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

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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 Research Article Marker Genes Change of Synovial Fibroblasts in Rheumatoid Arthritis Patients 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 syno- vial 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 tar- gets. 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
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