Skip to main content
NCBI home page
American Journal of Clinical and Experimental Immunology logoLink to American Journal of Clinical and Experimental Immunology
. 2025 Aug 15;14(4):204–222. doi: 10.62347/XRGR9217

Identification and analysis of immune aging related biomarkers in cartilage and meniscus tissues of osteoarthritis

Zhian Chen 1,*, Mingjun Li 2,*, Yujiao Feng 2, Yanling Chen 2, Zhijun Cai 2, Yongqing Xu 2, Rongqing Pang 3
PMCID: PMC12444405  PMID: 40977917

Abstract

This study aimed to investigate the relationship between immunosenescence and osteoarthritis (OA) and analyze its potential clinical implications. Thus, we conducted transcriptome sequencing by collecting clinical meniscus (Aging_meniscus:Control_meniscus = 3:7) and cartilage tissues (Aging_cartilage:Control_cartilage = 2:6). Meanwhile, immune-related genes (IRGs) and aging-related genes (ARGs) were included in this research. The differentially expressed genes (DEGs) between Aging_meniscus and Control_meniscus as well as Aging_cartilage and Control_cartilage were analyzed by differential analysis, respectively. Then, differentially expressed IRGs (DEIRGs) were generated by crossing DEG with IRGs. Similarly, differentially expressed ARGs (DEARGs) were achieved by intersecting DEG and ARGs. To obtain genes simultaneously associated with immune and aging in both meniscus and cartilage samples, biomarkers were screened out by crossing share.IRGs and share.ARGs overlapped by DEIRGs1 and DEIRGs2 as well as DEARGs1 and DEARGs2, respectively. In addition, the biomarkers’ functions were analyzed by gene set enrichment analysis (GSEA). To detect the regulatory mechanism, a miRNA-mRNA-transcription factors (TFs) regulatory network and a X2K network were constructed. Moreover, disease association analysis and potential small molecule drugs for biomarkers were also performed to further reveal the possible role of biomarkers for OA. Then, 3 biomarkers, namely Insulin-like Growth Factor 1 Receptor (IGF1R), Interleukin 7 receptor (IL7R) and Leptin receptor (LEPR), were selected out through the intersection of 14 share.IRGs and 4 share.ARGs. And they were all enriched in ‘ribosome’ from both meniscus and cartilage samples, and had complex regulatory networks. In all, the expression of IGF1R was markedly up-regulated in OA (P < 0.05). Eventually, mecasermin could stably bind to IGF1R and simvastatin could stably bind to LEPR. It suggested that mecasermin and simvastatin may exhibit significant clinical potential in treating immunosenescence-related OA.

Keywords: Osteoarthritis, meniscus, cartilage, aging, immune

Introduction

Osteoarthritis (OA) is a degenerative joint disease characterized by cartilage degeneration, osteophyte formation, subchondral bone sclerosis abnormalities, as well as synovial fibrosis and proliferation [1-3]. According to statistics, approximately 240 million people worldwide suffer from OA and are deeply troubled, resulting in huge medical costs and mortality rates [4]. The current methods for treating OA include: nonsteroidal anti-inflammatory drugs (NSAIDs) [5], intra-articular injection of corticosteroids [6], intra-articular injection of hyaluronic acid (HA) [7], central nervous system inhibitors [8], Biological inhibitors such as IL-1 or TNF-α [9] and joint replacement surgery [10] are not widely accepted due to poor long-term efficacy of drugs and biologics, and joint replacement faces various postoperative complications and other disadvantages. At present, various risk factors for OA have been recognized from the etiology, such as age, obesity, joint trauma, biomechanical changes, and developmental diseases. However, the exact pathogenesis of OA is still unknown, so finding biomarkers for OA is crucial.

In recent years, researchers have been trying to uncover the biological information related to human aging, thereby inhibiting the progression of age-related diseases [11], including neurological diseases, cardiovascular diseases, and osteoarticular diseases [12,13]. In OA, aging cells increase with age, while the proliferation of chondrocytes and meniscus cells decreases, leading to tissue regeneration and impaired function, thereby exacerbating the progression of the disease [14]. In addition to aging cells, immune cells (neutrophils, macrophages, monocytes, dendritic cells, and natural killer cells) play a crucial role in the pathogenesis of OA [15]. They not only induce the expression of inflammatory factors, but also activate key degrading enzymes to degrade chondrocytes and extracellular matrix [16]. In OA, there is not yet sufficient understanding of the combined analysis of biomarkers for aging and immune cells. Currently, biomarkers can provide clinical diagnostic guidance and prognosis for specific causes of OA, such as hemophilia [17], alkaptonuria/ochronosis and Kashin Beck Disease [18], and rheumatoid arthritis [19]. Regarding patients with OA without specific causes, studying aging and immune cells may be the best approach. Therefore, it is urgent to research and develop biomarkers for aging and immune cells.

Although various tissue cells are involved in the pathological process of OA, chondrocytes are considered a key factor in the occurrence and development of OA [20]. At the same time, the meniscus is an important stable and mechanical buffer structure between the femur and tibia, which is crucial for maintaining knee joint function [21]. Therefore, this study is based on transcriptome data of cartilage, meniscus, and normal samples from patients with OA. A series of bioinformatics methods are used to screen for immune and age-related biomarkers in OA. Through their functions, regulatory mechanisms, and drug prediction, further understanding of the driving mechanism of OA is achieved, and theoretical reference is provided for the clinical treatment of OA.

Materials and methods

Data source

Transcriptome sequencing data were collected from samples of meniscus (Aging_meniscus:Control_meniscus = 3:7) and cartilage (Aging_cartilage:Control_cartilage = 2:6). Aging_meniscus and Aging_cartilage were collected from OA over 60 years of age. Control_meniscus and Control_cartilage were obtained from 18-50 year olds with lower extremity injuries or more than a disarticulated knee. The external validation set GSE114007, containing 20 OA and 18 Control from cartilage tissues, was mined from Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). And 1,793 immune-related genes (IRGs) [22] were retrieved from the Immunology Database and Analysis Portal database (ImmPort, https://www.immport.org), along with 307 aging-related genes (ARGs) from the Human Aging Genomic Resources (HAGR, https://www.genomics.senescence.info/).

Acquisition of differentially expressed genes (DEGs) from the samples of meniscus and cartilage

To obtain the DEGs1, differential expression analysis was performed using DESeq2 package (v 1.34.0, [23]) for the 2 meniscus groups (Aging_meniscus vs Control_meniscus) with P < 0.05, |log2FC| > 0.5. Then, differentially expressed IRGs1 (DEIRGs1) were generated by crossing DEG1 with IRGs. Similarly, differentially expressed ARGs1 (DEARGs1) were achieved by overlapping DEG1 with ARGs. To explore the biological functions and processes in which DEIRGs1 and DEARGs1 might be involved, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the up-regulated and down-regulated genes of these two sets of genes, respectively, using ClusterProfiler package (v 4.7.1, [24]). Similarly, DEGs2 from Aging_cartilage and Control_cartilage groups were also obtained by DESeq2 with same parameters. In addition, differentially expressed IRGs2 (DEIRGs2) and differentially expressed ARGs2 (DEARGs2) were acquired by taking the intersection of DEG2 with IRGs as well as DEG2 with ARGs, respectively. The functions of DEIRGs2 and DEARGs2 were also detected by GO and KEGG.

Identification and enrichment analysis of biomarkers

To obtain genes simultaneously associated with immune in both meniscus and cartilage samples, DEIRGs1 and DEIRGs2 were overlapped, resulting in share.IRGs. In the same way, share.ARGs were obtained by overlapping DEARGs1 and DEARGs2. Additionally, share.IRGs and share.ARGs were taken to intersection, so as to get biomarkers that were simultaneously related to immune and aging in both meniscus and cartilage samples. Functional enrichment of the biomarkers was then performed using GO and KEGG. Moreover, a co-expression for biomarkers was constructed for biomarkers using GeneMANIA (http://genemania.org/) to further explore protein interactions of biomarkers.

Subcellular localization and gene set enrichment analysis (GSEA) further explore biomarker functions

Based on the mRNA fasta files of the biomarkers obtained from National Center for Biotechnology Information (NCBI, https://www.ncbi.nlm.nih.gov/), subcellular localization analysis of the biomarkers was performed to provide further insight into gene expression and cellular function using the mRNALocater database (http://bio-bigdata.cn/mRNALocater/) [25]. The GSEA for biomarkers were performed on cartilage samples and meniscus samples, respectively. These analyses were conducted based on the ranking of correlation coefficients of the biomarkers with all genes utilizing ClusterProfiler package. The c2.cp.kegg.v7.5.1.symbols.gmt from the Molecular Signatures Database (MSigDB, https://www.gsea-msigdb.org/gsea/msigdb) was used as background gene set.

Construction of regulatory networks

To explore the molecular regulation of biomarkers, the Encyclopedia of RNA Interactomes (ENCORI, http://starbase.sysu.edu.cn/index.php) and miRWalk (http://mirwalk.umm.uni-heidelberg.de/) were used to predict the upstream miRNAs of biomarkers, respectively. We intersected the pairs of mRNA-miRNA relationships from both databases and retained miRNAs that target two or more mRNAs simultaneously as core miRNAs. The transcription factors (TFs) were then obtained by identifying the intersection of predicted mRNA-TF relationship pairs from JASPAR (https://jaspar.genereg.net) and ChIP-X Enrichment Analysis (ChEA, https://amp.pharm.mssm.edu/ChEA3) databases. Finally, the core mRNA-miRNA network and mRNA-TF network were integrated and visualized using CytoScape (v 3.9.1, [26]) to obtain the miRNA-mRNA-TF regulatory network. Furthermore, TFs, kinases, and related proteins associated with biomarkers were analyzed by eXpression2Kinases (X2K, https://amp.pharm.mssm.edu/X2K/) to explore potential regulatory mechanisms of biomarkers.

Disease association analysis and drug prediction

In order to analyze the role of biomarkers in other orthopedic diseases, the relationship between biomarkers and orthopedic diseases was analyzed using the Comparative Toxicogenomics Database (CTD, https://ctdbase.org/), and the Top 5 diseases of each biomarker were selected for presentation according to inference score. In addition, to screen for small molecule drugs associated with biomarkers, the Drug-Gene Interaction database (DGIdb, https://dgidb.org/) was used to predict potential drugs for biomarkers. To further validate the potential role of biomarkers in drug therapy, the highest scoring drugs with available molecular structures were downloaded from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) according to inference score. Meanwhile, the 3D protein structures of the corresponding biomarkers were downloaded from the Protein Data Bank (PDB, https://www.rcsb.org/) database. Finally, molecular docking was performed for the selected drugs and biomarkers.

Expression of biomarkers

Biomarkers expression was shown separately in meniscus and cartilage samples based on transcriptome sequencing data. To further investigate the role of biomarkers in OA, the expression of biomarkers was validated by reverse transcription quantitative polymerase chain reaction (RT-qPCR). In this experiment, a total of 6 clinical cartilage samples were obtained from control and OA patients in people’s liberation army joint logistic support force 920th hospital, including 3 control and 3 OA samples. Additionally, 6 clinical meniscus samples were collected, of which 3 samples were from OA patients and 3 samples from control individuals. This study was approved by the Ethics Committee of Hospital 920 of the Joint Logistics Support Force (No. 2021-067 (Department) -01). Total RNA was prepared using TRIZol reagent. Reverse transcription was performed using the SureScript First-Strand cDNA Synthesis Kit to obtain cDNAs. RT-qPCR was performed as follows: a total of 40 cycles, 95°C for 1 min, 95°C for 20 s, 55°C for 20 s, and 72°C for 30 s. GAPDH was used as the internal reference genes. The RT-qPCR primers were listed in Table S1. The relative expression levels of biomarkers were calculated by 2-∆∆ct method.

Statistical analysis

R software (v 4.2.3) was engaged to implement statistical analysis. Unless otherwise specified, P < 0.05 was guessed as statistically significant.

Results

There were 4,199 DEGs1 between Aging_meniscus and Control_meniscus groups

In total, 4,199 DEGs1 (2,247 up-regulated and 1,952 down-regulated) between Aging_meniscus and Control_meniscus groups were gained (Figure 1A, 1B). Furthermore, 202 DEIRGs1 (75 up-regulated and 127 down-regulated) and 72 DEARGs1 (23 up-regulated and 49 down-regulated) were obtained by intersecting DEGs1 with 1,793 IRGs and 307 ARGs, respectively (Figure 1C, 1D). In GO, 75 up-DEIRGs1 (like ‘positive regulation of cytokine production’) and 127 down-DEIRGs1 (‘cytokine-mediated signaling pathway’, ‘cytokine activity’ and so on) were enriched for a number of cytokine-related pathways, which were closely linked to immune and inflammatory responses (Figure 1E). In KEGG, both up- and down-DEIRGs1 were enriched in ‘cytokine-cytokine receptor interaction’ (Figure 1F). In other hand, 23 up-DEARGs1 enriched in ‘cellular response to abiotic stimulus’, ‘response to UV’, ‘p53 binding’ and so forth. And 49 down-DEARGs1 involved in ‘response to oxidative stress’, ‘D repair complex’, ‘phosphatidylinositol 3-kinase complex’, etc. (Figure 1G). In KEGG, both up- and down-DEARGs1 were related to ‘C-type lectin receptor signaling pathway’, ‘FoxO signaling pathway’ and other and other cellular senescence-related pathways (Figure 1H).

Figure 1.

Figure 1

Open in a new tab

A. Volcano map of DEGs1 between Aging_meniscus and Control_meniscus. Green represents a significant up-regulation of DEGs1, represents downward adjustments, orange refers to a down-regulation of DEGs1 and gray represents no significant difference in gene expression. B. Heat map of DEGs1. Red indicates high expression and green indicates low expression in Aging_meniscus. C. Venn map for 202 DEIRGs1 by intersecting DEGs1 and IRGs. D. Venn map of 72 DEARGs1 by intersecting DEGs1 and ARGs. E. GO term enrichment of up- (left) and down-DEIRGs1 (right). F. KEGG pathway analysis of up- (left) and down-DEIRGs1 (right). G. GO term enrichment of up- (left) and down-DEARGs1 (right). H. KEGG pathway analysis of (left) and down-DEARGs1 (right).

There were 934 DEGs2 between Aging_cartilage and Control_cartilage groups

Altogether 934 DEGs2 (216 up-regulated and 718 down-regulated) were got between Aging_cartilage and Control_cartilage groups (Figure 2A, 2B). Moreover, 88 DEIRGs2 (15 up-regulated and 73 down-regulated) and 8 DEARGs2 (3 up-regulated and 5 down-regulated) were obtained by intersecting DEGs2 with 1,793 IRGs and 307 ARGs, respectively (Figure 2C, 2D). In addition, 15 up-DEIRGs2 enriched in ‘response to ketone’, ‘response to alcohol’, ‘response to prostaglandin’, etc. and 73 down-DEIRGs2 enriched to multiple immune system-related pathways by GO, e.g., ‘B cell receptor signaling pathway’, ‘immunoglobulin complex’, and ‘antigen binding’ (Figure 2E). Both up- and down-DEIRGs2 were also enriched in ‘cytokine-cytokine receptor interaction’ in KEGG (Figure 2F). Meanwhile, ‘neuronal cell body’, ‘peptide hormone binding’, ‘hormone binding’ etc. in GO might be connected to 3 up-DEARGs2, and ‘ERK1 and ERK2 cascade’, ‘positive regulation of MAP kinase’, ‘cytokine receptor binding’ and so on in GO were associated with 5 down-DEARGs2 (Figure 2G). For KEGG pathways, up-DEARGs2 involved in ‘longevity regulating pathway’, ‘HIF-1 signaling pathway’, ‘FoxO signaling pathway’ and so on, and down-DEARGs2 involved in ‘C-type lectin receptor signaling pathway’, ‘FoxO signaling pathway’, ‘Cellular senescence’ etc. (Figure 2H).

Figure 2.

Figure 2

Open in a new tab

A. Volcano map of DEGs2 between Aging_cartilage and Control_cartilage groups. Green represents a significant up-regulation of DEGs2, represents downward adjustments, orange refers to a down-regulation of DEGs2 and gray represents no significant difference in gene expression. B. Heat map of DEGs2. Red indicates high expression and green indicates low expression in Aging_cartilage. C. Venn map for 88 DEIRGs2 by intersecting DEGs2 and IRGs. D. Venn map of 8 DEARGs2 by intersecting DEGs2 and ARGs. E. GO term enrichment of up- (left) and down-DEIRGs2 (right). F. KEGG pathway analysis of up- (left) and down-DEIRGs2 (right). G. GO term enrichment of up- (left) and down-DEARGs2 (right). H. KEGG pathway analysis of up- (left) and down-DEARGs2 (right).

IGF1-R, IL-7R and LEPR were recognized as biomarkers and were mainly localized in cytoplasm, endoplasmic reticulum, and extracellular region, respectively

A total of 14 share.IRGs and 4 share.ARGs were identified by overlapping DEIRGs1 and DEIRGs2 as well as DEARGs1 and DEARGs2, respectively (Figure 3A, 3B). Then, 3 biomarkers, namely insulin-like growth factor 1 receptor (IGF1-R), interleukin 7 receptor (IL-7R) and leptin receptor (LEPR), were selected out through the intersection of 14 share.IRGs and 4 share.ARGs (Figure 3C). The 3 biomarkers enriched in ‘hormone binding’, ‘cytokine receptor activity’, ‘immune receptor activity’, etc. in GO (Figure 3D and Table S2) and ‘FoxO signaling pathway’, ‘cytokine-cytokine receptor interaction’, ‘longevity regulating pathway’, etc. in KEGG (Figure 3E and Table S3). Additionally, the co-expression indicated 3 biomarkers with diverse forms of interactions or co-expression with 20 proteins (e.g. IL-7, IGF1, LEP) (Figure 3F). Subcellular localization could enhance comprehension of gene expression and cellular function. In this study, IGF1-R was predominantly located in the cytoplasm and might play an important role in regulating intracellular signaling processes. On the other hand, IL-7R was mainly sited in the endoplasmic reticulum and probably participated in protein synthesis and folding. LEPR was mainly situated in the extracellular region, presumed to exist on the cell membrane and connected to extracellular signaling and intercellular communication (Figure 3G).

Figure 3.

Figure 3

Open in a new tab

A. Venn map for 14 share.IRGs by overlapping DEIRGs1 and DEIRGs2. B. Venn map for 4 share.ARGs by overlapping DEARGs1 and DEARGs2. C. Three biomarkers (IGF1-R, IL-7R and LEPR) were obtained by 14 share.IRGs and 4 share.ARGs. D. GO term enrichment of 3 biomarkers. E. KEGG pathway analysis of 3 biomarkers. F. Co-expression network for 3 biomarkers. The inner circle is 3 biomarkers, and the outer circle is other proteins that have interaction or co-expression relationships with the 3 biomarkers, with different colored lines representing different relationships. G. Subcellular localization for 3 biomarkers. Different colors represent various organelles.

IGF1-R, IL-7R and LEPR were all enriched in ‘ribosome’ from both meniscus and cartilage samples

In meniscus, IGF1-R, IL-7R, and LEPR were all enriched in ‘oxidative phosphorylation’ and ‘ribosome’ pathways by GSEA (Figure 4A). In cartilage, IGF1-R, IL-7R, and LEPR were all also enriched in ‘ribosome’ pathway, while IGF1-R and LEPR were both enriched in ‘DNA replication’ and ‘spliceosome’ (Figure 4B).

Figure 4.

Figure 4

Open in a new tab

A. GSEA enrichment analysis for KEGG geneset in IGF1-R, IL-7R, and LEPR in meniscus. B. GSEA enrichment analysis for KEGG geneset in IGF1-R, IL-7R, and LEPR in cartilages.

Three biomarkers had complex regulatory networks

On the basis of upstream miRNAs of biomarkers predicted by ENCORI and miRWalk, a total of 275 relationship pairs were obtained by 265 miRNAs and 3 mRNAs (IGF1-R, IL-7R, and LEPR). Further, we identified 19 relationship pairs involved 3 mRNAs and 9 core miRNAs (hsa-miR-124-3p, hsa-miR-183-5p, hsa-miR-186-5p etc.) by retaining miRNAs that target two or more mRNAs simultaneously (Table S4). Meanwhile, 4 relationship pairs (GATA2-LEPR, STAT3-LEPR, STAT3-IL-7R, E2F1-IGF1-R) from the 3 mRNAs and 3 TFs (GATA2, STAT3 and E2F1) were acquired by JASPAR and ChEA databases. Finally, we integrated the core mRNA-miRNA network and mRNA-TF network to obtain the miRNA-mRNA-TF regulatory network using CytoScape (Figure 5A). In addition, a X2K network was constructed, which included 8 kinases (e.g. CDK1, GSK3B and MAPK14), 10 TFs (e.g. YY1, GATA1 and SPI1) and lots of intermediate proteins (Figure 5B).

Figure 5.

Figure 5

Open in a new tab

A. The miRNA-mRNA-TF regulatory network. Orange labels are biomarkers, green labels are TFs, and pink labels are miRNAs. B. X2K regulatory network. Red labels are TFs, gray labels are intermediate proteins, blue labels are kinases, green lines represent phosphorylation, and gray lines represent protein-protein interactions.

Mecasermin could stably bind to IGF1R and LEPR

The results of disease association analysis showed that craniofacial abnormalities were predicted by all 3 biomarkers, and cleft palate and rheumatoid arthritis were predicted by both IL-7R, and LEPR. Thus, there was a strong association between the biomarkers obtained and orthopedic diseases (Figure 6A). Based on the DGIdb database, we predicted potential small molecule drugs for three biomarkers, and only 2 biomarkers had outcomes, with 10 drugs (Rinfabate, Cixutumumab, Mecasermin, etc.) for IGF1-R and 3 drugs (Atorvastatin, Metreleptin and Simvastatin) for LEPR (Figure 6B). Ultimately, Mecasermin was selected to interface with IGF1-R (ΔG = -9.5 KJ/mol, Figure 6C) and Simvastatin was selected to interface with LEPR (ΔG = -8.4 KJ/mol, Figure 6D).

Figure 6.

Figure 6

Open in a new tab

A. Association analysis of 3 biomarkers with other orthopedic diseases. Green, red, and blue indicate IGF1-R, IL-7R, and LEPR, respectively. B. Drug prediction for biomarkers. Orange labels refer to biomarkers, purple labels refer to predicted drugs. C. The molecular docking of Mecasermin with IGF1-R (ΔG = -9.5 KJ/mol). D. The molecular docking of Simvastatin with LEPR (ΔG = -8.4 KJ/mol).

Expression of IGF1-R, IL-7R, and LEPR in the meniscus of OA

In transcriptome sequencing data, IGF1R expression was up-regulated, while IL7R and LEPR expressions were down-regulated in Aging_meniscus (Figure 7A). The expression of all 3 biomarkers was verified in RT-qPCR (Figure 7B).

Figure 7.

Figure 7

Open in a new tab

A. Expression levels of biomarkers (IGF1-R, IL-7R, and LEPR) in meniscus with transcriptome sequencing data. B. RT-qPCR for validation of biomarkers expression in meniscus. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.001, ns: non-significance.

Expression of IGF1-R, IL-7R, and LEPR in the cartilage of OA

In Aging_cartilage, IGF1-R and LEPR expression was up-regulated and IL-7R expression was down-regulated (Figure 8A). In the validation set, IGF1-R was significantly highly expressed in OA, and IL7R was also lowly expressed in OA but the difference was not significant (Figure 8B). The expression of IGF1-R and LEPR exhibited a marked up-regulation in OA cartilage via RT-qPCR, aligning with the sequencing findings (Figure 8C). Although not statistically significant, IL-7R expression was also down-regulated in OA cartilage by RT-qPCR.

Figure 8.

Figure 8

Open in a new tab

A. Expression levels of biomarkers (IGF1-R, IL-7R, and LEPR) in cartilages with transcriptome sequencing data. B. Expression levels of biomarkers in GSE114007. C. RT-qPCR for validation of biomarkers expression in cartilages. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.001, ns: non-significance.

Discussion

OA is a chronic disease characterized by pain, cartilage loss, and joint inflammation, in which immune cells and aging play important roles in its progression. So far, there has been extensive research on individual immune cells or aging cells, such as immune cells (T and B cells, macrophages) involved in osteoclastogenesis, bone erosion, degradation and destruction of chondrocytes in OA [27,28]. TLR7, CSF1R, APOE, C1QA, and CCL5 are involved in immune response to promote the progression of OA [29]. Oxidative stress leads to chondrocyte aging by increasing the expression of p53 and p21 and activating the p38 MAPK and PI3K/Akt signaling pathways [30]. Mechanical stress can lead to chondrocyte aging and accelerate cartilage breakdown metabolism by downregulating FBXW7 [31]. However, there are no studies on genes that function in both immune cells and aging cells. Therefore, this study aims to identify potential aging and immune related biomarkers in OA, and explore the roles and mechanisms of aging and immunity in OA cartilage and meniscus tissue, thereby providing new directions and ideas for the treatment of OA.

In this study, three biomarkers related to aging and immunity were screened based on self sequencing data combined with bioinformatics, namely IGF1R, IL7R, and LEPR. Through GO and KEGG enrichment analysis, it was found that biomarkers mainly affect cell aging through energy metabolism. Hormones and their receptors play a role in immune, metabolic, and cellular signaling. The AMPK signaling pathway, FoxO signaling pathway, JAK-STAT signaling pathway, and PI3K-Akt signaling pathway affect the progression of OA, which is consistent with previous studies [32-35]. Some have suggested that aging and immune cells may be involved in inflammation regulation, cell metabolism, cell cycle regulation, transcriptional regulation, and other mechanisms that promote OA progression [35,36]. GSEA analysis in meniscus and cartilage shows that the aging and immune function of cartilage and meniscus cells are affected through chemokine signaling pathways, oxidative phosphorylation, RIG-1 like receptor signaling pathways, and fatty acid metabolism, thereby affecting their physiological function and health status.

By analyzing the disease correlation through the CTD database, it was found that all genes were screened to be associated with OA. IGF1-R is a member of the insulin-like growth factor 1 (IGF) signaling pathway and is crucial for cell growth and tissue differentiation [37]. This receptor mainly mediates the effects of IGF1 and IGF2, and IGF1-R is also believed to participate in the regulation of articular cartilage metabolism through IGF-1 binding [38]. Previous studies had shown that the expression of IGF1-R increased in OA, and the increased expression was positively correlated with the degree of cartilage lesions in OA [39,40]. In the cartilage and meniscus tissues of patients with OA, negative regulation is used to promote the expression of IGF1-R and inhibit the progression of osteoarthritis. In our experiment, we found through screening biomarkers and qPCR studies that IGF1-R is highly expressed in both cartilage and meniscus tissues of the bone and joint, making it a biomarker for OA. The IL-7R is heterodimeric, consistent of IL-7R α Chain (IL-7R α, CD127) and the common γ Chain [41]. In the progress of OA, there is significant controversy surrounding IL-7R [42,43]. The signal mediated by IL-7R promotes lymphatic development and immune balance in the body. Currently, extensive research has been conducted on cancer and autoimmune diseases [44,45]. We have innovatively discovered low expression in cartilage and meniscus tissues of OA. LEPR determines the role of leptin signaling [46]. In the presence of leptin, LEPR is overexpressed in human chondrocytes, increasing chondrocyte aging by activating mTOR [47]. Leptin can also induce the release of pro-inflammatory agents in cartilage, thereby accelerating the progression of OA [48], however, research has not yet been conducted in meniscus tissue, and our experiment has made up for this drawback. In the experiment, it was found that LEPR was upregulated in cartilage, which is consistent with previous studies. However, in meniscus tissue, LEPR expression was downregulated, which may be related to the differential expression of the same disease in different tissues.

As the human body ages and the innate and adaptive immune system activates, it plays a crucial role in various aspects of the pathogenesis of OA [49]. To investigate the molecular regulation of OA pathogenesis by biomarkers, we used ENCORI and miRWalk to predict upstream miRNAs of biomarkers, and obtained a total of 19 relationship pairs between biomarkers and miRNAs. At the same time, we used the JASPAR and ChEA databases of miRNet online tools to predict biomarkers and transcription factors, and obtained four pairs of relationships between biomarkers and transcription factors. We found that LEPR/STAT3 plays an important role in inflammation regulation [50], hormone regulation, and cell regeneration [51]. Finally, in this study, we identified two potential small molecule compounds, Mecasermin, which is a recombinant human insulin-like growth factor-1 (rh-IGF-1). Studies have shown that IGF-1 is considered the main synthetic metabolic factor in articular cartilage, capable of stimulating the synthesis of proteoglycans and collagen [52]. When IGF-1 expression is inhibited, it can promote the progression of OA [53]. Our study shows that Mecasermin can bind to IGF-1R, suggesting that the drug could work to treat OA by supplementing IGF-1 in combination with IGF-1R. Simvastatin is an effective hydroxymethylglutaryl CoA (HMG CoA) inhibitor commonly used as a cholesterol lowering drug for hypercholesterolemia and cardiovascular disease. It plays an important role in inhibiting chondrocyte aging [54], death, and catabolism [55], as well as the expression of interleukin and matrix metalloproteinase [56,57]. In the future, Mecasermin and Simvastatin may become important drugs for the treatment of OA.

Based on transcriptome sequencing data and using bioinformatics methods, this study obtained immune and aging biomarkers (IGF1-R, IL-7R, LEPR). These genes have good diagnostic and therapeutic effects on OA, laying a certain direction for the next basic research. The biological functions of these biomarkers were also explored, and disease-related drugs were predicted. Meanwhile, our research also has the following limitations: limited sample size has a potential impact on the accuracy of the results, and larger sample size and prospective study design are needed to validate and strengthen the model’s findings. The specific regulatory mechanisms and functions of biomarkers require further clinical research, but we will continue to pay attention to the mechanisms of action of genes related to OA diagnosis/treatment.

Acknowledgements

We thank the GEO project and other groups for providing invaluable datasets for statistical analyses. This study supported by National Natural Science Foundation of China (31970515), Yunnan Provincial Plan Project (202301AY070001-022) Support.

All patients signed an informed consent form to acquire and use discarded tissue.

Disclosure of conflict of interest

None.

Abbreviations

OA

osteoarthritis

IRGs

immune-related genes

ARGs

aging-related genes

DEGs

differentially expressed genes

DEIRGs

differentially expressed IRGs

DEARGs

differentially expressed ARGs

GSEA

gene set enrichment analysis

TF

transcription factor

NSAIDs

nonsteroidal anti-inflammatory drugs

HA

hyaluronic acid

GO

Gene Ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes

NCBI

National Center for Biotechnology Information

RT-qPCR

reverse transcription quantitative polymerase chain reaction

IGF1-R

insulin-like growth factor 1 receptor

IL-7R

interleukin 7 receptor

LEPR

leptin receptor

Supporting Information

ajcei0014-0204-f9.pdf (347.4KB, pdf)

References

  • 1.Kang D, Shin J, Cho Y, Kim HS, Gu YR, Kim H, You KT, Chang MJ, Chang CB, Kang SB, Kim JS, Kim VN, Kim JH. Stress-activated miR-204 governs senescent phenotypes of chondrocytes to promote osteoarthritis development. Sci Transl Med. 2019;11:eaar6659. doi: 10.1126/scitranslmed.aar6659. [DOI] [PubMed] [Google Scholar]
  • 2.Kim S, Han S, Kim Y, Kim HS, Gu YR, Kang D, Cho Y, Kim H, Lee J, Seo Y, Chang MJ, Chang CB, Kang SB, Kim JH. Tankyrase inhibition preserves osteoarthritic cartilage by coordinating cartilage matrix anabolism via effects on SOX9 PARylation. Nat Commun. 2019;10:4898. doi: 10.1038/s41467-019-12910-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Ying J, Wang P, Shi Z, Xu J, Ge Q, Sun Q, Wang W, Li J, Wu C, Tong P, Jin H. Inflammation-mediated aberrant glucose metabolism in subchondral bone induces osteoarthritis. Stem Cells. 2023;41:482–492. doi: 10.1093/stmcls/sxad012. [DOI] [PubMed] [Google Scholar]
  • 4.Katz JN, Arant KR, Loeser RF. Diagnosis and treatment of hip and knee osteoarthritis: a review. JAMA. 2021;325:568–578. doi: 10.1001/jama.2020.22171. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Kolasinski SL, Neogi T, Hochberg MC, Oatis C, Guyatt G, Block J, Callahan L, Copenhaver C, Dodge C, Felson D, Gellar K, Harvey WF, Hawker G, Herzig E, Kwoh CK, Nelson AE, Samuels J, Scanzello C, White D, Wise B, Altman RD, DiRenzo D, Fontanarosa J, Giradi G, Ishimori M, Misra D, Shah AA, Shmagel AK, Thoma LM, Turgunbaev M, Turner AS, Reston J. 2019 american college of rheumatology/arthritis foundation guideline for the management of osteoarthritis of the hand, hip, and knee. Arthritis Rheumatol. 2020;72:220–233. doi: 10.1002/art.41142. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Deyle GD, Allen CS, Allison SC, Gill NW, Hando BR, Petersen EJ, Dusenberry DI, Rhon DI. Physical therapy versus glucocorticoid injection for osteoarthritis of the knee. N Engl J Med. 2020;382:1420–1429. doi: 10.1056/NEJMoa1905877. [DOI] [PubMed] [Google Scholar]
  • 7.Ronconi G, Codazza S, Panunzio M, La Cagnina F, Ariani M, Gatto DM, Coraci D, Ferrara PE. The effects of ultrasound-guided intra-articular injections with hyaluronic acid and corticosteroids in patients with hip osteoarthritis: a long-term real-world analysis. J Clin Med. 2023;12:6600. doi: 10.3390/jcm12206600. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Enteshari-Moghaddam A, Azami A, Isazadehfar K, Mohebbi H, Habibzadeh A, Jahanpanah P. Efficacy of duloxetine and gabapentin in pain reduction in patients with knee osteoarthritis. Clin Rheumatol. 2019;38:2873–2880. doi: 10.1007/s10067-019-04573-7. [DOI] [PubMed] [Google Scholar]
  • 9.Fleischmann RM, Bliddal H, Blanco FJ, Schnitzer TJ, Peterfy C, Chen S, Wang L, Feng S, Conaghan PG, Berenbaum F, Pelletier JP, Martel-Pelletier J, Vaeterlein O, Kaeley GS, Liu W, Kosloski MP, Levy G, Zhang L, Medema JK, Levesque MC. A Phase II trial of lutikizumab, an anti-interleukin-1α/β dual variable domain immunoglobulin, in knee osteoarthritis patients with synovitis. Arthritis Rheumatol. 2019;71:1056–1069. doi: 10.1002/art.40840. [DOI] [PubMed] [Google Scholar]
  • 10.Evans JT, Walker RW, Evans JP, Blom AW, Sayers A, Whitehouse MR. How long does a knee replacement last? A systematic review and meta-analysis of case series and national registry reports with more than 15 years of follow-up. Lancet. 2019;393:655–663. doi: 10.1016/S0140-6736(18)32531-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Zhang L, Pitcher LE, Yousefzadeh MJ, Niedernhofer LJ, Robbins PD, Zhu Y. Cellular senescence: a key therapeutic target in aging and diseases. J Clin Invest. 2022;132:e158450. doi: 10.1172/JCI158450. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Zhu Y, Armstrong JL, Tchkonia T, Kirkland JL. Cellular senescence and the senescent secretory phenotype in age-related chronic diseases. Curr Opin Clin Nutr Metab Care. 2014;17:324–328. doi: 10.1097/MCO.0000000000000065. [DOI] [PubMed] [Google Scholar]
  • 13.Di Micco R, Krizhanovsky V, Baker D, d’Adda di Fagagna F. Cellular senescence in ageing: from mechanisms to therapeutic opportunities. Nat Rev Mol Cell Biol. 2021;22:75–95. doi: 10.1038/s41580-020-00314-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Xie J, Wang Y, Lu L, Liu L, Yu X, Pei F. Cellular senescence in knee osteoarthritis: molecular mechanisms and therapeutic implications. Ageing Res Rev. 2021;70:101413. doi: 10.1016/j.arr.2021.101413. [DOI] [PubMed] [Google Scholar]
  • 15.Motta F, Barone E, Sica A, Selmi C. Inflammaging and osteoarthritis. Clin Rev Allergy Immunol. 2023;64:222–238. doi: 10.1007/s12016-022-08941-1. [DOI] [PubMed] [Google Scholar]
  • 16.Nedunchezhiyan U, Varughese I, Sun AR, Wu X, Crawford R, Prasadam I. Obesity, inflammation, and immune system in osteoarthritis. Front Immunol. 2022;13:907750. doi: 10.3389/fimmu.2022.907750. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.van Bergen EDP, van Vulpen LFD, Schutgens REG, Mastbergen SC, Lafeber FPJG. Biochemical marker research in hemophilic arthropathy: a systematic review. Blood Rev. 2021;47:100781. doi: 10.1016/j.blre.2020.100781. [DOI] [PubMed] [Google Scholar]
  • 18.Wang X, Ning Y, Zhang P, Poulet B, Huang R, Gong Y, Hu M, Li C, Zhou R, Lammi MJ, Guo X. Comparison of the major cell populations among osteoarthritis, Kashin-Beck disease and healthy chondrocytes by single-cell RNA-seq analysis. Cell Death Dis. 2021;12:551. doi: 10.1038/s41419-021-03832-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Cheng Q, Chen X, Wu H, Du Y. Three hematologic/immune system-specific expressed genes are considered as the potential biomarkers for the diagnosis of early rheumatoid arthritis through bioinformatics analysis. J Transl Med. 2021;19:18. doi: 10.1186/s12967-020-02689-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Carneiro DC, Araújo LT, Santos GC, Damasceno PKF, Vieira JL, Santos RRD, Barbosa JDV, Soares MBP. Clinical trials with mesenchymal stem cell therapies for osteoarthritis: challenges in the regeneration of articular cartilage. Int J Mol Sci. 2023;24:9939. doi: 10.3390/ijms24129939. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Bradley PX, Thomas KN, Kratzer AL, Robinson AC, Wittstein JR, DeFrate LE, McNulty AL. The interplay of biomechanical and biological changes following meniscus injury. Curr Rheumatol Rep. 2023;25:35–46. doi: 10.1007/s11926-022-01093-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.He Q, Yang J, Jin Y. Immune infiltration and clinical significance analyses of the coagulation-related genes in hepatocellular carcinoma. Brief Bioinform. 2022;23:bbac291. doi: 10.1093/bib/bbac291. [DOI] [PubMed] [Google Scholar]
  • 23.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550. doi: 10.1186/s13059-014-0550-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics. 2012;16:284–287. doi: 10.1089/omi.2011.0118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Tang Q, Nie F, Kang J, Chen W. mRNALocater: enhance the prediction accuracy of eukaryotic mRNA subcellular localization by using model fusion strategy. Mol Ther. 2021;29:2617–2623. doi: 10.1016/j.ymthe.2021.04.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Su G, Morris JH, Demchak B, Bader GD. Biological network exploration with cytoscape 3. Curr Protoc Bioinformatics. 2014;47 doi: 10.1002/0471250953.bi0813s47. 8.13.11-24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Zhang H, Cai D, Bai X. Macrophages regulate the progression of osteoarthritis. Osteoarthritis Cartilage. 2020;28:555–561. doi: 10.1016/j.joca.2020.01.007. [DOI] [PubMed] [Google Scholar]
  • 28.Weber A, Chan PMB, Wen C. Do immune cells lead the way in subchondral bone disturbance in osteoarthritis? Prog Biophys Mol Biol. 2019;148:21–31. doi: 10.1016/j.pbiomolbio.2017.12.004. [DOI] [PubMed] [Google Scholar]
  • 29.Han Y, Wu J, Gong Z, Zhou Y, Li H, Wang B, Qian Q. Identification and development of a novel 5-gene diagnostic model based on immune infiltration analysis of osteoarthritis. J Transl Med. 2021;19:522. doi: 10.1186/s12967-021-03183-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Jeon OH, David N, Campisi J, Elisseeff JH. Senescent cells and osteoarthritis: a painful connection. J Clin Invest. 2018;128:1229–1237. doi: 10.1172/JCI95147. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Zhang H, Shao Y, Yao Z, Liu L, Zhang H, Yin J, Xie H, Li K, Lai P, Zeng H, Xiao G, Zeng C, Cai D, Bai X. Mechanical overloading promotes chondrocyte senescence and osteoarthritis development through downregulating FBXW7. Ann Rheum Dis. 2022;81:676–686. doi: 10.1136/annrheumdis-2021-221513. [DOI] [PubMed] [Google Scholar]
  • 32.Sun C, Peng S, Lv Z, Guo T, Zhang L. Research of STEAP3 interaction with Rab7A and RACK1 to modulate the MAPK and JAK/STAT signaling in osteoarthritis. Int Immunopharmacol. 2023;124:111034. doi: 10.1016/j.intimp.2023.111034. [DOI] [PubMed] [Google Scholar]
  • 33.Lee KI, Choi S, Matsuzaki T, Alvarez-Garcia O, Olmer M, Grogan SP, D’Lima DD, Lotz MK. FOXO1 and FOXO3 transcription factors have unique functions in meniscus development and homeostasis during aging and osteoarthritis. Proc Natl Acad Sci U S A. 2020;117:3135–3143. doi: 10.1073/pnas.1918673117. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Li J, Zhang B, Liu WX, Lu K, Pan H, Wang T, Oh CD, Yi D, Huang J, Zhao L, Ning G, Xing C, Xiao G, Liu-Bryan R, Feng S, Chen D. Metformin limits osteoarthritis development and progression through activation of AMPK signalling. Ann Rheum Dis. 2020;79:635–645. doi: 10.1136/annrheumdis-2019-216713. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Zhao C, Li X, Sun G, Liu P, Kong K, Chen X, Yang F, Wang X. CircFOXO3 protects against osteoarthritis by targeting its parental gene FOXO3 and activating PI3K/AKT-mediated autophagy. Cell Death Dis. 2022;13:932. doi: 10.1038/s41419-022-05390-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Childs BG, Gluscevic M, Baker DJ, Laberge RM, Marquess D, Dananberg J, van Deursen JM. Senescent cells: an emerging target for diseases of ageing. Nat Rev Drug Discov. 2017;16:718–735. doi: 10.1038/nrd.2017.116. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Brown J, Delaine C, Zaccheo OJ, Siebold C, Gilbert RJ, van Boxel G, Denley A, Wallace JC, Hassan AB, Forbes BE, Jones EY. Structure and functional analysis of the IGF-II/IGF2R interaction. EMBO J. 2008;27:265–276. doi: 10.1038/sj.emboj.7601938. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Shakibaei M, Seifarth C, John T, Rahmanzadeh M, Mobasheri A. Igf-I extends the chondrogenic potential of human articular chondrocytes in vitro: molecular association between Sox9 and Erk1/2. Biochem Pharmacol. 2006;72:1382–1395. doi: 10.1016/j.bcp.2006.08.022. [DOI] [PubMed] [Google Scholar]
  • 39.Iwanaga H, Matsumoto T, Enomoto H, Okano K, Hishikawa Y, Shindo H, Koji T. Enhanced expression of insulin-like growth factor-binding proteins in human osteoarthritic cartilage detected by immunohistochemistry and in situ hybridization. Osteoarthritis Cartilage. 2005;13:439–448. doi: 10.1016/j.joca.2004.12.006. [DOI] [PubMed] [Google Scholar]
  • 40.Wang J, Verdonk P, Elewaut D, Veys EM, Verbruggen G. Homeostasis of the extracellular matrix of normal and osteoarthritic human articular cartilage chondrocytes in vitro. Osteoarthritis Cartilage. 2003;11:801–809. doi: 10.1016/s1063-4584(03)00168-7. [DOI] [PubMed] [Google Scholar]
  • 41.Kjer-Hansen P, Weatheritt RJ. The function of alternative splicing in the proteome: rewiring protein interactomes to put old functions into new contexts. Nat Struct Mol Biol. 2023;30:1844–1856. doi: 10.1038/s41594-023-01155-9. [DOI] [PubMed] [Google Scholar]
  • 42.Meyer A, Parmar PJ, Shahrara S. Significance of IL-7 and IL-7R in RA and autoimmunity. Autoimmun Rev. 2022;21:103120. doi: 10.1016/j.autrev.2022.103120. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Hartgring SA, van Roon JA, Wenting-van Wijk M, Jacobs KM, Jahangier ZN, Willis CR, Bijlsma JW, Lafeber FP. Elevated expression of interleukin-7 receptor in inflamed joints mediates interleukin-7-induced immune activation in rheumatoid arthritis. Arthritis Rheum. 2009;60:2595–2605. doi: 10.1002/art.24754. [DOI] [PubMed] [Google Scholar]
  • 44.González-García S, Mosquera M, Fuentes P, Palumbo T, Escudero A, Pérez-Martínez A, Ramírez M, Corcoran AE, Toribio ML. IL-7R is essential for leukemia-initiating cell activity of T-cell acute lymphoblastic leukemia. Blood. 2019;134:2171–2182. doi: 10.1182/blood.2019000982. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Yasunaga M. Antibody therapeutics and immunoregulation in cancer and autoimmune disease. Semin Cancer Biol. 2020;64:1–12. doi: 10.1016/j.semcancer.2019.06.001. [DOI] [PubMed] [Google Scholar]
  • 46.Cordero-Barreal A, González-Rodríguez M, Ruiz-Fernández C, Eldjoudi DA, AbdElHafez YRF, Lago F, Conde J, Gómez R, González-Gay MA, Mobasheri A, Pino J, Gualillo O. An update on the role of leptin in the immuno-metabolism of cartilage. Int J Mol Sci. 2021;22:2411. doi: 10.3390/ijms22052411. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Zhao X, Huang P, Li G, Lv Z, Hu G, Xu Q. Activation of the leptin pathway by high expression of the long form of the leptin receptor (Ob-Rb) accelerates chondrocyte senescence in osteoarthritis. Bone Joint Res. 2019;8:425–436. doi: 10.1302/2046-3758.89.BJR-2018-0325.R2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Vuolteenaho K, Koskinen A, Kukkonen M, Nieminen R, Päivärinta U, Moilanen T, Moilanen E. Leptin enhances synthesis of proinflammatory mediators in human osteoarthritic cartilage--mediator role of NO in leptin-induced PGE2, IL-6, and IL-8 production. Mediators Inflamm. 2009;2009:345838. doi: 10.1155/2009/345838. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Zhou J, Huang J, Li Z, Song Q, Yang Z, Wang L, Meng Q. Identification of aging-related biomarkers and immune infiltration characteristics in osteoarthritis based on bioinformatics analysis and machine learning. Front Immunol. 2023;14:1168780. doi: 10.3389/fimmu.2023.1168780. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Gove ME, Rhodes DH, Pini M, van Baal JW, Sennello JA, Fayad R, Cabay RJ, Myers MG Jr, Fantuzzi G. Role of leptin receptor-induced STAT3 signaling in modulation of intestinal and hepatic inflammation in mice. J Leukoc Biol. 2009;85:491–496. doi: 10.1189/jlb.0808508. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Bodary PF, Shen Y, Ohman M, Bahrou KL, Vargas FB, Cudney SS, Wickenheiser KJ, Myers MG Jr, Eitzman DT. Leptin regulates neointima formation after arterial injury through mechanisms independent of blood pressure and the leptin receptor/STAT3 signaling pathways involved in energy balance. Arterioscler Thromb Vasc Biol. 2007;27:70–76. doi: 10.1161/01.ATV.0000252068.89775.ee. [DOI] [PubMed] [Google Scholar]
  • 52.Zhang M, Wang W, Wang H, Liu Y, Li Z, Yi C, Shi Y, Ma T, Chen J. Downregulation of insulin-like growth factor-1 receptor mediates chondrocyte death and matrix degradation in kashin-beck disease. Cartilage. 2021;13:809S–817S. doi: 10.1177/19476035211021890. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Collins JA, Kim CJ, Coleman A, Little A, Perez MM, Clarke EJ, Diekman B, Peffers MJ, Chubinskaya S, Tomlinson RE, Freeman TA, Loeser RF. Cartilage-specific Sirt6 deficiency represses IGF-1 and enhances osteoarthritis severity in mice. Ann Rheum Dis. 2023;82:1464–1473. doi: 10.1136/ard-2023-224385. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Yudoh K, Karasawa R. Statin prevents chondrocyte aging and degeneration of articular cartilage in osteoarthritis (OA) Aging (Albany NY) 2010;2:990–998. doi: 10.18632/aging.100213. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Riegger J, Maurer S, Pulasani S, Brenner RE. Simvastatin and fluvastatin attenuate trauma-induced cell death and catabolism in human cartilage. Front Bioeng Biotechnol. 2022;10:965302. doi: 10.3389/fbioe.2022.965302. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Lazzerini PE, Capecchi PL, Nerucci F, Fioravanti A, Chellini F, Piccini M, Bisogno S, Marcolongo R, Laghi Pasini F. Simvastatin reduces MMP-3 level in interleukin 1beta stimulated human chondrocyte culture. Ann Rheum Dis. 2004;63:867–869. doi: 10.1136/ard.2003.009746. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Yu SM, Kim SJ. Simvastatin prevents articular chondrocyte dedifferentiation induced by nitric oxide by inhibiting the expression of matrix metalloproteinases 1 and 13. Exp Biol Med (Maywood) 2018;243:1165–1172. doi: 10.1177/1535370218820650. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

ajcei0014-0204-f9.pdf (347.4KB, pdf)

Articles from American Journal of Clinical and Experimental Immunology are provided here courtesy of e-Century Publishing Corporation

RESOURCES