Inflammatory Molecular Networks in Age-Related Meniscus Injury: TLR/NF-κB Signaling, Immune Dysregulation, and Epigenetic Modifications

Inflammatory Molecular Networks in Age-Related Meniscus Injury: TLR/NF-κB Signaling, Immune Dysregulation, and Epigenetic Modifications | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Inflammatory Molecular Networks in Age-Related Meniscus Injury: TLR/NF-κB Signaling, Immune Dysregulation, and Epigenetic Modifications Shunjie Yang, Hui Wang, Fangang Fu, Xiaohe Tian, Li Liu, Peng Xu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6559896/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: The meniscus plays a crucial biomechanical role in the knee joint, and its injury often leads to degenerative joint diseases. Aging significantly increases the risk of meniscus injury, with emerging evidence suggesting dysregulated inflammatory responses as a key factor. This study investigated the inflammatory molecular mechanisms underlying age-related meniscus injury and identified potential therapeutic targets. Methods: The transcriptomic data of injured meniscus tissues from 4 young and 4 aging patients were obtained from the GEO database. Differentially expressed genes (DEGs) were analyzed and intersected with inflammation-related genes in MsigDB. Functional enrichment (GO/KEGG), protein-protein interaction (PPI) network analysis, and hub gene identification were performed. ROC curve was used to evaluate the diagnostic efficacy. We evaluated the correlations among hub genes, immune cell infiltration, RNA methylation regulators (m6A/m5C), and regulatory networks (miRNAs/TFs). Meanwhile, the expression of hub genes in the meniscus injury tissue between aging and young patients was verified by qRT-PCR and immumohistochemical staining. Results: A total of 1009 DEGs (755 upregulated and 254 downregulated) were identified in aging meniscus, with 13 inflammation-related DEGs enriched in transcription factor regulation, osteoclast differentiation, and T-helper cell pathways. Six hub genes ( IFNGR2, NFKBIA, CCL22, TLR3, PLAUR, ITGA5 ) were identified, with NFKBIA, CCL22 , and TLR3 showing high diagnostic accuracy (AUC > 0.95). In the validation samples, CCL22, NFKBIA and TLR3 were upregulated and IFNGR2, ITGA5 and PLAUR were downregulated in aging meniscus, consistent with the transcriptomic data. TLR3 strongly correlated with other hub genes ( NFKBIA : R = 0.81; IFNGR2 : R=-0.9). Immune cell analysis showed a decrease in activated B/T cells and an increase in dendritic cells in aging samples. NFKBIA and PLAUR were correlated with plasmacytoid dendritic cells, while CCL22 and TLR3 were negatively associated with Th1 cells. Hub genes also showed strong links with m6A/m5C regulators ( YTHDC2, UHRF2, NSUN3 ). Regulatory network analysis implicated hsa-let-7b-5p, NFKB1 , and RELA in modulating hub genes. Conclusion: Age-related meniscus injury involves inflammatory pathways, immune dysregulation, and epigenetic modifications. Hub genes ( CCL22, NFKBIA, TLR3 ) and associated regulators (e.g., hsa-let-7b-5p, YTHDC2 ) may serve as diagnostic markers or therapeutic targets for age-related meniscus injury. Biological sciences/Genetics Biological sciences/Immunology Biological sciences/Molecular biology Health sciences/Biomarkers Health sciences/Diseases Meniscus injury Aging Inflammation NFKBIA TLR3 CCL22 Immune infiltration RNA methylation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction The meniscus is a vital fibrocartilaginous structure that maintains knee homeostasis by distributing load, absorbing impact, and providing stability [ 1 ]. Meniscus injury is one of the most common orthopedic disorders, often leading to progressive joint degeneration and osteoarthritis, especially in aging populations [ 2 ]. Epidemiological studies have shown that the prevalence of meniscus tears increases significantly with age, with degenerative tears predominating in those over 40 years of age [ 3 , 4 ]. Acute traumatic tears in young patients often have reparative potential, whereas age-related meniscus injury tends to follow a chronic, degenerative process with poor healing [ 5 ]. This disparity suggests that biological aging fundamentally alters the meniscus microenvironment, but the underlying molecular mechanisms remain poorly understood. Emerging evidence implicates that the chronic low-grade inflammation known as "inflammaging" is a key driver of age-related tissue degeneration [ 6 ]. In the meniscus, aging is associated with increased expression of pro-inflammatory cytokine and matrix-degrading enzymes [ 7 ]. Transcriptomic analyses further revealed that aging altered gene signatures related to immune activation, extracellular matrix (ECM) remodeling, and cellular senescence in meniscal tissues [ 8 ]. However, there is a lack of systematic studies on the inflammatory network specific to age-related meniscus injury, especially its interaction with immune cell infiltration and epigenetic regulation. Recent studies have highlighted the role of RNA methylation, N 6 -methyladenosine (m 6 A) and 5-methylcytosine (m 5 C) in regulating inflammatory responses and tissue aging [ 9 , 10 ]. Modifications such as m 6 A and m 5 C affect mRNA stability and translation of inflammatory mediators [ 11 – 13 ], but their role in meniscus degeneration is unknown. Furthermore, while specific miRNAs are known to regulate inflammation and fibrosis [ 14 , 15 ], their networks in aged meniscus remains undefined. This study aims to explore the key inflammatory drivers of meniscus injury in aging patients through bioinformatics and experimental validation. By integrating the immune infiltration, epigenetics, and miRNA-transcription factor regulatory networks, the inflammatory axis of meniscus aging is revealed, which provides a basis for targeted interventions to protect joint health in aging population. 2. Materials and methods 2.1 Data source Microarray expression data for meniscus injury were downloaded from the Gene Expression Omnibus (GEO) database. There were 8 samples in the GSE191157 dataset, including 4 young patients with meniscus injury aged 20–25 years and 4 aging patients with meniscus injury aged 50–55 years. Inflammatory response-associated genes were obtained from the hallmark gene sets in the Molecular Signature Database (MsigDB). The flowchart of this study was shown in Fig. 1 . 2.2 Acquisition of differentially expressed genes (DEGs) The meniscus injury samples in GEO database were divided into two groups: young and aging. All the microarray data after normalization were analyzed by R software. R package “limma” was used to identify DEGs between the two groups, with a threshold of p.adjust 0.5. Heatmap cluster and volcano plot of the DEGs were created using the “pheatmap” and “ggplots” packages. 2.3 Functional enrichment analysis of inflammation-related DEGs The tissue-specific expression of DEGs was analyzed according to the BioGPS database and literatures. Inflammation-related DEGs were obtained by overlapping DEGs and inflammation-related genes. To reveal the functions of DEGs Inflammation-related DEGs, Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed using the “clusterProfiler” package. GO terms include biological process (BP), cellular component (CC) and molecular function (MF). KEGG pathway enrichment analysis tended to describe gene functions at the genomic and molecular levels. P.adjust < 0.05 after BH correction was considered statistically significant. The KEGG analysis results were further analyzed by network analysis, and the key pathways were identified according to the weighted degree calculated by the shared genes of each pathway. 2.4 Constructing protein–protein interaction (PPI) network The PPI network was constructed from STRING database, with a threshold of medium confidence ≥ 0.2. Cytoscape software was used to visualize the network, and then the Cytoscape plugin-MCODE to screen important modules in the PPI network as hub genes. 2.5 Validating the expression of hub genes The meniscus tissue of 12 patients with meniscus injury who underwent arthroscopic meniscectomy were collected from sports medicine center, West China Hospital of Sichuan University, including 6 young patients aged 14–25 years and 6 aging patients aged 50–60 years. Informed consent was obtained from all participants, and this study was approved by the ethics committee of West China Hospital of Sichuan University. Excised meniscus tissue (0.5 cm 3 blocks) was taken and immediately snap frozen in liquid nitrogen for Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) or fixed in 4% paraformaldehyde for 24 h for immunohistochemistry (IHC). The qRT-PCR and IHC methods were similar to those of the previous study [ 16 ]. Primers and antibodies used in this study were shown in Supplementary Table S1 -2. Gene quantification was expressed as the relative CT value (2 −△△Ct ), normalized to GAPDH levels. IHC stained slides were scanned at high power magnification (400×) using the Motic EasyScan Pro 6 (Motic Digital Pathology, Barcelona, Spain) and exported as JPEG files (Motic DSAssistant). The IHC signal was quantified as an area of positive cells using Image-Pro Plus 6 (Media Cybernetics, Bethesda, MD, USA). For each slide, integrated optical density (IOD) were recorded from three different images and calculated the mean IOD. IOD was computed as pixel area × mean intensity. Each experiment repeated in three times. Data were analyzed by SPSS 23.0 and expressed as mean ± standard deviation. Student's t-test was used for comparison between groups. P value < 0.05 was considered statistically significant. 2.6 Evaluation of diagnostic performance of hub genes Receiver operating characteristic (ROC) curve analysis was performed using ROC software package to evaluate the diagnostic value of hub genes for meniscus inflammation in aging and young patients. The area under the curve (AUC) obtained provide the rationale and theoretical basis for distinguishing the diagnostic specificity and sensitivity of hub genes. 2.7 Correlation and functional similarity analysis among hub genes Spearman correlation analysis of hub genes was performed using the "ggpub" software package. P.adjust < 0.05 after BH correction was considered statistically significant. The geometric mean of CC, BP, and MF semantic similarity was used to assess the functional similarity between proteins through the GOSemSim package. Functional similarity is a measure to evaluate the strength of the relationship between each protein and its partners by the function and location of the protein. 2.8 Correlation of hub genes with immune-infiltration cells Cibersort and Single-sample Gene Set Enrichment Analysis (ssGSEA) were used to analyze the proportion and expression difference of infiltrating immune cells, and the “ggpub” software package was used to analyze the spearman correlation between the proportion and expression of hub genes and infiltrating immune cells. P.adjust < 0.05 after BH correction was considered statistically significant. 2.9 Correlation of hub genes with m6A and m5C regulators The ggplot2 software package was used to evaluate the correlations of hub genes with 28 m6A modifiers and 29 m5C regulators, and the specific molecules analyzed were shown in Fig. 8 A-B. P.adjust < 0.05 after BH correction was considered statistically significant. 2.10 Construction of hub genes regulatory network Transcription factors (TFs) and microRNAs (miRNAs) of hub genes were predicted by miRNet databases and then integrated into the regulatory network and visualized by Cytoscape software. 3. Results 3.1 DEGs and functional analysis Based on the GSE database, we first analyzed DEGs in meniscus injury between the young group and the aging group. The analysis revealed a total of 1014 DEGs, of which 783 were up-regulated and 231 were down-regulated, as shown in the generated volcano plot (Fig. 2 A). The heat map showed the top 15 up-regulated and top 15 down-regulated DEGs with the greatest fold change (Fig. 2 B). DEGs was mainly specifically expressed in immune-related tissues or cells, including 721 B lymphoblasts, CD33 + Myeloid, CD8 + Tcells and CD4 + T Cell (Supplementary Table S3). 3.2 Inflammation-related DEGs and functional analysis After intersecting the inflammation-related genes with DEGs, thirteen inflammation-related DEGs were obtained (Fig. 3 A), including IFNGR2, TLR3, INHBA, CCL22, PLAUR, BTG2, NFKBIA, GCH1, SRI, PDE4B, ITGA5, GPR132 and SELENOS (Supplementary Table S4). GO annotate showed that these inflammation-related DEGs were highly enriched in cytoplasmic sequestering of transcription factor, protein complex involved in cell adhesion, and calcium channel regulator activity (Fig. 3 B). KEGG analysis showed that osteoclast differentiation, type 1 T helper cell (Th1), type 2 T helper cell and type 17 T helper cell differentiation were the top three key pathways, all with a weight degree of 29, and the common shared genes were IFNGR2 and NFKBIA (Fig. 3 C-D, Supplementary Table S5). 3.3 Obtaining hub genes by PPI network construction To further investigate the interactions of thirteen inflammation-related DEGs, we constructed a PPI network with thirteen nodes and twenty edges from the STRING database (Fig. 4 A). The most significant module consisting of six genes and twelve edges was found using the MCODE plug-in of Cytoscape software. Six genes in the key modules, IFNGR2, NFKBIA, CCL22, TLR3, PLAUR , and ITGA5 , were selected as hub genes (Fig. 4 B). 3.4 Validating the expression of hub genes Meniscus tissues were collected from 12 young and aging patients with meniscus injury, and the expressions of hub genes were verified by qRT-PCR and IHC. It was found that the mRNA expression of CCL22, NFKBIA and TLR3 was up-regulated, and that of IFNGR2, ITGA5 and PLAUR were down-regulated in the aging group compared with the young group (p < 0.05 for all), which was consistent with GEO transcriptome data (Fig. 4 C). The differences in protein expression of these six hub genes between the two groups were statistically significant (p < 0.05 for all), and the difference trend was consistent with that of mRNA expression (Fig. 5 ). 3.5 Evaluating the diagnostic performance and functional similarity of hub genes The AUC of NFKBIA, CCL22 and TLR3 was larger than 0.95, and those of IFNGR2, PLAUR and ITGA5 was close to zero (Fig. 6 A). The results of spearman correlation analysis of hub genes are shown in Fig. 6 B. TLR3 was significantly correlated with other five hub genes, among which IFNGR2 had the highest negative correlation (R=-0.9, p.adjust < 0.05) and NFKBIA had the highest positive correlation (R = 0.81, p.adjust < 0.05). The correlation coefficients of TLR3 with CCL22, ITGA5 and PLAUR were 0.76, -0.76 and − 0.76, respectively (p.adjust < 0.05 for all). NFKBIA was also significantly correlated with ITGA5 (R=-0.81, p.adjust < 0.05) and PLAUR (R=-0.98, p.adjust < 0.001). ITGA5 was also significantly positively correlated with IFNGR2 (R = 0.79, p.adjust < 0.05) and PLAUR (R = 0.88, p.adjust < 0.05). CCL22 was only positively correlated with TLR3 . In addition, we ranked hub genes based on the average functional similarity relationships between proteins in the interactome. The results showed that these hub genes were not similar in BP but similar in MF and CC. Overall, IFNGR2, NFKBIA and TLR3 were similar in gene function (Fig. 6 C). 3.6 Correlation of hub genes with immune-infiltration cells In terms of the proportion of immune cells classified by cibersort, there was no significant difference between the aging group and the young group (Fig. 7 A-B), and there was no significant correlation between hub genes and the proportion of immune cells (Fig. 7 C). In terms of the expression of immune cells analyzed by ssGSEA, activated B cell, activated CD4 T cell, central memory CD8 T cell, regulatory T cell (Treg) and Th1 were decreased, while immature dendritic cell and plasmacytoid dendritic cell were increased in the aging group compared with the young group (p < 0.05 for all) (Fig. 8 A). Correlation analysis between key genes and immune cells expression showed that CCL22 was negatively correlated with activated CD4 T cell (R=-0.98, p.adjust < 0.01), immature dendritic cell (R = 0.90, p.adjust < 0.05), and regulatory T cell (R=-0.88, p.adjust < 0.05). TLR3 and CCL22 were negatively correlated with type 1 T helper cell (R=-0.88, p.adjust < 0.05). ITGA5 was positively correlated with central memory CD8 T cell (R = 0.90, p.adjust < 0.05 for all). NFKBIA (R = 0.93, p.adjust < 0.05) and PLAUR (R=-0.9, p.adjust < 0.05) were associated with plasmacytoid dendritic cell (Fig. 8 B). 3.7 Correlation of hub genes with m6A and m5C regulators Accumulating evidence suggests that RNA methylation plays an important role in osteogenesis and inflammation. Therefore, we sought to analyze the interactive effects of m6A and 5mC regulators on the expression of hub genes in meniscus injury. For m6A regulators, HNRNPC, YTHDC1, YTHDC2, METTL14 , and VIRMA were up-regulated, while IGF2BP2, YTHDF1 , and CBLL1 were down-regulated in the aged group compared with the young group (p < 0.05 for all) (Fig. 9 A). Analysis of the correlation between hub genes and m6A modifier expression (Fig. 9 C) showed that NFKBIA had the strongest positive correlation with YTHDC2 (R = 0.95, p.adjust < 0.05). TLR3 was positively correlated with HNPRNPC (R = 0.93, p.adjust < 0.01). For m5C regulators, NEIL1, TDG, UHRF2, NSUN3 , and NSUN6 were up-regulated, while YBX1, UHRF1 , and DNMT3A were down-regulated in the aged group compared with the young group (p < 0.05 for all) (Fig. 9 B). Analysis of the correlation between hub genes and m5C modifier expression (Fig. 9 D) shown that NFKBIA had the strongest positive correlation with UHRF2 (R = 0.98, p.adjust < 0.01) and NSUN3 (R = 0.93, p.adjust < 0.01). NSUN3 was positively correlated with TLR3 (R = 0.90, p.adjust < 0.05). UHRF2 was negatively correlated with PLARU (R=-0.93, p.adjust < 0.05). For the correlations between the expression of m6A and m5C regulators (Fig. 9 E), YTHDC2 showed the strongest positive correlation with UHRF2 (R = 0.98, p.adjust < 0.01) and NSUN3 (R = 0.95, p.adjust < 0.05). HNPRNPC was also positively correlated with UHRF2 (R = 0.95, p.adjust < 0.05) and NSUN3 (R = 0.98, p.adjust < 0.01). 3.8 Constructing the miRNAs-genes-TFs regulatory networks of hub genes The miRNAs-genes-TFs networks consisting of 6 hub genes, 407 miRNAs and 24 TFs was constructed using miRNet database and Cytoscape software (Fig. 10 A). One miRNA, hsa-let-7b-5p, acted on all hub genes, and thirteen miRNAs on five common hub genes were screened (Fig. 10 B). Since no transcription factor acts on all hub genes, the transcription factor most relevant to the common hub gene was selected as the ideal target. Two transcription factors that act on four common core genes were screened, including NFKB1 and RELA, both of which act on CCL22, NFKBIA, PLAUR, and TLR3 (Fig. 10 B). 4. Discussion Age-related meniscus injury is an important cause of knee joint degeneration, but its inflammatory mechanism is still unclear. In this study, bioinformatics and experimental validation were used to explore the inflammatory molecular mechanisms of age-related meniscus injury. Our findings highlight the critical roles of TLR3/NF-κB signaling, immune dysregulation, and epigenetic modifications (m6A/m5C) in driving age-related meniscus injury (Fig. 11 ). 4.1 TLR3/NF-κB signaling as a central driver of inflammation and immune dysregulation in the age-related meniscus injury We identified six hub genes ( IFNGR2, NFKBIA, CCL22, TLR3, PLAUR ,and ITGA5 ) that played pivotal roles in age-related meniscus injury. Among them, NFKBIA, TLR3 , and CCL22 had high diagnostic value (AUC > 0.95), suggesting their potential as biomarkers for age-related meniscus degeneration. NF-κB is central to the immune response, and its dysregulation is associated with chronic inflammation and osteoarthritis [ 17 ]. NFKBIA limits inflammation by inhibiting the NF-κB signaling pathway. In our study, NFKBIA was upregulated in aged meniscus injury, consistent with prior studies linking abnormally prolonged NF-κB activation to negative feedback dysregulation [ 18 ]. In chronic inflammation, IκBα and A20, negative feedback of NF-κB regulators was much less synthesized because a large fraction is uncapped, unspliced, and retained in the nucleus [ 18 ]. Thus, the anti-inflammatory effects of NFKBIA may be "submerged" and manifest as persistent inflammation. TLR3 , a viral RNA sensor, is increasingly recognized for its role in aseptic inflammation of aging and osteoarthritis [ 19 ]. TLR3 showed a general correlation with all other hub genes. The strong positive correlation between TLR3 and NFKBIA indicates that Toll-like receptor signaling may exacerbate the inflammation of aged-related meniscus injury through NF-κB, consistent with studies linking TLR3 to cartilage degradation by NF-κB-mediated MMPs upregulatoin [ 20 ]. Notably, CCL22 , a chemokine involved in T-cell recruitment [ 21 – 23 ], was significantly upregulated in age-related meniscus injury, positive correlated only with TLR3, negatively correlated with Treg and Th1 cells, and positively correlated with immature dendritic cells. This aligns with evidence that aging disrupts immune homeostasis and promotes a pro-inflammatory microenvironment [ 24 ]. IFNGR2 , an interferon gamma receptor, is an important anti-inflammatory factor. The downregulation of IFNGR2 and the strong negative correlation with TLR3 further suggest impaired anti-inflammatory signaling and potentially exacerbating tissue damage. This may be a feedback inhibitory effect of persistent inflammation. 4.2 CCL22-related immune microenvironment changes in aging meniscus Immune infiltration analysis showed reduced activated B cells, activated CD4 + T cells, Treg and Th1 cells, alongside increased immature and plasmacytoid dendritic cells in age-related meniscus injury. This is consistent with the "immunosenescence" phenotype observed in osteoarthritis [ 24 , 25 ]. Upregulation of CCL22/TLR3 is consistent with their proinflammatory roles in osteoarthritis. The expression of CCL22/CCR4 is increased in osteoarthritic cartilage injury and is involved in the inflammation and cartilage degradation of chondrocytes. CCL22 silencing can alleviate the cartilage degradation of mouse chondrocytes through CCR4 [ 26 ]. Moreover, the symptoms and progression of osteoarthritis were effectively alleviated and controlled when CCL22 expression was inhibited [ 27 ]. CCL22 was negatively correlated with downregulated Treg and Th1 cells, and positively correlated with upregulated immature dendritic cell. CCL22 can promote the migration of CCR4 + DCs to the injury site, and CCL22-induced DCs can promote the proliferation of Treg [ 22 ]. In addition, CCL22 production and its attraction to Treg are regulated by TLR3/NFκB [ 28 – 30 ]. There is a strong positive correlation between CCL22 and TLR3, and a negative correlation between CCL22/TLR3 and Th1 cells, suggesting that chronic inflammation of the aging meniscus may inhibit TLR3/NF-κB-mediated protective t cell response and accelerate meniscus degeneration. In addition, CCL22 is elevated in human ostoarthritis cartilage and induces apoptosis in isolated human chondrocytes, which may be an early factor in the initiation of cartilage degeneration [ 31 ]. CCL22 is selectively upregulated in osteoclast-like cells [ 32 ], which may be involved in osteoclast differentiation and bone degeneration. 4.3 ITGA5, PLAUR and ECM degradation ITGA5 (integrin α5) pairs with β1 integrin to form α5β1, which in turn binds to fibronectin and mediates cell-ECM adhesion, crucial for the stability of meniscus structure [ 33 ]. This process may be related to NF-κB signaling pathway, which is a downstream molecule of integrin-α5β1 [ 34 ]. PLAUR binds urokinase plasminogen activator converts plasminogen to plasmin, which in turn activates MMPs and leads to ECM breakdown. PLAUR transcription depends on NF-κB/RELA [ 35 – 37 ]. ITGA5 and PLAUR were downregulated in aged samples. The former was positively correlated with central memory CD8 + T cells, while the latter was negatively correlated with plasmacytoid dendritic cell. This may be related to the alteration of immune cell migration caused by ECM degradation. Downregulation o f ITGA5 in aging meniscus tissue may lead to weakened cell-ECM adhesion and impaired fibrochondrocyte anchoring, thereby reducing CD8 + T cells migration [ 38 ]. Conversely, reduced PLAUR may inhibit fibrinolysis, which may increase plasmacytoid dendritic cell migration [ 39 ]. It is speculated that IGA5 and PLAUR-related ECM degradation is involved in the changes of immune microenvironment of the aging meniscus, which may be mediated by TLR/NF-κBa/CCL22 signaling pathway. 4.4 Epigenetic regulation through RNA methylation A novel aspect of this study was to explore the regulators of RNA methylation (m6A/m5C) in meniscus aging. We found that NFKBIA and TLR3 were strongly correlated with m6A/m5C writers ( YTHDC2, UHRF2, NSUN3 ), which were upregulated in aging meniscus. This suggests that post-transcriptional RNA modifications may stabilize proinflammatory transcripts and perpetuate inflammation. YTHDC2, an m6A reader, has been reported to be associated with NF-κB activation in oxidative stress [ 40 , 41 ], supporting our findings. The tight correlation among YTHDC2 , UHRF2 , and NSUN3 further supports their synergistic role in age-related epigenetic dysregulation. 4.4 miRNAs-TFs regulatory network The miRNAs-TFs regulatory network revealed that hsa-let-7b-5p targets all hub genes, which is consistent with its known role in suppressing inflammation [ 42 – 44 ] and fibrosis [ 45 , 46 ]. Reportedly, hsa-let-7b-5p and hsa-miR-19b-3p are correlated in irritable bowel syndrome, and hsa-miR-19b-3p may regulate the production of NF-κB through the the PI3K/Akt pathway [ 43 ]. Whether the function of hsa-let-7b-5p in age-related meniscus injury is associated with the TLR/NF-κB pathway needs to be further validated. In addition, RELA and NFKB1, components of the NF-κB complex, can regulate multiple hub genes ( CCL22, NFKBIA, TLR3, PLAUR ), thereby reinforcing the central role of NF-κB in meniscal degeneration. This may be a key mechanism for the positive feedback amplification of NF-κB signaling, thus contributing to the persistence of inflammation in aging patients [ 47 ]. Conclusion Our study unveils the multifaceted interplay of inflammation, immunity, and epigenetics in age-related meniscus injury. TLR3/NF-κB/CCL22 and m6A/m5C networks are key regulators. Future studies will focus on validation in TLR3 knockout models or m6A editing experiments to establish mechanistic links and translate these findings into therapeutics targeting TLR3/NF-κB/CCL22 or m6A modifications. Abbreviations AUC: Area Under the Curve; BP: Biological Process; CC: Cellular Component; DCs: Dendritic Cells; DEGs: Differentially Expressed Genes; DSIF: DRB Sensitivity-Inducing Factor; ECM: Extracellular Matrix; GEO: Gene Expression Omnibus; GO: Gene Ontology; IHC: Immunohistochemistry; IOD: Integrated Optical Density; KEGG: Kyoto Encyclopedia of Genes and Genomes; MF: Molecular Function; MMPs: Matrix Metalloproteinases; PPI: Protein-Protein Interaction; ROC: Receiver Operating Characteristic. Declarations Ethics approval and consent to participate Ethical approval was obtained from the Human and Ethics Committee for Medical Research at Sichuan University in accordance with the Declaration of Helsinki [Ethical Committee Approval: Annual Review 2022 (No. 1252)]. Written medical informed consent was obtained from all patients prior to their participation in the study Consent for publication All authors read the final version of this manuscript and endorsed it for publication. Competing interests The authors declare no competing interests. Funding This study was supported by the Key Research and Development Program of Science and Technology Department of Sichuan Province (No. 2023YFS0214), the General Program of Natural Science Foundation of Sichuan Province (No. 2023NSFSC0546), the Youth Program of Natural Science Foundation of Sichuan Province (No. 2024NSFSC1809), and the Science and Technology Program of Yantai City (No. 2021MSGY052). Author Contribution SY and HW wrote the main manuscript text and FF, XT, PX, prepared fgures 1–11. GC, PX and LL revised the manuscript. Data Availability The datasets involved in this study are available from the GEO (Gene Expression Omnibus) database (https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE191157. The code for data analysis and other data are available from the corresponding author upon reasonable request. References Markes AR, Hodax JD, Ma CB: Meniscus Form and Function. 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Li X, Liu Z, Zhou W, Liu X, Cao W: Downregulation of CCL22 and mutated NOTCH1 in tongue and mouth floor squamous cell carcinoma results in decreased Th2 cell recruitment and expression, predicting poor clinical outcome. BMC Cancer 2021, 21(1):922. Liu S, Zhang G, Li N, Wang Z, Lu L: The Interplay of Aging and PANoptosis in Osteoarthritis Pathogenesis: Implications for Novel Therapeutic Strategies. J Inflamm Res 2025, 18:1951-1967. Pawelec G: Age and immunity: What is "immunosenescence"? Exp Gerontol 2018, 105:4-9. Xu H, Lin S, Huang H: Involvement of increased expression of chemokine C-C motif chemokine 22 (CCL22)/CC chemokine receptor 4 (CCR4) in the inflammatory injury and cartilage degradation of chondrocytes. Cytotechnology 2021, 73(5):715-726. Luo H, Li L, Han S, Liu T: The role of monocyte/macrophage chemokines in pathogenesis of osteoarthritis: A review. Int J Immunogenet 2024, 51(3):130-142. Theodoraki MN, Yerneni S, Sarkar SN, Orr B, Muthuswamy R, Voyten J, Modugno F, Jiang W, Grimm M, Basse PH et al : Helicase-Driven Activation of NFκB-COX2 Pathway Mediates the Immunosuppressive Component of dsRNA-Driven Inflammation in the Human Tumor Microenvironment. Cancer Res 2018, 78(15):4292-4302. Kötting C, Hofmann L, Lotfi R, Engelhardt D, Laban S, Schuler PJ, Hoffmann TK, Brunner C, Theodoraki MN: Immune-Stimulatory Effects of Curcumin on the Tumor Microenvironment in Head and Neck Squamous Cell Carcinoma. Cancers (Basel) 2021, 13(6). Muthuswamy R, Berk E, Junecko BF, Zeh HJ, Zureikat AH, Normolle D, Luong TM, Reinhart TA, Bartlett DL, Kalinski P: NF-κB hyperactivation in tumor tissues allows tumor-selective reprogramming of the chemokine microenvironment to enhance the recruitment of cytolytic T effector cells. Cancer Res 2012, 72(15):3735-3743. Ren G, Whittaker JL, Leonard C, De Rantere D, Pang DSJ, Salo P, Fritzler M, Kapoor M, de Koning APJ, Jaremko JL et al : CCL22 is a biomarker of cartilage injury and plays a functional role in chondrocyte apoptosis. Cytokine 2019, 115:32-44. Nakamura ES, Koizumi K, Kobayashi M, Saitoh Y, Arita Y, Nakayama T, Sakurai H, Yoshie O, Saiki I: RANKL-induced CCL22/macrophage-derived chemokine produced from osteoclasts potentially promotes the bone metastasis of lung cancer expressing its receptor CCR4. Clin Exp Metastasis 2006, 23(1):9-18. Wang M, Tan G, Jiang H, Liu A, Wu R, Li J, Sun Z, Lv Z, Sun W, Shi D: Molecular crosstalk between articular cartilage, meniscus, synovium, and subchondral bone in osteoarthritis. Bone Joint Res 2022, 11(12):862-872. Liu C, Xu J, Fan J, Liu C, Xie W, Kong H: DPP-4 exacerbates LPS-induced endothelial cells inflammation via integrin-α5β1/FAK/AKT signaling. Exp Cell Res 2024, 435(1):113909. Sharma SB, Melvin WJ, Audu CO, Bame M, Rhoads N, Wu W, Kanthi Y, Knight JS, Adili R, Holinstat MA et al : The histone methyltransferase MLL1/KMT2A in monocytes drives coronavirus-associated coagulopathy and inflammation. Blood 2023, 141(7):725-742. Besta F, Müller I, Lorenz M, Massberg S, Bültmann A, Cabeza N, Richter T, Kremmer E, Nothdurfter C, Brand K et al : Reduced beta3-endonexin levels are associated with enhanced urokinase-type plasminogen activator receptor expression in ApoE-/- mice. Thromb Res 2004, 114(4):283-292. Besta F, Massberg S, Brand K, Müller E, Page S, Grüner S, Lorenz M, Sadoul K, Kolanus W, Lengyel E et al : Role of beta(3)-endonexin in the regulation of NF-kappaB-dependent expression of urokinase-type plasminogen activator receptor. J Cell Sci 2002, 115(Pt 20):3879-3888. Guo L, An T, Wan Z, Huang Z, Chong T: SERPINE1 and its co-expressed genes are associated with the progression of clear cell renal cell carcinoma. BMC Urol 2023, 23(1):43. Pang H, Ouyang J, Yang Z, Shang H, Liang G: Urokinase plasminogen activator surface receptor restricts HIV-1 replication by blocking virion release from the cell membrane. Proc Natl Acad Sci U S A 2023, 120(3):e2212991120. Han L, Zhang W, Wang J, Jing J, Zhang L, Liu Z, Gao A: Shikonin targets to m6A-modified oxidative damage pathway to alleviate benzene-induced testicular injury. Food Chem Toxicol 2022, 170:113496. Zhang N, Yang J, Zhao Y, Li W, Zhao B, Li R, He Z, Gu S: RNA m(6)A involves in regulation of oxidative stress and apoptosis may via NF-kB pathway in cadmium-induced lung cells. Cell Death Discov 2025, 11(1):4. D'Antona S, Porro D, Gallivanone F, Bertoli G: Characterization of cell cycle, inflammation, and oxidative stress signaling role in non-communicable diseases: Insights into genetic variants, microRNAs and pathways. Comput Biol Med 2024, 174:108346. Yan W, Kan Z, Li Z, Ma Y, Du D: Identification of Potential MicroRNA-MRNA Regulatory Relationship Pairs in Irritable Bowel Syndrome with Diarrhea. Comb Chem High Throughput Screen 2023, 26(8):1618-1628. Kim JY, Rhim WK, Woo J, Cha SG, Park CG, Han DK: The Upregulation of Regenerative Activity for Extracellular Vesicles with Melatonin Modulation in Chemically Defined Media. Int J Mol Sci 2022, 23(23). Ideozu JE, Zhang X, Rangaraj V, McColley S, Levy H: Microarray profiling identifies extracellular circulating miRNAs dysregulated in cystic fibrosis. Sci Rep 2019, 9(1):15483. Qiu ZK, Yang E, Yu NZ, Zhang MZ, Zhang WC, Si LB, Wang XJ: The biomarkers associated with epithelial-mesenchymal transition in human keloids. Burns 2024, 50(2):474-487. Wangsanut T, Brann KR, Adcox HE, Carlyon JA: Orientia tsutsugamushi modulates cellular levels of NF-κB inhibitor p105. PLoS Negl Trop Dis 2021, 15(4):e0009339. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6559896","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":472610073,"identity":"e26e3452-3ba3-4fdf-a968-7b07c50a7459","order_by":0,"name":"Shunjie Yang","email":"","orcid":"","institution":"West China Hospital of Sichuan University, Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Shunjie","middleName":"","lastName":"Yang","suffix":""},{"id":472610074,"identity":"47d5df31-a70e-4cfb-af64-c4bee09ef8b1","order_by":1,"name":"Hui Wang","email":"","orcid":"","institution":"West China Hospital of Sichuan University, Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Wang","suffix":""},{"id":472610075,"identity":"b29e3afd-c010-4080-9be4-e27d7856c882","order_by":2,"name":"Fangang Fu","email":"","orcid":"","institution":"Yantai Affiliated Hospital of Binzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fangang","middleName":"","lastName":"Fu","suffix":""},{"id":472610076,"identity":"8ebcf4b2-1bd2-4c2c-bbd5-ba8090bbafa7","order_by":3,"name":"Xiaohe Tian","email":"","orcid":"","institution":"West China Hospital of Sichuan University, Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Xiaohe","middleName":"","lastName":"Tian","suffix":""},{"id":472610077,"identity":"e29f165e-2697-4a6c-a491-01abbf0309b6","order_by":4,"name":"Li Liu","email":"","orcid":"","institution":"West China Hospital of Sichuan University, Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Liu","suffix":""},{"id":472610078,"identity":"05840fa2-c3e3-4767-bf85-44bf241c574c","order_by":5,"name":"Peng Xu","email":"","orcid":"","institution":"The Second People’s Hospital of Yibin","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Xu","suffix":""},{"id":472610079,"identity":"afe7d338-c370-4ebe-bfaf-ad194a2c6528","order_by":6,"name":"Gang Chen","email":"data:image/png;base64,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","orcid":"","institution":"West China Hospital of Sichuan University, Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Gang","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-04-30 00:38:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6559896/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6559896/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85346626,"identity":"2a77f64f-452f-48c1-822e-6ad23cc659ff","added_by":"auto","created_at":"2025-06-25 02:11:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":91891,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe flowchart of the study.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6559896/v1/4952e3c18f6f6e95174edcaf.png"},{"id":85346638,"identity":"a07cdb1c-f064-4243-b58b-4305f22cd4d3","added_by":"auto","created_at":"2025-06-25 02:11:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":131417,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVolcano plot, Heatmaps, and GO and KEGG enrichment analysis for DEGs. \u003c/strong\u003e(A) Volcano plot of DEGs between two groups, with p.adjust \u0026lt; 0.05 and log\u003csub\u003e2\u003c/sub\u003e| (FoldChange)|\u0026gt;0.5 as threshold. (B) Heatmap of the top 15 up-regulated and top 15 down-regulated DEGs (blue: downregulated; red: upregulated).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6559896/v1/ea60eb776fff3657a9b146f2.png"},{"id":85346627,"identity":"ef2649be-3058-4692-a7d8-1301531dde1e","added_by":"auto","created_at":"2025-06-25 02:11:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":363482,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of thirteen inflammation-related DEGs.\u003c/strong\u003e (A) Vennplots showing thirteen common DEGs shared by the inflammation-related genes. (B) GO analysis for DEGs, shown top 5 terms in MF/BP/CC with after sorted by p.adjust. (C) KEGG analysis for DEGs, shown top 10 pathways after sorted by p.adjust. (D) Network analysis for the KEGG enriched pathways. The more the total shared genes, the larger or redder the node, the more genes shared with more pathways, the more core the pathway. The more shared genes, the more pronounced the line, and the closer the relationship.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6559896/v1/5695b5d66235f60b6e64f608.png"},{"id":85346649,"identity":"7aa1e87b-4f0a-4d9a-9c7a-81d6a44f69ee","added_by":"auto","created_at":"2025-06-25 02:11:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":177408,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentifying hub genes and validating their mRNA expression in meniscus injury tissue. \u003c/strong\u003e(A) PPI network of thirteen inflammation-related DEGs. (B) The most significant module consisting of six potential hub genes. (C) mRNA expression difference of hub genes between aging and young group (** p\u0026lt;0.01).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6559896/v1/e6a98ccd556bb120a2ac45e1.png"},{"id":85346648,"identity":"93f1576e-5d58-4469-97c6-c4eb8437802d","added_by":"auto","created_at":"2025-06-25 02:11:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":643853,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRepresentative immunohistochemical images of hub genes in the meniscus injury tissue between aging and young patients. x400; *p\u0026lt;0.05.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6559896/v1/dfb4769a5a7702d81d1d5388.png"},{"id":85346646,"identity":"820a30e1-8374-4d9d-ae55-9eef946bb573","added_by":"auto","created_at":"2025-06-25 02:11:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":115244,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation, similarity, and diagnostic performance of hub genes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Correlation analysis between potential hub genes (* p.adjust \u0026lt;0.05, ** p.adjust \u0026lt;0.01, *** p.adjust \u0026lt;0.001). (B) Functional similarity analysis of potential hub genes in cellular component (CC), biological process (BP), and molecular function (MF). Mean: the average of scores in CC, BP, and MF. (C) Evaluation of diagnostic performance of hub genes.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6559896/v1/ccdf1812c63663dbab84ca38.png"},{"id":85346650,"identity":"fb90d655-3174-407a-ac36-5e2b0d8f5604","added_by":"auto","created_at":"2025-06-25 02:11:59","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":201951,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation of hub genes with the proportion of immune-infiltration cells.\u003c/strong\u003e (A) The proportion of immune cells in each sample. (B) The proportion difference of immune cells between aging and young group (ns: p \u0026gt; 0.05). (C) Correlation of hub genes with the proportion of immune-infiltration cells.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6559896/v1/05ada53131a13a1e42efed4b.png"},{"id":85346660,"identity":"7fd16542-6153-4046-b12e-11588ac3d5b6","added_by":"auto","created_at":"2025-06-25 02:11:59","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":262496,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation of hub genes with the expression of immune-infiltration cells.\u003c/strong\u003e (A) The expression of immune cells between aging and young group. (B) Correlation of hub genes with the expression of immune cells.* p.adjust \u0026lt;0.05, ** p.adjust \u0026lt;0.01. ns: p.adjust \u0026gt; 0.05.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6559896/v1/9de833d3fb608bc7de44c044.png"},{"id":85347220,"identity":"fcdab22b-01d6-4251-9891-aa9a77b09d5e","added_by":"auto","created_at":"2025-06-25 02:19:59","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":298318,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between hub genes and m6A/m5C regulators.\u003c/strong\u003e (A) Expression difference of m6A modifiers between aging and young groups. (B) Expression difference of m5C regulators between aging and young groups. (C) Correlation of hub genes with m6A modifiers. (D) Correlations of hub genes with m5C regulators. (E) Correlation between m6A modifiers and m5C regulators.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6559896/v1/16518a3951a308892cb219e6.png"},{"id":85346641,"identity":"481b2234-a1c0-461a-8f32-26be87588e16","added_by":"auto","created_at":"2025-06-25 02:11:58","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":589730,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of miRNAs-genes-TFs regulatory network of hub genes. \u003c/strong\u003e(A) The miRNAs-genes-TFs network of hub genes. The red are hub genes, the yellow are miRNAs, and the blue are TFs. (B) Regulatory nework of major relevant miRNAs and transcription factors with hub genes. Dark gray represents mirRNAs and transcription factors with the most associated hub genes.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-6559896/v1/8a07d95ca0326852ec6d9f1c.png"},{"id":85346655,"identity":"2f09c6d8-d5c7-40c8-8d0b-69b890f36703","added_by":"auto","created_at":"2025-06-25 02:11:59","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":121734,"visible":true,"origin":"","legend":"\u003cp\u003eThe potential inflammatory molecular mechanisms underlying age-related meniscus injury. Treg: regulatory T cell; Th1: Type 1 T helper cell; imDC: immature dendritic cell; aCD4T: activated CD4 T cell; plaDC: plasmacytoid dendritic cell.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-6559896/v1/812261eb77fc38363913e7e0.png"},{"id":102409602,"identity":"6f32903f-ea3b-4668-9f53-7cbf18c263e8","added_by":"auto","created_at":"2026-02-11 11:42:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3985748,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6559896/v1/b254dea1-d957-4ae7-b34d-499ad125d4f6.pdf"},{"id":85346625,"identity":"3b8e8975-d18d-44c8-8189-3f7599c28c68","added_by":"auto","created_at":"2025-06-25 02:11:57","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":32016,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-6559896/v1/e73fa80451ca524648358050.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eInflammatory Molecular Networks in Age-Related Meniscus Injury: TLR/NF-κB Signaling, Immune Dysregulation, and Epigenetic Modifications\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe meniscus is a vital fibrocartilaginous structure that maintains knee homeostasis by distributing load, absorbing impact, and providing stability [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Meniscus injury is one of the most common orthopedic disorders, often leading to progressive joint degeneration and osteoarthritis, especially in aging populations [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Epidemiological studies have shown that the prevalence of meniscus tears increases significantly with age, with degenerative tears predominating in those over 40 years of age [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Acute traumatic tears in young patients often have reparative potential, whereas age-related meniscus injury tends to follow a chronic, degenerative process with poor healing [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This disparity suggests that biological aging fundamentally alters the meniscus microenvironment, but the underlying molecular mechanisms remain poorly understood.\u003c/p\u003e \u003cp\u003eEmerging evidence implicates that the chronic low-grade inflammation known as \"inflammaging\" is a key driver of age-related tissue degeneration [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In the meniscus, aging is associated with increased expression of pro-inflammatory cytokine and matrix-degrading enzymes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Transcriptomic analyses further revealed that aging altered gene signatures related to immune activation, extracellular matrix (ECM) remodeling, and cellular senescence in meniscal tissues [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, there is a lack of systematic studies on the inflammatory network specific to age-related meniscus injury, especially its interaction with immune cell infiltration and epigenetic regulation. Recent studies have highlighted the role of RNA methylation, N\u003csup\u003e6\u003c/sup\u003e-methyladenosine (m\u003csup\u003e6\u003c/sup\u003eA) and 5-methylcytosine (m\u003csup\u003e5\u003c/sup\u003eC) in regulating inflammatory responses and tissue aging [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Modifications such as m\u003csup\u003e6\u003c/sup\u003eA and m\u003csup\u003e5\u003c/sup\u003eC affect mRNA stability and translation of inflammatory mediators [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], but their role in meniscus degeneration is unknown. Furthermore, while specific miRNAs are known to regulate inflammation and fibrosis [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], their networks in aged meniscus remains undefined.\u003c/p\u003e \u003cp\u003eThis study aims to explore the key inflammatory drivers of meniscus injury in aging patients through bioinformatics and experimental validation. By integrating the immune infiltration, epigenetics, and miRNA-transcription factor regulatory networks, the inflammatory axis of meniscus aging is revealed, which provides a basis for targeted interventions to protect joint health in aging population.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data source\u003c/h2\u003e \u003cp\u003eMicroarray expression data for meniscus injury were downloaded from the Gene Expression Omnibus (GEO) database. There were 8 samples in the GSE191157 dataset, including 4 young patients with meniscus injury aged 20\u0026ndash;25 years and 4 aging patients with meniscus injury aged 50\u0026ndash;55 years. Inflammatory response-associated genes were obtained from the hallmark gene sets in the Molecular Signature Database (MsigDB). The flowchart of this study was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Acquisition of differentially expressed genes (DEGs)\u003c/h2\u003e \u003cp\u003eThe meniscus injury samples in GEO database were divided into two groups: young and aging. All the microarray data after normalization were analyzed by R software. R package \u0026ldquo;limma\u0026rdquo; was used to identify DEGs between the two groups, with a threshold of p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05 after Benjamini-Hochberg (BH) correction and log\u003csub\u003e2\u003c/sub\u003e|(FoldChange)|\u0026gt;0.5. Heatmap cluster and volcano plot of the DEGs were created using the \u0026ldquo;pheatmap\u0026rdquo; and \u0026ldquo;ggplots\u0026rdquo; packages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Functional enrichment analysis of inflammation-related DEGs\u003c/h2\u003e \u003cp\u003eThe tissue-specific expression of DEGs was analyzed according to the BioGPS database and literatures. Inflammation-related DEGs were obtained by overlapping DEGs and inflammation-related genes. To reveal the functions of DEGs Inflammation-related DEGs, Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed using the \u0026ldquo;clusterProfiler\u0026rdquo; package. GO terms include biological process (BP), cellular component (CC) and molecular function (MF). KEGG pathway enrichment analysis tended to describe gene functions at the genomic and molecular levels. P.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05 after BH correction was considered statistically significant. The KEGG analysis results were further analyzed by network analysis, and the key pathways were identified according to the weighted degree calculated by the shared genes of each pathway.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Constructing protein\u0026ndash;protein interaction (PPI) network\u003c/h2\u003e \u003cp\u003eThe PPI network was constructed from STRING database, with a threshold of medium confidence\u0026thinsp;\u0026ge;\u0026thinsp;0.2. Cytoscape software was used to visualize the network, and then the Cytoscape plugin-MCODE to screen important modules in the PPI network as hub genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Validating the expression of hub genes\u003c/h2\u003e \u003cp\u003eThe meniscus tissue of 12 patients with meniscus injury who underwent arthroscopic meniscectomy were collected from sports medicine center, West China Hospital of Sichuan University, including 6 young patients aged 14\u0026ndash;25 years and 6 aging patients aged 50\u0026ndash;60 years. Informed consent was obtained from all participants, and this study was approved by the ethics committee of West China Hospital of Sichuan University. Excised meniscus tissue (0.5 cm\u003csup\u003e3\u003c/sup\u003e blocks) was taken and immediately snap frozen in liquid nitrogen for Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) or fixed in 4% paraformaldehyde for 24 h for immunohistochemistry (IHC).\u003c/p\u003e \u003cp\u003eThe qRT-PCR and IHC methods were similar to those of the previous study [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Primers and antibodies used in this study were shown in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-2. Gene quantification was expressed as the relative CT value (2\u003csup\u003e\u0026minus;△△Ct\u003c/sup\u003e), normalized to GAPDH levels. IHC stained slides were scanned at high power magnification (400\u0026times;) using the Motic EasyScan Pro 6 (Motic Digital Pathology, Barcelona, Spain) and exported as JPEG files (Motic DSAssistant). The IHC signal was quantified as an area of positive cells using Image-Pro Plus 6 (Media Cybernetics, Bethesda, MD, USA). For each slide, integrated optical density (IOD) were recorded from three different images and calculated the mean IOD. IOD was computed as pixel area \u0026times; mean intensity. Each experiment repeated in three times. Data were analyzed by SPSS 23.0 and expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. Student's t-test was used for comparison between groups. P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Evaluation of diagnostic performance of hub genes\u003c/h2\u003e \u003cp\u003eReceiver operating characteristic (ROC) curve analysis was performed using ROC software package to evaluate the diagnostic value of hub genes for meniscus inflammation in aging and young patients. The area under the curve (AUC) obtained provide the rationale and theoretical basis for distinguishing the diagnostic specificity and sensitivity of hub genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Correlation and functional similarity analysis among hub genes\u003c/h2\u003e \u003cp\u003eSpearman correlation analysis of hub genes was performed using the \"ggpub\" software package. P.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05 after BH correction was considered statistically significant. The geometric mean of CC, BP, and MF semantic similarity was used to assess the functional similarity between proteins through the GOSemSim package. Functional similarity is a measure to evaluate the strength of the relationship between each protein and its partners by the function and location of the protein.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Correlation of hub genes with immune-infiltration cells\u003c/h2\u003e \u003cp\u003eCibersort and Single-sample Gene Set Enrichment Analysis (ssGSEA) were used to analyze the proportion and expression difference of infiltrating immune cells, and the \u0026ldquo;ggpub\u0026rdquo; software package was used to analyze the spearman correlation between the proportion and expression of hub genes and infiltrating immune cells. P.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05 after BH correction was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Correlation of hub genes with m6A and m5C regulators\u003c/h2\u003e \u003cp\u003eThe ggplot2 software package was used to evaluate the correlations of hub genes with 28 m6A modifiers and 29 m5C regulators, and the specific molecules analyzed were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-B. P.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05 after BH correction was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Construction of hub genes regulatory network\u003c/h2\u003e \u003cp\u003eTranscription factors (TFs) and microRNAs (miRNAs) of hub genes were predicted by miRNet databases and then integrated into the regulatory network and visualized by Cytoscape software.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 DEGs and functional analysis\u003c/h2\u003e\n \u003cp\u003eBased on the GSE database, we first analyzed DEGs in meniscus injury between the young group and the aging group. The analysis revealed a total of 1014 DEGs, of which 783 were up-regulated and 231 were down-regulated, as shown in the generated volcano plot (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). The heat map showed the top 15 up-regulated and top 15 down-regulated DEGs with the greatest fold change (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). DEGs was mainly specifically expressed in immune-related tissues or cells, including 721 B lymphoblasts, CD33\u0026thinsp;+\u0026thinsp;Myeloid, CD8\u0026thinsp;+\u0026thinsp;Tcells and CD4\u0026thinsp;+\u0026thinsp;T Cell (Supplementary Table S3).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Inflammation-related DEGs and functional analysis\u003c/h2\u003e\n \u003cp\u003eAfter intersecting the inflammation-related genes with DEGs, thirteen inflammation-related DEGs were obtained (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA), including \u003cem\u003eIFNGR2, TLR3, INHBA, CCL22, PLAUR, BTG2, NFKBIA, GCH1, SRI, PDE4B, ITGA5, GPR132\u003c/em\u003e and \u003cem\u003eSELENOS\u003c/em\u003e (Supplementary Table S4). GO annotate showed that these inflammation-related DEGs were highly enriched in cytoplasmic sequestering of transcription factor, protein complex involved in cell adhesion, and calcium channel regulator activity (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). KEGG analysis showed that osteoclast differentiation, type 1 T helper cell (Th1), type 2 T helper cell and type 17 T helper cell differentiation were the top three key pathways, all with a weight degree of 29, and the common shared genes were \u003cem\u003eIFNGR2\u003c/em\u003e and \u003cem\u003eNFKBIA\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC-D, Supplementary Table S5).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Obtaining hub genes by PPI network construction\u003c/h2\u003e\n \u003cp\u003eTo further investigate the interactions of thirteen inflammation-related DEGs, we constructed a PPI network with thirteen nodes and twenty edges from the STRING database (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). The most significant module consisting of six genes and twelve edges was found using the MCODE plug-in of Cytoscape software. Six genes in the key modules, \u003cem\u003eIFNGR2, NFKBIA, CCL22, TLR3, PLAUR\u003c/em\u003e, and \u003cem\u003eITGA5\u003c/em\u003e, were selected as hub genes (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Validating the expression of hub genes\u003c/h2\u003e\n \u003cp\u003eMeniscus tissues were collected from 12 young and aging patients with meniscus injury, and the expressions of hub genes were verified by qRT-PCR and IHC. It was found that the mRNA expression of \u003cem\u003eCCL22, NFKBIA\u003c/em\u003e and \u003cem\u003eTLR3\u003c/em\u003e was up-regulated, and that of \u003cem\u003eIFNGR2, ITGA5\u003c/em\u003e and \u003cem\u003ePLAUR\u003c/em\u003e were down-regulated in the aging group compared with the young group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all), which was consistent with GEO transcriptome data (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC). The differences in protein expression of these six hub genes between the two groups were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all), and the difference trend was consistent with that of mRNA expression (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Evaluating the diagnostic performance and functional similarity of hub genes\u003c/h2\u003e\n \u003cp\u003eThe AUC of \u003cem\u003eNFKBIA, CCL22\u003c/em\u003e and \u003cem\u003eTLR3\u003c/em\u003e was larger than 0.95, and those of \u003cem\u003eIFNGR2, PLAUR\u003c/em\u003e and \u003cem\u003eITGA5\u003c/em\u003e was close to zero (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA). The results of spearman correlation analysis of hub genes are shown in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB. \u003cem\u003eTLR3\u003c/em\u003e was significantly correlated with other five hub genes, among which \u003cem\u003eIFNGR2\u003c/em\u003e had the highest negative correlation (R=-0.9, p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and \u003cem\u003eNFKBIA\u003c/em\u003e had the highest positive correlation (R\u0026thinsp;=\u0026thinsp;0.81, p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The correlation coefficients of \u003cem\u003eTLR3\u003c/em\u003e with \u003cem\u003eCCL22, ITGA5\u003c/em\u003e and \u003cem\u003ePLAUR\u003c/em\u003e were 0.76, -0.76 and \u0026minus;\u0026thinsp;0.76, respectively (p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all). \u003cem\u003eNFKBIA\u003c/em\u003e was also significantly correlated with \u003cem\u003eITGA5\u003c/em\u003e (R=-0.81, p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and \u003cem\u003ePLAUR\u003c/em\u003e (R=-0.98, p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.001). \u003cem\u003eITGA5\u003c/em\u003e was also significantly positively correlated with \u003cem\u003eIFNGR2\u003c/em\u003e (R\u0026thinsp;=\u0026thinsp;0.79, p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and \u003cem\u003ePLAUR\u003c/em\u003e (R\u0026thinsp;=\u0026thinsp;0.88, p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05). \u003cem\u003eCCL22\u003c/em\u003e was only positively correlated with \u003cem\u003eTLR3\u003c/em\u003e. In addition, we ranked hub genes based on the average functional similarity relationships between proteins in the interactome. The results showed that these hub genes were not similar in BP but similar in MF and CC. Overall, \u003cem\u003eIFNGR2, NFKBIA\u003c/em\u003e and \u003cem\u003eTLR3\u003c/em\u003e were similar in gene function (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Correlation of hub genes with immune-infiltration cells\u003c/h2\u003e\n \u003cp\u003eIn terms of the proportion of immune cells classified by cibersort, there was no significant difference between the aging group and the young group (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA-B), and there was no significant correlation between hub genes and the proportion of immune cells (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eC).\u003c/p\u003e\n \u003cp\u003eIn terms of the expression of immune cells analyzed by ssGSEA, activated B cell, activated CD4 T cell, central memory CD8 T cell, regulatory T cell (Treg) and Th1 were decreased, while immature dendritic cell and plasmacytoid dendritic cell were increased in the aging group compared with the young group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all) (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eA). Correlation analysis between key genes and immune cells expression showed that \u003cem\u003eCCL22\u003c/em\u003e was negatively correlated with activated CD4 T cell (R=-0.98, p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.01), immature dendritic cell (R\u0026thinsp;=\u0026thinsp;0.90, p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and regulatory T cell (R=-0.88, p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05). \u003cem\u003eTLR3\u003c/em\u003e and \u003cem\u003eCCL22\u003c/em\u003e were negatively correlated with type 1 T helper cell (R=-0.88, p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05). \u003cem\u003eITGA5\u003c/em\u003e was positively correlated with central memory CD8 T cell (R\u0026thinsp;=\u0026thinsp;0.90, p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all). \u003cem\u003eNFKBIA\u003c/em\u003e (R\u0026thinsp;=\u0026thinsp;0.93, p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and \u003cem\u003ePLAUR\u003c/em\u003e (R=-0.9, p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were associated with plasmacytoid dendritic cell (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eB).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7 Correlation of hub genes with m6A and m5C regulators\u003c/h2\u003e\n \u003cp\u003eAccumulating evidence suggests that RNA methylation plays an important role in osteogenesis and inflammation. Therefore, we sought to analyze the interactive effects of m6A and 5mC regulators on the expression of hub genes in meniscus injury.\u003c/p\u003e\n \u003cp\u003eFor m6A regulators, \u003cem\u003eHNRNPC, YTHDC1, YTHDC2, METTL14\u003c/em\u003e, and \u003cem\u003eVIRMA\u003c/em\u003e were up-regulated, while \u003cem\u003eIGF2BP2, YTHDF1\u003c/em\u003e, and \u003cem\u003eCBLL1\u003c/em\u003e were down-regulated in the aged group compared with the young group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all) (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eA). Analysis of the correlation between hub genes and m6A modifier expression (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eC) showed that \u003cem\u003eNFKBIA\u003c/em\u003e had the strongest positive correlation with \u003cem\u003eYTHDC2\u003c/em\u003e (R\u0026thinsp;=\u0026thinsp;0.95, p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05). \u003cem\u003eTLR3\u003c/em\u003e was positively correlated with \u003cem\u003eHNPRNPC\u003c/em\u003e (R\u0026thinsp;=\u0026thinsp;0.93, p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\n \u003cp\u003eFor m5C regulators, \u003cem\u003eNEIL1, TDG, UHRF2, NSUN3\u003c/em\u003e, and \u003cem\u003eNSUN6\u003c/em\u003e were up-regulated, while \u003cem\u003eYBX1, UHRF1\u003c/em\u003e, and \u003cem\u003eDNMT3A\u003c/em\u003e were down-regulated in the aged group compared with the young group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all) (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eB). Analysis of the correlation between hub genes and m5C modifier expression (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eD) shown that \u003cem\u003eNFKBIA\u003c/em\u003e had the strongest positive correlation with \u003cem\u003eUHRF2\u003c/em\u003e (R\u0026thinsp;=\u0026thinsp;0.98, p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and \u003cem\u003eNSUN3\u003c/em\u003e (R\u0026thinsp;=\u0026thinsp;0.93, p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.01). \u003cem\u003eNSUN3\u003c/em\u003e was positively correlated with \u003cem\u003eTLR3\u003c/em\u003e (R\u0026thinsp;=\u0026thinsp;0.90, p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05). \u003cem\u003eUHRF2\u003c/em\u003e was negatively correlated with \u003cem\u003ePLARU\u003c/em\u003e (R=-0.93, p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cp\u003eFor the correlations between the expression of m6A and m5C regulators (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eE), \u003cem\u003eYTHDC2\u003c/em\u003e showed the strongest positive correlation with \u003cem\u003eUHRF2\u003c/em\u003e (R\u0026thinsp;=\u0026thinsp;0.98, p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and \u003cem\u003eNSUN3\u003c/em\u003e (R\u0026thinsp;=\u0026thinsp;0.95, p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05). \u003cem\u003eHNPRNPC\u003c/em\u003e was also positively correlated with \u003cem\u003eUHRF2\u003c/em\u003e (R\u0026thinsp;=\u0026thinsp;0.95, p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and \u003cem\u003eNSUN3\u003c/em\u003e (R\u0026thinsp;=\u0026thinsp;0.98, p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003e3.8 Constructing the miRNAs-genes-TFs regulatory networks of hub genes\u003c/h2\u003e\n \u003cp\u003eThe miRNAs-genes-TFs networks consisting of 6 hub genes, 407 miRNAs and 24 TFs was constructed using miRNet database and Cytoscape software (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003eA). One miRNA, hsa-let-7b-5p, acted on all hub genes, and thirteen miRNAs on five common hub genes were screened (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003eB). Since no transcription factor acts on all hub genes, the transcription factor most relevant to the common hub gene was selected as the ideal target. Two transcription factors that act on four common core genes were screened, including NFKB1 and RELA, both of which act on CCL22, NFKBIA, PLAUR, and TLR3 (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003eB).\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAge-related meniscus injury is an important cause of knee joint degeneration, but its inflammatory mechanism is still unclear. In this study, bioinformatics and experimental validation were used to explore the inflammatory molecular mechanisms of age-related meniscus injury. Our findings highlight the critical roles of TLR3/NF-κB signaling, immune dysregulation, and epigenetic modifications (m6A/m5C) in driving age-related meniscus injury (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e4.1 TLR3/NF-κB signaling as a central driver of inflammation and immune dysregulation in the age-related meniscus injury\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe identified six hub genes (\u003cem\u003eIFNGR2, NFKBIA, CCL22, TLR3, PLAUR\u003c/em\u003e,and \u003cem\u003eITGA5\u003c/em\u003e) that played pivotal roles in age-related meniscus injury. Among them, \u003cem\u003eNFKBIA, TLR3\u003c/em\u003e, and \u003cem\u003eCCL22\u003c/em\u003e had high diagnostic value (AUC \u0026gt; 0.95), suggesting their potential as biomarkers for age-related meniscus degeneration.\u003c/p\u003e \u003cp\u003eNF-κB is central to the immune response, and its dysregulation is associated with chronic inflammation and osteoarthritis [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. \u003cem\u003eNFKBIA\u003c/em\u003e limits inflammation by inhibiting the NF-κB signaling pathway. In our study, \u003cem\u003eNFKBIA\u003c/em\u003e was upregulated in aged meniscus injury, consistent with prior studies linking abnormally prolonged NF-κB activation to negative feedback dysregulation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In chronic inflammation, IκBα and A20, negative feedback of NF-κB regulators was much less synthesized because a large fraction is uncapped, unspliced, and retained in the nucleus [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Thus, the anti-inflammatory effects \u003cem\u003eof NFKBIA\u003c/em\u003e may be \"submerged\" and manifest as persistent inflammation.\u003c/p\u003e \u003cp\u003e \u003cem\u003eTLR3\u003c/em\u003e, a viral RNA sensor, is increasingly recognized for its role in aseptic inflammation of aging and osteoarthritis [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. \u003cem\u003eTLR3\u003c/em\u003e showed a general correlation with all other hub genes. The strong positive correlation between \u003cem\u003eTLR3\u003c/em\u003e and \u003cem\u003eNFKBIA\u003c/em\u003e indicates that Toll-like receptor signaling may exacerbate the inflammation of aged-related meniscus injury through NF-κB, consistent with studies linking \u003cem\u003eTLR3\u003c/em\u003e to cartilage degradation by NF-κB-mediated \u003cem\u003eMMPs\u003c/em\u003e upregulatoin [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNotably, \u003cem\u003eCCL22\u003c/em\u003e, a chemokine involved in T-cell recruitment [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e–\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], was significantly upregulated in age-related meniscus injury, positive correlated only with TLR3, negatively correlated with Treg and Th1 cells, and positively correlated with immature dendritic cells. This aligns with evidence that aging disrupts immune homeostasis and promotes a pro-inflammatory microenvironment [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. \u003cem\u003eIFNGR2\u003c/em\u003e, an interferon gamma receptor, is an important anti-inflammatory factor. The downregulation of \u003cem\u003eIFNGR2 and\u003c/em\u003e the strong negative correlation with \u003cem\u003eTLR3\u003c/em\u003e further suggest impaired anti-inflammatory signaling and potentially exacerbating tissue damage. This may be a feedback inhibitory effect of persistent inflammation.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.2 CCL22-related immune microenvironment changes in aging meniscus\u003c/h2\u003e \u003cp\u003eImmune infiltration analysis showed reduced activated B cells, activated CD4 + T cells, Treg and Th1 cells, alongside increased immature and plasmacytoid dendritic cells in age-related meniscus injury. This is consistent with the \"immunosenescence\" phenotype observed in osteoarthritis [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Upregulation of CCL22/TLR3 is consistent with their proinflammatory roles in osteoarthritis. The expression of CCL22/CCR4 is increased in osteoarthritic cartilage injury and is involved in the inflammation and cartilage degradation of chondrocytes. CCL22 silencing can alleviate the cartilage degradation of mouse chondrocytes through CCR4 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Moreover, the symptoms and progression of osteoarthritis were effectively alleviated and controlled when CCL22 expression was inhibited [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCCL22 was negatively correlated with downregulated Treg and Th1 cells, and positively correlated with upregulated immature dendritic cell. CCL22 can promote the migration of CCR4 + DCs to the injury site, and CCL22-induced DCs can promote the proliferation of Treg [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In addition, CCL22 production and its attraction to Treg are regulated by TLR3/NFκB [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e–\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. There is a strong positive correlation between CCL22 and TLR3, and a negative correlation between CCL22/TLR3 and Th1 cells, suggesting that chronic inflammation of the aging meniscus may inhibit TLR3/NF-κB-mediated protective t cell response and accelerate meniscus degeneration. In addition, CCL22 is elevated in human ostoarthritis cartilage and induces apoptosis in isolated human chondrocytes, which may be an early factor in the initiation of cartilage degeneration [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. CCL22 is selectively upregulated in osteoclast-like cells [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], which may be involved in osteoclast differentiation and bone degeneration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.3 ITGA5, PLAUR and ECM degradation\u003c/h2\u003e \u003cp\u003eITGA5 (integrin α5) pairs with β1 integrin to form α5β1, which in turn binds to fibronectin and mediates cell-ECM adhesion, crucial for the stability of meniscus structure [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This process may be related to NF-κB signaling pathway, which is a downstream molecule of integrin-α5β1 [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. \u003cem\u003ePLAUR\u003c/em\u003e binds urokinase plasminogen activator converts plasminogen to plasmin, which in turn activates MMPs and leads to ECM breakdown. \u003cem\u003ePLAUR\u003c/em\u003e transcription depends on NF-κB/RELA [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e–\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. \u003cem\u003eITGA5\u003c/em\u003e and \u003cem\u003ePLAUR\u003c/em\u003e were downregulated in aged samples. The former was positively correlated with central memory CD8 + T cells, while the latter was negatively correlated with plasmacytoid dendritic cell. This may be related to the alteration of immune cell migration caused by ECM degradation. Downregulation o\u003cem\u003ef ITGA5\u003c/em\u003e in aging meniscus tissue may lead to weakened cell-ECM adhesion and impaired fibrochondrocyte anchoring, thereby reducing CD8 + T cells migration [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Conversely, reduced \u003cem\u003ePLAUR\u003c/em\u003e may inhibit fibrinolysis, which may increase plasmacytoid dendritic cell migration [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. It is speculated that IGA5 and PLAUR-related ECM degradation is involved in the changes of immune microenvironment of the aging meniscus, which may be mediated by TLR/NF-κBa/CCL22 signaling pathway.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Epigenetic regulation through RNA methylation\u003c/h2\u003e \u003cp\u003eA novel aspect of this study was to explore the regulators of RNA methylation (m6A/m5C) in meniscus aging. We found that \u003cem\u003eNFKBIA\u003c/em\u003e and \u003cem\u003eTLR3\u003c/em\u003e were strongly correlated with m6A/m5C writers (\u003cem\u003eYTHDC2, UHRF2, NSUN3\u003c/em\u003e), which were upregulated in aging meniscus. This suggests that post-transcriptional RNA modifications may stabilize proinflammatory transcripts and perpetuate inflammation. YTHDC2, an m6A reader, has been reported to be associated with NF-κB activation in oxidative stress [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], supporting our findings. The tight correlation among \u003cem\u003eYTHDC2\u003c/em\u003e, \u003cem\u003eUHRF2\u003c/em\u003e, and \u003cem\u003eNSUN3\u003c/em\u003e further supports their synergistic role in age-related epigenetic dysregulation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.4 miRNAs-TFs regulatory network\u003c/h2\u003e \u003cp\u003eThe miRNAs-TFs regulatory network revealed that \u003cem\u003ehsa-let-7b-5p\u003c/em\u003e targets all hub genes, which is consistent with its known role in suppressing inflammation [\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e–\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] and fibrosis [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Reportedly, hsa-let-7b-5p and hsa-miR-19b-3p are correlated in irritable bowel syndrome, and hsa-miR-19b-3p may regulate the production of NF-κB through the the PI3K/Akt pathway [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Whether the function of hsa-let-7b-5p in age-related meniscus injury is associated with the TLR/NF-κB pathway needs to be further validated.\u003c/p\u003e \u003cp\u003eIn addition, RELA and NFKB1, components of the NF-κB complex, can regulate multiple hub genes (\u003cem\u003eCCL22, NFKBIA, TLR3, PLAUR\u003c/em\u003e), thereby reinforcing the central role of NF-κB in meniscal degeneration. This may be a key mechanism for the positive feedback amplification of NF-κB signaling, thus contributing to the persistence of inflammation in aging patients [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study unveils the multifaceted interplay of inflammation, immunity, and epigenetics in age-related meniscus injury. TLR3/NF-κB/CCL22 and m6A/m5C networks are key regulators. Future studies will focus on validation in TLR3 knockout models or m6A editing experiments to establish mechanistic links and translate these findings into therapeutics targeting TLR3/NF-κB/CCL22 or m6A modifications.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAUC: Area Under the Curve; BP: Biological Process; CC: Cellular Component; DCs: Dendritic Cells; DEGs: Differentially Expressed Genes; DSIF: DRB Sensitivity-Inducing Factor; ECM: Extracellular Matrix; GEO: Gene Expression Omnibus; GO: Gene Ontology; IHC: Immunohistochemistry; IOD: Integrated Optical Density; KEGG: Kyoto Encyclopedia of Genes and Genomes; MF: Molecular Function; MMPs: Matrix Metalloproteinases; PPI: Protein-Protein Interaction; ROC: Receiver Operating Characteristic.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e Ethical approval was obtained from the Human and Ethics Committee for Medical Research at Sichuan University in accordance with the Declaration of Helsinki [Ethical Committee Approval: Annual Review 2022 (No. 1252)]. Written medical informed consent was obtained from all patients prior to their participation in the study\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eAll authors read the final version of this manuscript and endorsed it for publication.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by the Key Research and Development Program of Science and Technology Department of Sichuan Province (No. 2023YFS0214), the General Program of Natural Science Foundation of Sichuan Province (No. 2023NSFSC0546), the Youth Program of Natural Science Foundation of Sichuan Province (No. 2024NSFSC1809), and the Science and Technology Program of Yantai City (No. 2021MSGY052).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSY and HW wrote the main manuscript text and FF, XT, PX, prepared fgures 1\u0026ndash;11. GC, PX and LL revised the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets involved in this study are available from the GEO (Gene Expression Omnibus) database (https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE191157. The code for data analysis and other data are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMarkes AR, Hodax JD, Ma CB: Meniscus Form and Function. \u003cem\u003eClin Sports Med \u003c/em\u003e2020, 39(1):1-12.\u003c/li\u003e\n\u003cli\u003eEdd SN, Giori NJ, Andriacchi TP: The role of inflammation in the initiation of osteoarthritis after meniscal damage. \u003cem\u003eJ Biomech \u003c/em\u003e2015, 48(8):1420-1426.\u003c/li\u003e\n\u003cli\u003eTsujii A, Nakamura N, Horibe S: Age-related changes in the knee meniscus. \u003cem\u003eKnee \u003c/em\u003e2017, 24(6):1262-1270.\u003c/li\u003e\n\u003cli\u003eHayashi M, Koga S, Kitagawa T: Effectiveness of Rehabilitation for Knee Osteoarthritis Associated With Isolated Meniscus Injury: A Scoping Review. \u003cem\u003eCureus \u003c/em\u003e2023, 15(2):e34544.\u003c/li\u003e\n\u003cli\u003eDuong V, Oo WM, Ding C, Culvenor AG, Hunter DJ: 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15(4):e0009339.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Meniscus injury, Aging, Inflammation, NFKBIA, TLR3, CCL22, Immune infiltration, RNA methylation","lastPublishedDoi":"10.21203/rs.3.rs-6559896/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6559896/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eThe meniscus plays a crucial biomechanical role in the knee joint, and its injury often leads to degenerative joint diseases. Aging significantly increases the risk of meniscus injury, with emerging evidence suggesting dysregulated inflammatory responses as a key factor. This study investigated the inflammatory molecular mechanisms underlying age-related meniscus injury and identified potential therapeutic targets.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eThe transcriptomic data of injured meniscus tissues from 4 young and 4 aging patients were obtained from the GEO database. Differentially expressed genes (DEGs) were analyzed and intersected with inflammation-related genes in MsigDB. Functional enrichment (GO/KEGG), protein-protein interaction (PPI) network analysis, and hub gene identification were performed. ROC curve was used to evaluate the diagnostic efficacy. We evaluated the correlations among hub genes, immune cell infiltration, RNA methylation regulators (m6A/m5C), and regulatory networks (miRNAs/TFs). Meanwhile, the expression of hub genes in the meniscus injury tissue between aging and young patients was verified by qRT-PCR and immumohistochemical staining.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eA total of 1009 DEGs (755 upregulated and 254 downregulated) were identified in aging meniscus, with 13 inflammation-related DEGs enriched in transcription factor regulation, osteoclast differentiation, and T-helper cell pathways. Six hub genes (\u003cem\u003eIFNGR2, NFKBIA, CCL22, TLR3, PLAUR, ITGA5\u003c/em\u003e) were identified, with \u003cem\u003eNFKBIA, CCL22\u003c/em\u003e, and \u003cem\u003eTLR3\u003c/em\u003e showing high diagnostic accuracy (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.95). In the validation samples, \u003cem\u003eCCL22, NFKBIA\u003c/em\u003e and \u003cem\u003eTLR3\u003c/em\u003e were upregulated and \u003cem\u003eIFNGR2, ITGA5\u003c/em\u003e and \u003cem\u003ePLAUR\u003c/em\u003e were downregulated in aging meniscus, consistent with the transcriptomic data. \u003cem\u003eTLR3\u003c/em\u003e strongly correlated with other hub genes (\u003cem\u003eNFKBIA\u003c/em\u003e: R\u0026thinsp;=\u0026thinsp;0.81; \u003cem\u003eIFNGR2\u003c/em\u003e: R=-0.9). Immune cell analysis showed a decrease in activated B/T cells and an increase in dendritic cells in aging samples. \u003cem\u003eNFKBIA\u003c/em\u003e and \u003cem\u003ePLAUR\u003c/em\u003e were correlated with plasmacytoid dendritic cells, while \u003cem\u003eCCL22\u003c/em\u003e and \u003cem\u003eTLR3\u003c/em\u003e were negatively associated with Th1 cells. Hub genes also showed strong links with m6A/m5C regulators (\u003cem\u003eYTHDC2, UHRF2, NSUN3\u003c/em\u003e). Regulatory network analysis implicated \u003cem\u003ehsa-let-7b-5p, NFKB1\u003c/em\u003e, and \u003cem\u003eRELA\u003c/em\u003e in modulating hub genes.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eAge-related meniscus injury involves inflammatory pathways, immune dysregulation, and epigenetic modifications. Hub genes (\u003cem\u003eCCL22, NFKBIA, TLR3\u003c/em\u003e) and associated regulators (e.g., \u003cem\u003ehsa-let-7b-5p, YTHDC2\u003c/em\u003e) may serve as diagnostic markers or therapeutic targets for age-related meniscus injury.\u003c/p\u003e","manuscriptTitle":"Inflammatory Molecular Networks in Age-Related Meniscus Injury: TLR/NF-κB Signaling, Immune Dysregulation, and Epigenetic Modifications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-25 02:11:52","doi":"10.21203/rs.3.rs-6559896/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a2bbd81e-cca7-40e9-bbc1-2f0cfb0fddf9","owner":[],"postedDate":"June 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50239787,"name":"Biological sciences/Genetics"},{"id":50239788,"name":"Biological sciences/Immunology"},{"id":50239789,"name":"Biological sciences/Molecular biology"},{"id":50239790,"name":"Health sciences/Biomarkers"},{"id":50239791,"name":"Health sciences/Diseases"}],"tags":[],"updatedAt":"2026-02-11T11:41:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-25 02:11:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6559896","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6559896","identity":"rs-6559896","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}