S. Aznavoorian, J. A. Engler, and L. J. Windsor, “Induction of collagenase-3 (MMP-13) in rheumatoid arthritis synovial fibroblasts,” Biochimica et Biophysica Acta, vol. 1502, no. 2, pp. 307–318, 2000. [55] D. Hammaker and G. S. Firestein, “Epigenetics of inflammatory arthritis,” Current Opinion in Rheumatology, vol. 30, no. 2, pp. 188–196, 2018. [56] S. Dudics, D. Langan, R. Meka et al., “Natural products for the treatment of autoimmune arthritis: their mechanisms of action, targeted delivery, and interplay with the host micro- biome,” International Journal of Molecular Sciences, vol. 19, no. 9, p. 2508, 2018. [57] L. Wang, Y. Zhang, and S. Y. Zhang, “Immunotherapy for the rheumatoid arthritis-associated coronary artery disease: promise and future,” Chinese Medical Journal, vol. 132, no. 24, pp. 2972–2983, 2019. [58] M. Mellado, L. Martínez-Muñoz, G. Cascio, P. Lucas, J. L. Pablos, and J. M. Rodríguez-Frade, “T cell migration in rheumatoid arthritis,” Frontiers in Immunology, vol. 6, p. 384, 2015. [59] N. Jung, J. L. Bueb, F. Tolle, and S. Bréchard, “Regulation of neutrophil pro-inflammatory functions sheds new light on the pathogenesis of rheumatoid arthritis,” Biochemical Phar- macology, vol. 165, pp. 170–180, 2019.