Abstract
Background/Aim: Worldwide, osteoarthritis causes pain in many patients, reducing their quality of life. Unfortunately, there are limited ways to alleviate the pain of osteoarthritis patients. Today, advances in genetic analysis have made it possible to analyze various causes of disease, including osteoarthritis. However, genetic analysis of osteoarthritis patients in the Korean population has rarely been performed. This study aims to find specific gene expression patterns in synovium tissues of Korean osteoarthritis patients through transcriptome analysis.
Materials and Methods: Transcriptome analysis was performed on eight tissue samples obtained from osteoarthritis patients and seven tissue samples obtained from normal individuals. To functionally analyze the differentially expressed genes (DEGs) identified from the transcriptome analysis, Gene Ontology (GO) term enrichment analysis, network analysis, and Gene Set Enrichment Analysis (GSEA) analysis were conducted.
Results: After performing GO analysis on the top 50 DEGs, 11 candidate genes were selected based on adjusted p-value <0.05 and |log2 fold change (FC)| ≥2. Gene network analysis of the 11 DEGs confirmed their association with immune responses. Furthermore, GSEA analysis of the 11 DEGs revealed that all of them showed positive correlations with the corresponding GO terms.
Conclusion: We identified 11 candidate genes associated with immune responses that are abnormally overexpressed in the synovium tissues of Korean osteoarthritis patients. Establishment of the strategies for targeting these genes may help alleviate pain in Korean osteoarthritis patients.
Introduction
Osteoarthritis is caused by an aging population and external and genetic factors (1) and is positioned as the most common type of arthritis not only in Korea but also worldwide (2). According to the Global Data database, the global lifetime risk of developing osteoarthritis in 2021 was estimated at 14.21%, with approximately 466.3 million new cases reported (3). The burden of osteoarthritis was significantly higher in women compared to men, and the age-standardized incidence rate demonstrated a positive association with socio-demographic index levels (3-5). Notably, the Republic of Korea exhibited the highest lifetime risk, at 21.2% (6). Globally, the majority of osteoarthritis patients are women, and this sex difference becomes more pronounced after the age of 40 (7). As the number of osteoarthritis patients increases, medical expenses for osteoarthritis treatment increase, which has a huge socioeconomic impact (8, 9). Therefore, the market size of arthritis treatment is expected to increase significantly in 7 major countries (USA, France, Germany, Italy, Spain, UK, and Japan) (10, 11).
Osteoarthritis is a disease that causes irreversible, degenerative changes in limited joint components such as articular cartilage, synovial membrane, and subchondral bone, characterized by inflammation within the joint and changes in the periarticular and subchondral bones (12, 13). The areas most often affected are the hands, knees, hips, and spine. The symptoms are signs of inflammation, including pain, stiffness, and loss of mobility (14, 15). In addition, the newly reported onset of fibromyalgia following total knee arthroplasty in patients with osteoarthritis further underscores the importance of pain management in osteoarthritis (16). Since the main symptom of osteoarthritis is severe pain, the clinical management of osteoarthritis is dominated by pain relief and pain management (17). Generally, patients with end-stage osteoarthritis manage pain by replacing the joint through a surgical operation (18, 19). In the early stages of osteoarthritis, muscle strengthening, exercise, and available drugs are used rather than surgical treatment (20). Unfortunately, effective relief of joint pain is still difficult because current generic analgesics provide minimal relief and are associated with significant side effects when taken chronically (21). There are various causes of osteoarthritis, including aging, obesity, mechanical load, and genetic factors (22, 23). In particular, recent studies have been conducted to identify genetic markers using transcriptome analysis techniques. Aki and colleagues analyzed the whole-genome transcriptome of secondary hip osteoarthritis chondrocytes (22). The research findings revealed a new potential osteoarthritis-associated gene. Liu and colleagues identified 22 novel dysregulation genes associated with articular cartilage degeneration on post-traumatic osteoarthritis progression through transcriptome analysis (24). Recently, Nanusa et al. reported the results of a study related to the differential phenotype with fibroblast subsets in the synovium tissue of the pain area of patients with osteoarthritis of the knee (25). However, there are few genetic studies related to osteoarthritis in Korea.
The molecular pathogenesis of osteoarthritis is complex because it is a clinical disease involving multiple joint tissues, such as cartilage, subchondral bone, and synovial membrane (26). Osteoarthritis was previously considered a simple mechanical cartilage degeneration disease, but now it is known as a complex disease in which various factors affect the entire joint (26-28). For example, activation of matrix proteases plays a pivotal role in the development of osteoarthritis, and an imbalance between anabolism and catabolism in chondrocytes occurs during the course of osteoarthritis (29-31). In other words, advances in genetic analysis and an improved understanding of the molecular mechanisms of osteoarthritis make it possible to focus on the prevention and treatment of early osteoarthritis (8, 27, 29). In addition, understanding the molecular basis for osteoarthritis is important for developing more effective drug therapies. The pain severity in people with osteoarthritis of the knee is closely related to inflammation of the synovial membrane, called synovitis (32). Thus, we performed a transcriptome analysis focused on pain relief, which is a secondary symptom of osteoarthritis. To identify key factors that can alleviate pain in patients with osteoarthritis, we investigated differences in gene expression in the synovial region of patients with osteoarthritis. Therefore, this study aims to identify specific gene expression patterns through mRNA sequencing analysis of synovium tissues for osteoarthritis in the Korean population. In addition, it aims to establish a database to be used in preclinical trials for further drug development for pain relief.
Materials and Methods
Patients and synovium tissues. Eight synovium tissue samples were obtained during total knee arthroplasty from patients with symptomatic osteoarthritis, and seven normal synovium tissue samples were also collected. The study was approved by the institutional review board of Haeundae Paik Hospital, Inje University (#2020-09-009), and written consent was obtained from all subjects. The included osteoarthritis clinical subjects were Korean nationals [mean age of 73 years (interquartile range=60-85)] and patients with advanced knee osteoarthritis. Normal synovial tissue samples were collected from patients undergoing arthroscopic surgery for sports related injuries, including anterior cruciate ligament rupture and meniscal tear. In the control group, synovium was carefully harvested from the suprapatellar pouch under direct arthroscopic visualization. For accurate analysis, sufficient synovium tissues were safely removed to prevent bone and blood contamination and stored in RNAlater™ Stabilization Solution (Thermofisher Scientific, Waltham, MA, USA, #AM7020) until analysis.
Total RNA library preparation and sequencing. Total RNA concentration was calculated by Quant-IT RiboGreen (Invitrogen, Waltham, MA, USA, #R11490). To assess the integrity of the total RNA, samples are run on the TapeStation RNA screentape (Agilent Technologies, Santa Clara, CA, USA, #5067-5576). Only high-quality RNA preparations, with RIN greater than 7.0, were used for RNA library construction. A library was independently prepared with 1μg of total RNA for each sample by Illumina TruSeq Stranded mRNA Sample Prep Kit (Illumina, Inc., San Diego, CA, USA, #RS-122-2101). The first step in the workflow involved purifying the poly-A containing mRNA molecules using poly-T-attached magnetic beads. Following purification, the mRNA was fragmented into small pieces using divalent cations at elevated temperature. The cleaved RNA fragments were copied into the first strand cDNA using SuperScript II reverse transcriptase (Invitrogen, #18064014) and random primers. This was followed by second-strand cDNA synthesis using DNA Polymerase I, RNase H, and dUTP. These cDNA fragments then went through an end repair process, the addition of a single ‘A’ base, and then ligation of the adapters. The products were purified and enriched with PCR to create the final cDNA library. The libraries were quantified using KAPA Library Quantification kits for Illumina Sequencing platforms according to the qPCR Quantification Protocol Guide (Kapa Biosystems, Inc, Wilmington, MA, USA, #KK4854) and qualified using the TapeStation D1000 ScreenTape (Agilent Technologies, #5067-5582). Indexed libraries were then submitted to an Illumina NovaSeq (Illumina, Inc.), and the paired-end (2×100 bp) sequencing was performed.
Gene expression datasets. Paired-end sequencing reads were generated on the Illumina sequencing platform. Before starting analysis, Trimmomatic (v0.39) (33) was used to remove the reads that contained adaptor contamination, low-quality bases, and undetermined bases. A sliding window of 4 nucleotides was analyzed for each read, and sequences trimmed to shorter than 36 bases were removed. The sequence quality was then verified using FastQC (v0.10.1) (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Filtered reads were aligned to the human genome assembly GRCh38 using Star aligner (v2.7.9a) (34), and the transcript assembly was reconstructed with StringTie (v2.2.1) (35, 36). The reference genome sequence and gene annotation data were downloaded from Ensembl Release 109. After the final transcriptome was generated, StringTie was used to estimate the expression levels of all the transcripts. For differential expression analysis, differential expressed genes (DEGs) from two group comparisons were obtained using the DESeq2 package with a threshold of adjusted p-value (p.adj) <0.05, and |log2 fold change (FC)| ≥2.
Functional enrichment analysis of DEGs. To further understand the biological relevance and associated pathways of DEGs, we performed Gene Ontology (GO; http://geneontology.org/) annotation, including ‘Biological Process’, ‘Cell Composition’, and ‘Molecular Function’ (37, 38) through the R package, clusterProfiler (v4.6.2; https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html) (39-41). A p-value <0.05 was defined as the criterion of statistical significance. The Biological Network Gene Ontology (BiNGO; v3.0.5) was used for understanding and visualizing the biological network. BiNGO (www.psb.ugent.be/cbd/papers/BiNGO) (42) maps the predominant functional themes of the DEGs on the GO hierarchy and takes advantage of Cytoscape’s versatile visualization environment to produce an intuitive molecular interaction network. Also, we created and visualized the functional enrichment network of GO Biological Process term using Cytoscape (v3.9.1) (43) plug-in ClueGO (v2.5.10) (44) and CluePedia (v1.5.10) (45) with the human genome. Gene Set Enrichment Analysis (GSEA) was performed by GSEA software version 4.4.0 (46). GO (c5.go.bp.v2023.2.Hs.symbol.gmt) gene set was selected as the reference gene set.
Results
Whole mRNA sequencing for DEGs analysis. To identify candidate genes associated with osteoarthritis, synovium tissue samples were obtained from eight patients with osteoarthritis and seven normal synovium tissues. Information for each patient is shown in Table I. RNA was extracted from the collected tissue samples, and RNA-seq analysis was performed. The 15 samples generated an average of total bases 7,659,141,686 nucleotide (nt), from which total reads 75,833,086 nt remained after trimming, with a Q30 (Q30: Phred quality score, sequencing error probability of 0.1%) over 95% (Table II). Based on the read counts, differential expression analysis was performed using DESeq2 to identify DEGs, followed by downstream analyses (Figure 1). The sequencing data have been deposited in the Gene Expression Omnibus (GEO) database (GEO Series accession number: GSE254682).
Clinical information of the analysis sample.
Sequence data quality check results.
Workflow of whole mRNA sequencing analysis. The FASTQ files obtained from RNA sequencing were preprocessed using Trimmomatic, and sequence quality was assessed using FastQC. The reads were aligned to the GRCh38 reference genome, and transcriptome assembly was performed with StringTie. The assembled transcripts were quantified and normalized, and differentially expressed genes (DEGs) were identified using DESeq2. Based on these results, additional downstream analyses, including Gene Ontololgy (GO), network analysis, and Gene Set Enrichment Analysis (GSEA), were conducted.
DEGs identified in Korean osteoarthritis patients. A profiling analysis was performed to identify differentially expressed genes based on the data obtained from the RNA-seq analysis (Figure 2A). We identified 174 DEGs that changed in the osteoarthritis group compared to the control group (p.adj <0.05, and |log2FC| ≥2) (Figure 2B). As a result of the analysis, 162 up-regulated DEGs (about 93%) and 12 down-regulated DEGs (about 7%) were identified in the osteoarthritis group (Figure 2C). The changed DEGs were analyzed largely divided into non-coding genes and protein-coding genes. The overall heat-map analysis results confirmed that there was a clear difference and correlation in gene expression between the osteoarthritis patient group and the control group (Top 50) (Figure 2D). In particular, genes related to human immunity showed distinct differences.
Overall distribution chart of differential expressed genes (DEGs) expressed in the osteoarthritis (OA) group compared to the control (Ctrl) group. (A) Heat map of total read counts for the OA group and the Ctrl group. (B) Volcano plot of the OA group and the Ctrl group. (C) DEG numbers showing differences in the OA group compared to the Ctrl group. (D) Overall heatmap of the top 50 DEGs. Red indicates up-regulation, and green indicates down-regulation.
DEGs screening and functional enrichment analysis. We performed the GO analysis to determine the molecular and biological functions of the DEGs. The top 10 GO terms are shown in Figure 3. First, we found that significantly enriched GO terms for ‘Biological Processes’ were related to ‘nucleosome assembly (13 genes, p.adj=3.26E-10)’, ‘positive regulation of cyclin-dependent protein kinase activity (9 genes, p.adj=4.45E-09)’, ‘protein-DNA complex subunit organization (14 genes, p.adj=1.76E-07)’, and ‘humoral immune response (11 genes, p.adj=2.9E-0.4)’. We also found that significantly enriched GO terms for ‘Cellular Component’ were ‘nucleosome (12 genes, p.adj=1.1E-09)’, ‘immunoglobulin complex (12 genes, p.adj=7.9E-09)’, and ‘DNA packaging complex (12 genes, p.adj=1.2E-08)’. Next, we found that significantly enriched GO terms for ‘Molecular Function’ were ‘structural constituent of chromatin (12 genes, p.adj=4.5E-11)’, ‘immunoglobulin receptor binding (6 genes, p.adj=3.2E-04)’, and ‘protein heterodimerization activity (10 genes, p.adj=8.9E-04)’.
Gene Ontology (GO) terms enrichment analysis result. Dot plot shows the top 10 significantly enriched GO terms of differentially expressed genes (DEGs) in the osteoarthritis group and the control group. The x-axis represents the adjusted p-value and counts of DEGs, and y-axis represents the different GO terms.
Identification of 11 candidate genes among DEGs in Korean osteoarthritis patients. We identified meaningful candidate genes for alleviating osteoarthritis pain, which was the main goal of this study. In particular, we identified DEGs with interest in ‘Biological Functions’ in various GO analysis results. We were interested in DEGs belonging to the human immunity response from the identified analysis results (up and down-regulation, p.adj <0.05, |log2FC| ≥2) (Figure 4A). We focused on changes in a total of 11 DEGs, H2B clustered histone 10 (H2BC10; 6.62-fold increase), interferon alpha 21 (IFNA21; 6.19-fold increase), immunoglobulin heavy variable 3-20 (IGHV3-20; 6.17-fold increase), immune globulin heavy constant mu (IGHM; 5.72-fold increase), immunoglobulin heavy variable 3-49 (IGHV3-49; 5.69-fold increase), immunoglobulin heavy variable 3-66 (IGHV3-66; 5.27-fold increase), H2B clustered histone 8 (H2BC8; 4.66-fold increase), immune globulin lambda constant 7 (IGLC7; 3.97-fold increase), immunoglobulin kappa variable 3-20 (IGKV3-20; 3.56-fold increase), immune globulin lambda constant 6 (IGLC6; 3.28-fold increase), and H2B clustered histone 6 (H2BC6; 2.90-fold increase). In particular, we segmented and analyzed DEGs related to B cell activity among the categories of human immune responses. In addition, we confirmed through gene network analysis that the discovered key factors were related to various human immune responses (Figure 4B). We performed BingGO analysis to confirm the association with the GO terms ‘Biological Process’, ‘Cellular Component’, and ‘Molecular Function’, by entering 11 DEGs of interest (Figure 4C). As a result, it was confirmed that there was a significant association between the immune system process and the antigen binding.
Selection of candidate differentially expressed genes (DEGs) in tissues from Korean patients with osteoarthritis. (A) Heatmap of DEGs related to the human immune response. Red indicates up-regulation, and green indicates down-regulation. (B) A network of DEGs showing the relationship of key factors about several human immune responses. (C) BingGO analysis of DEGs in the human immune response category.
GSEA analysis of 11 DEGs. We performed GSEA analysis to examine the association between the hub genes and GO terms (Figure 5). All 11 DEGs (H2BC10, IFNA21, IGHV3-20, IGHM, IGHV3-49, IGHV3-66, H2BC8, IGLC7, IGKV3-20, IGLC6, and H2BC6) were found to be associated with the ‘humoral immune response’. Among them, seven DEGs (IGHV3-20, IGHM, IGHV3-49, IGHV3-66, IGLC7, IGKV3-20, and IGLC6) were associated with ‘immunoglobulin complex’, ‘immunoglobulin complex, circulating’, and ‘antigen binding’. Lastly, six hub genes (IGHV3-20, IGHM, IGHV3-49, IGHV3-66, IGLC7, and IGLC6) were related to ‘humoral immune response mediated by circulating immunoglobulin’, ‘phagocytosis, recognition’, ‘positive regulation of B cell activation’, and ‘immunoglobulin receptor binding’. All 11 candidate genes were found to be overexpressed in osteoarthritis synovium tissues compared to normal synovium tissues, and the GSEA results revealed that they were all positively correlated with immune-related GO terms.
Gene Set Enrichment Analysis (GSEA) results. GSEA was performed on the full ranked list of total genes. Shown here are representative immuno-related GO terms in which of the 11 individual DEGs are included.
Discussion
According to the World Health Organization, approximately 350 million people worldwide are reported to suffer from osteoarthritis, with the lifetime risk in Korea reaching as high as 21.2% (6, 47). Osteoarthritis is a degenerative disease for which there is no definitive cure, and current treatment methods primarily focus on pain relief, although their efficacy remains unsatisfactory (48). In the past, synovial inflammatory responses were primarily thought to originate from debris generated by cartilage wear (49). However, recent studies have reported that these inflammatory responses may, in fact, serve as early indicators of cartilage damage in osteoarthritis (50). Moreover, emerging evidence suggests that immune responses and inflammatory mechanisms also play critical roles in the disease progression (51, 52). Therefore, for effective pain relief, it is necessary to adopt strategies that enable early diagnosis and proper regulation of synovial inflammatory responses during the initial stages of the disease (53). We screened a list of differentially expressed genes by comparing synovium tissues from osteoarthritis patients and normal individuals in the Korean population. To understand the functions of the top 50 genes, we performed GO term enrichment analysis. We focused on genes belonging to the human immune response category. As a result, we identified 11 candidate genes related to immune that were overexpressed in the synovium tissues of osteoarthritis patients compared to normal tissues. Individual GSEA analyses of these genes demonstrated that each gene showed a positive correlation with the associated GO terms.
Among the DEGs we were interested in IFNA21. In the Korean Osteoarthritis Genetic Database, we confirmed that this gene increased 6.18-fold in the osteoarthritis group compared to the normal group. IFNA21 is one of the type I interferon families (IFNα and IFNβ), a cytokine created in response to viral infection, and plays an important role in interfering with viral replication by mediating immune responses (54). Type I IFN genes have been found to play an important role in the immune response process inside cancer cells and in the progression of chronic viral infection. Interestingly, IFN genes provide both normal and cancer cells with the necessary inflammatory signals at the same time, acting as a double-edged sword (55). This result suggests that it may be possible to reconstruct the immune system to control these chronic and cancer diseases by basically regulating the immune response. Recently, it has been suggested that the IFN gene can induce cancer stem cell-mediated chemotherapy resistance through a persistent IFN-I response, causing immune dysfunction and resistance to therapeutic agents (56, 57). In addition, IFNA21 may be involved in the development of inflammation in the progression of coronary artery disease. It may contribute to the pathophysiological changes of coronary artery disease by regulating the inflammatory process mediated by this gene (54). Therefore, we suggest that further research is needed on the correlation between IFN genes, including IFNA21, and osteoarthritis.
Next, among the genes involved in the human immune response, we were interested in IGHM, which regulates B cell activity. It was confirmed that this gene increased 5.72-fold in the osteoarthritis group compared to the normal group. Immunoglobulin (Ig) is an antigenic receptor expressed in B cells and secreted from plasma cells, and is one of the key components in the adaptive immune response process (58). Human Ig molecules are divided into heavy (IgH) and light chains (IgL), consisting of invariant and variable domains. The IgH is composed of repetitive, highly homologous gene composites of different types [variable (V), diversity (D), and joining (J)] (59). In a recent 2023 study, three key genes, FAS, GPR183, and TFRC, which reveal the B cell-associated molecular landscape of synovium in rheumatoid arthritis (RA) patients, were reported as potential targets for clinical diagnosis and immunomodulatory treatment of RA (60). Interestingly, at the same time, another research team also proposed six RNA modification-related genes (ADAMDEC1, IGHM, OGN, TNFRSF11B, SCARA3, and PTN), including IGHM, as potential osteoarthritis pathogenesis biomarkers (61). This is consistent with the Korean Osteoarthritis Patient Genetic Base data we constructed, and presents a new direction for understanding the mechanism, diagnosis, and treatment of osteoarthritis.
Additionally, we were interested in genes that change in the B cell receptor complex region. Among the DEGs, we focused on IGLC6 and IGLC7. As mentioned above, one of the light chain families of human Ig molecules plays a role in the extracellular space and is involved in several processes, including the activation of antigen-binding activity and immune responses, defense responses to other organisms, and phagocytosis (62). Immunoglobulin lambda (Ig λ) has been reported to be expressed in many normal and tumor tissues. In the past, studies on the function and clinical significance of tumor-derived Ig λ have been insignificant (63). Recent findings have shown that the expression of most IGLCs, including IGLC1, IGLC2, IGLC3, IGLC4, IGLC5, IGLC6, and IGLC7, is up-regulated in severe squamous cell carcinoma and endoscopic adenocarcinoma tissues compared to normal cervical tissues (64). In addition, bioinformatic analysis was utilized to present the potential value of Ig λ as a prognostic and therapeutic marker for uterine cancer and provide a new direction for the treatment of uterine cancer (64). We confirmed an increase of 3.28-fold of IGLC6 and 3.97-fold of IGLC7 in the results of Korean osteoarthritis gene analysis. Based on this, research on IGLC6 and IGLC7, which have the potential to increase the prognosis and treatment effects of osteoarthritis, an irreversible disease like cancer, is needed.
H2BC10, H2BC8, and H2BC6 belong to the H2B clustered histone family. H2BC10 is a ubiquitin and ubiquitin-like gene-related gene that has been shown to be significantly down-regulated in oral squamous cell carcinoma tissues (65). H2BC8 is a pyroptosis-related gene in papillary thyroid carcinoma, and a strong correlation has been observed between its expression and tumor immune microenvironment remodeling (66). IGHV3-20, IGHV3-49, and IGHV3-66 are immunoglobulin heavy variable region-related genes. Immunoglobulin V segments may be mis-quantified in RNA-seq due to recombination (67). IGHV3-20 has been identified as a differentially expressed protein in patients co-infected with HIV and HBV (68), while IGHV3-49 has been identified as a major down-regulated gene in patients infected during the chikungunya outbreak on Réunion Island (69). IGHV3-66 is expressed as part of a neutralizing antibody targeting the SARS-CoV-2 spike protein (70, 71). IGKV3-20 has been shown to be dysregulated in acute myeloid leukemia (72) and chronic myeloid leukemia (73, 74), and is also activated during SARS-CoV-2 infection (75). Furthermore, IGKV3-20 has been reported to increase affinity for rheumatoid factor binding sites in RA, potentially exacerbating the disease (76). The association with human diseases highlights links to RA as well as other inflammatory responses. Several DEGs belong to immunoglobulin variable segments and histone clusters, which may reflect B cell infiltration and proliferation in the inflamed synovium. However, B cell activity is also closely linked to pain, as activated B cells release cytokines and autoantibodies that promote immune complex formation, complement activation, and nociceptor sensitization (77-80). Thus, while these genes may indicate immune infiltration, they could also contribute to pain provocation. Therefore, developing therapeutic strategies targeting these genes is expected to help alleviate pain.
With the advent of an aging society, the prevalence of osteoarthritis is steadily increasing, and the lifetime risk is particularly high in Korea. This study aims to profile the gene expression patterns of osteoarthritis in the Koreans and to establish a database that supports drug development and preclinical animal studies for pain relief. These resources will provide a blueprint for developing effective therapeutic strategies for osteoarthritis. However, a limitation of this study is that most osteoarthritis patients were elderly women, whereas the synovium tissues used as controls were obtained from younger men. In addition, a limitation of this study is that we did not analyze patient pain scores, synovium MRI, or cytokine/protein data. Nevertheless, by identifying inflammation-related DEGs in Korean patients with osteoarthritis, this study is expected to contribute to pain relief in osteoarthritis patients.
Conclusion
In this study, transcriptome analysis was performed to profile DEGs in the synovium tissues of Korean osteoarthritis patients. A total of 162 up-regulated DEGs and 12 down-regulated DEGs were identified, among which 138 were protein-coding genes. From these, the top 50 DEGs were selected for GO term enrichment analysis, leading to the identification of 11 DEGs associated with immune responses. The correlations among these 11 DEGs were validated through gene network analysis, and positive associations with the corresponding GO terms were confirmed by GSEA analysis. Ultimately, this study identified 11 immune related candidate genes in the synovium tissues of Korean osteoarthritis patients and established a database that can be utilized for preclinical trials aimed at developing the strategies for pain relief.
Footnotes
Authors’ Contributions
Conceptualization, J.R., J.J., S.W.M. and W.K.; investigation, J.R., J.J., S.W.M. and W.K.; data curation, J.R., J.J., S.J., G.P., J.K., S.W.M. and W.K.; writing – original draft preparation, J.R., J.J., S.W.M. and W.K.; visualization, J.R., J.J., S.J., G.P., J.K., S.W.M. and W.K.; supervision, S.W.M. and W.K.; project administration, S.W.M. and W.K.; All the Authors have read and agreed to the published version of the manuscript.
Conflicts of Interest
No potential conflicts of interest are reported by the authors.
Artificial Intelligence (AI) Disclosure
No artificial intelligence (AI) tools, including large language models or machine learning software, were used in the preparation, analysis, or presentation of this manuscript.
- Received August 18, 2025.
- Revision received September 12, 2025.
- Accepted September 15, 2025.
- Copyright © 2025 The Author(s). Published by the International Institute of Anticancer Research.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC-ND) 4.0 international license (https://creativecommons.org/licenses/by-nc-nd/4.0).
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