Identification and verification of ferroptosis-related genes in the synovial tissue of osteoarthritis using bioinformatics analysis

Molecular mechanisms of ferroptosis in renal ischemia-reperfusion injury Investigated via bioinformatics analysis and animal experiments

Kidney transplantation is a pivotal treatment for end-stage renal disease. However, renal ischemia-reperfusion injury (IRI) during surgery significantly impacts graft function. Despite unclear molecular mechanisms, no specific therapies or preventative measures are available. Gene expression profiles from renal biopsies before and after IRI were downloaded from public databases. Differentially expressed genes were identified using the Wilcoxon rank-sum test and weighted gene co-expression network analysis. Ferroptosis-associated genes were screened using the FerrDb database. The genes with the highest connectivity were identified via the protein-protein interaction (PPI) network and upstream regulatory miRNAs were found through the gene-miRNA network. A mouse renal IRI model was constructed for transcriptome sequencing and quantitative real-time polymerase chain reaction (qRT-PCR) validation to elucidate the relationship between key ferroptosis genes and regulatory miRNAs in renal IRI. Differential analysis identified 15 ferroptosis-associated genes (TNFAIP3, IL6, KLF2, EGR1, JUN, ZFP36, GDF15, CDKN1A, HSPB1, BRD2, PDK4, DUSP1, SLC2A3, DDIT3, and CXCL2) involved in renal IRI regulation. In animal experiments, ferroptosis-related genes were also upregulated in the model group. Enrichment analysis and hematoxylin-eosin pathological staining suggested these genes are primarily involved in renal inflammatory responses. PPI network analysis revealed IL6 as the gene with the highest connectivity, and the gene-miRNA network indicated IL6 might be regulated by miR-let-7a. Animal experiments revealed decreased miR-let-7a and increased IL6 levels in the model group, identifying potential therapeutic targets. MiR-let-7a regulates ferroptosis in renal IRI by targeting IL6, highlighting IL6 as a crucial gene in the ferroptosis process of renal IRI.

Ferroptosis in Osteoarthritis: Towards Novel Therapeutic Strategy

Osteoarthritis (OA) is a chronic, degenerative joint disease primarily characterised by damage to the articular cartilage, synovitis and persistent pain, and has become one of the most common diseases worldwide. In OA cartilage, various forms of cell death have been identified, including apoptosis, necroptosis and autophagic cell death. Ever‐growing observations indicate that ferroptosis, a newly‐discovered iron‐dependent form of regulated cell death, is detrimental to OA occurrence and progression. In this review, we first analyse the pathogenetic mechanisms of OA by which iron overload, inflammatory response and mechanical stress contribute to ferroptosis. We then discuss how ferroptosis exacerbates OA progression, focusing on its impact on chondrocyte viability, synoviocyte populations and extracellular matrix integrity. Finally, we highlight several potential therapeutic strategies targeting ferroptosis that could be explored for the treatment of OA.

Exploring the role of ATF3 and ferroptosis-related RNA expression in osteoarthritis: An RNA analysis approach to immune infiltration

Osteoarthritis (OA) is a widespread joint disorder that is primarily noted for the progressive degeneration of joint cartilage, accompanied by a significant inflammatory response. Recently, there has been a growing interest in understanding the roles of ATF3 and ferritin-related RNAs in the context of immune responses and inflammatory processes. However, their specific functions and mechanisms in the progression of osteoarthritis have remained largely ambiguous and underexplored. The primary objective of this study was to thoroughly investigate the changes in expression levels of ATF3 and ferritin-related RNAs within osteoarthritic tissues, as well as to examine their potential effects on immune cell infiltration. To achieve this, advanced RNA sequencing technology was employed to meticulously analyze the expression levels of ATF3 and the ferritin-related RNAs. Furthermore, bioinformatics methods were utilized to assess the infiltration patterns of various immune cells and to explore the correlation between these infiltration patterns and the expression levels of RNA. The findings from this study revealed that both ATF3 and ferritin-related RNAs exhibited significantly elevated expression levels in tissues affected by osteoarthritis. Additionally, the immunoinfiltration analysis highlighted a positive correlation between the degree of infiltration of T cells and macrophages and the levels of ferritin-related RNAs. Such findings suggest that the presence of these immune cells is intricately linked to the expression of ferritin-associated RNAs. Further investigations indicated that ferritin-associated RNAs play a critical role in the progression of osteoarthritis by modulating inflammatory responses and influencing the activity of various immune cells. Consequently, both ATF3 and ferritin-related RNAs demonstrate abnormal expression patterns in osteoarthritis, which are closely associated with the infiltration of immune cells.

Construction of ferroptosis-related gene signatures for identifying potential biomarkers and immune cell infiltration in osteoarthritis

Osteoarthritis (OA) is a comprehensive joint disorder. The specific genes that trigger OA and the strategies for its effective management are not fully understood. This study focuses on identifying key genes linked to iron metabolism that could influence both the diagnosis and therapeutic approaches for OA. Analysis of GEO microarray data and iron metabolism genes identified 15 ferroptosis-related DEGs, enriched in hypoxia and HIF-1 pathways. Ten key hub genes (ATM, GCLC, PSEN1, CYBB, ATG7, MAP1LC3B, PLIN2, GRN, APOC1, SIAH2) were identified. Through stepwise regression, we screened 4 out of the above 10 genes, namely, GCLC, GRN, APOC1, and SIAH2, to obtain the optimal model. AUROCs for diagnosis of OA for the four hub genes were 0.81 and 0.80 of training and validation sets, separately. According to immune infiltration results, OA was related to significantly increased memory B cells, M0 macrophages, regulatory T cells, and resting mast cells but decreased activated dendritic cells. The four hub genes showed a close relation to them. It is anticipated that this model will aid in diagnosing osteoarthritis by assessing the expression of specific genes in blood samples. Moreover, studying these hub genes may further elucidate the pathogenesis of osteoarthritis.

Ferroptosis-related prognostic model of mantle cell lymphoma

Background

Mantle cell lymphoma (MCL) is a B-cell non-Hodgkin's lymphoma. Ferroptosis, an iron-dependent programmed cell death, is closely related to cancer prognosis. In this study, we established a model of ferroptosis related genes for prognostic evaluation of patients with MCL.

Methods

Using the single-cell RNA sequencing datasets GSE184031 and mRNA sequencing data GSE32018 from the Gene Expression Omnibus, we identified 139 ferroptosis-related genes in MCL. Next a prognostic model was constructed by Cox regression and Least absolute selection and shrinkage Operator regression analysis. Finally, we used CIBERSORT to analyze the immune microenvironment and the "oncoPredict" package to predict potential drugs.

Results

In our model, the prognosis of MCL patients was assessed by risk scoring using 7 genes ANXA1, IL1B, YBX1, CCND1, MS4A1, MFHAS1, and RILPL2. The patients were divided into high-risk and low-risk groups based on our model, and the high-risk patients had inferior overall survival. Finally, according to our model and computational drug sensitivity analysis, four small molecule compounds, BMS-754807, SB216763, Doramapimod, and Trametinib, were identified as potential therapeutic agents for patients with MCL.

Conclusion

In summary, we provide a prognostic model with ferroptosis-related gene signature for MCL. This study provides a prognostic model with ferroptosis-related gene signature for MCL. The results show that the model helps predict prognosis in MCL.

Regulation of ferroptosis in osteoarthritis and osteoarthritic chondrocytes by typical MicroRNAs in chondrocytes

Osteoarthritis (OA) is a progressive degenerative disorder impacting bones and joints, worsened by chronic inflammation, immune dysregulation, mechanical stress, metabolic disturbances, and various other contributing factors. The complex interplay of cartilage damage, loss, and impaired repair mechanisms remains a critical and formidable aspect of OA pathogenesis. At the genetic level, multiple genes have been implicated in the modulation of chondrocyte metabolism, displaying both promotive and inhibitory roles. Recent research has increasingly focused on the influence of non-coding RNAs in the regulation of distinct cell types within bone tissue in OA. In particular, an expanding body of evidence highlights the regulatory roles of microRNAs in OA chondrocytes. This review aims to consolidate the most relevant microRNAs associated with OA chondrocytes, as identified in recent studies, and to elucidate their involvement in chondrocyte metabolic processes and ferroptosis. Furthermore, this study explores the complex regulatory interactions between long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) in OA, with an emphasis on microRNA-mediated mechanisms. Finally, critical gaps in the current research are identified, offering strategic insights to advance the understanding of OA pathophysiology and guide therapeutic developments in this field.

A lactate metabolism-related gene signature to diagnose osteoarthritis based on machine learning combined with experimental validation

BACKGROUND

Lactate is gradually proved as the essential regulator in intercellular signal transduction, energy metabolism reprogramming, and histone modification. This study aims to clarify the diagnosis value of lactate metabolism-related genes in osteoarthritis (OA).

METHODS

Lactate metabolism-related genes were retrieved from the MSigDB. GSE51588 was downloaded from the Gene Expression Omnibus (GEO) as the training dataset. GSE114007, GSE117999, and GSE82107 datasets were adopted for external validation. Genomic difference detection, protein-protein interaction network analysis, LASSO, SVM-RFE, Boruta, and univariate logistic regression (LR) analyses were used for feature selection. Multivariate LR, Random Forest (RF), Support Vector Machine (SVM), and XGBoost (XGB) were used to develop the multiple-gene diagnosis models. 12 control and 12 OA samples were collected from the local hospital for re-verification. The transfection assays were conducted to explore the regulatory ability of the gene to the apoptosis and vitality of chondrocytes.

RESULTS

Through the bioinformatical analyses and machine learning algorithms, SLC2A1 and NDUFB9 of the 273 lactate metabolism-related genes were identified as the significant diagnosis biomarkers. The LR, RF, SVM, and XGB models performed impressively in the cohorts (AUC > 0.7). The local clinical samples indicated that SLC2A1 and NDUFB9 were both down-regulated in the OA samples (both P < 0.05). The knockdown of NDUFB9 inhibited the viability and promoted the apoptosis of the CHON-001 cells treated with IL-1beta (both P < 0.05).

CONCLUSIONS

A lactate metabolism-related gene signature was constructed to diagnose OA, which was validated in multiple independent cohorts, local clinical samples, and cellular functional experiments.

Repair and regeneration: ferroptosis in the process of remodeling and fibrosis in impaired organs

As common clinical-pathological processes, wound healing and tissue remodelling following injury or stimulation are essential topics in medical research. Promoting the effective healing of prolonged wounds, improving tissue repair and regeneration, and preventing fibrosis are important and challenging issues in clinical practice. Ferroptosis, which is characterized by iron overload and lipid peroxidation, is a nontraditional form of regulated cell death. Emerging evidence indicates that dysregulated metabolic pathways and impaired iron homeostasis play important roles in various healing and regeneration processes via ferroptosis. Thus, we review the intrinsic mechanisms of tissue repair and remodeling via ferroptosis in different organs and systems under various conditions, including the inflammatory response in skin wounds, remodeling of joints and cartilage, and fibrosis in multiple organs. Additionally, we summarize the common underlying mechanisms, key molecules, and targeted drugs for ferroptosis in repair and regeneration. Finally, we discuss the potential of therapeutic agents, small molecules, and novel materials emerging for targeting ferroptosis to promote wound healing and tissue repair and attenuate fibrosis.

The role of mitochondrial autophagy in osteoarthritis

Osteoarthritis (OA) is a progressive degenerative joint disease, and the underlying molecular mechanisms of OA remain poorly understood. This study aimed to elucidate the relationship between mitochondrial autophagy and OA by identifying key regulatory genes and their biological functions. Utilizing bioinformatics analyses of RNA expression profiles from the GSE55235 dataset, we identified 2,136 differentially expressed genes, leading to the discovery of hub genes associated with mitochondrial autophagy and OA. Gene set enrichment analysis (GSEA) revealed their involvement in critical pathways, highlighting their potential roles in OA pathogenesis. Furthermore, our study explored the immunological landscape of OA, identifying distinct immune cell infiltration patterns that contribute to the disease's inflammatory profile. We also evaluated the therapeutic potential of drugs targeting these hub genes, suggesting potential approaches for OA treatment. Collectively, this study advances our knowledge of mitochondrial autophagy in OA and proposes promising biomarkers and therapeutic targets.

Ferroptosis-Related Genes in IgA Nephropathy: Screening for Potential Targets of the Mechanism

Aims: Exploring key genes and potential molecular pathways of ferroptosis in immunoglobulin A nephropathy (IgAN). Methods: The IgAN datasets and ferroptosis-related genes (FRGs) were obtained in the Gene Expression Omnibus (GEO) and FerrDb database. Differentially expressed genes (DEGs) were identified using R software and intersected with FRGs to obtain differentially expressed FRGs (DE-FRGs). After that, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis (PEA) and Gene Ontology (GO) functional annotation were performed on DE-FRGs. In the Search Tool for the Retrieval of Interacting Genes (STRING) website, we construct a protein-protein interaction (PPI) network. The PPI network was further investigated with screening hub genes with Cytoscape software. The core genes were then subjected to gene set enrichment analysis (GSEA). Finally, the samples were analyzed for immune infiltration in R, and the correlation between hub genes and immune cells was analyzed. Results: A total of 347 DEGs were identified. CD44, CDO1, CYBB, IL1B, RRM2, AKR1C1, activated transcription factor-3 (ATF3), CDKN1A, GDF15, JUN, MGST1, MIOX, MT1G, NR4A1, PDK4, TNFAIP3, and ZFP36 were determined as DE-FRGs. JUN, IL1B, and ATF3 were then screened as hub genes. GSEA and immune infiltration analysis revealed that the hub genes were closely associated with immune inflammatory responses such as NOD-like receptor signaling, IL-17 signaling, and TNF signaling. Conclusions: Our results show that JUN and ATF3 are possibly critical genes in the process of IgAN ferroptosis and may be related with immune cell infiltration.

Risk factor prediction and immune correlation analysis of cuproptosis-related gene in osteoarthritis

Osteoarthritis (OA) is a widespread inflammatory joint disease with significant global disability burden. Cuproptosis, a newly identified mode of cell death, has emerged as a crucial factor in various pathological conditions, including OA. In this context, our study aims to investigate the intrinsic relationship between cuproptosis-related genes (CRGs) and OA, and assess their potential as biomarkers for OA diagnosis and treatment. Datasets from the GEO databases were analysed the differential expression of CRGs, leading to the identification of 10 key CRGs (CDKN2A, DLD, FDX1, GLS, LIAS, LIPT1, MTF1, PDHA1, DLAT and PDHB). A logistic regression analysis and calibration curves were used to show excellent diagnostic accuracy. Consensus clustering revealed two CRG patterns, with Cluster 1 indicating a closer association with OA progression. RT-PCR confirmed a significant increase in the expression levels of these nine key genes in IL-1β-induced C28/i2 cells, and the expression of CDKN2A and FDX1 were also elevated in conditioned monocytes, while the expression of GLS and MTF1 were significantly decreased. In vitro experiments demonstrated that the expression levels of these 7/10 CRGs were significantly increased in chondrocytes induced by IL-1β, and upon stimulation with cuproptosis inducers, chondrocyte apoptosis was exacerbated, accompanied by an increase in the expression of cuproptosis-related proteins. These further substantiated our research findings and indicated that the nine selected cuproptosis genes have high potential for application in the diagnosis of OA.

Iron metabolism and arthritis: Exploring connections and therapeutic avenues

ABSTRACT

Iron is indispensable for the viablility of nearly all living organisms, and it is imperative for cells, tissues, and organisms to acquire this essential metal sufficiently and maintain its metabolic stability for survival. Disruption of iron homeostasis can lead to the development of various diseases. There is a robust connection between iron metabolism and infection, immunity, inflammation, and aging, suggesting that disorders in iron metabolism may contribute to the pathogenesis of arthritis. Numerous studies have focused on the significant role of iron metabolism in the development of arthritis and its potential for targeted drug therapy. Targeting iron metabolism offers a promising approach for individualized treatment of arthritis. Therefore, this review aimed to investigate the mechanisms by which the body maintains iron metabolism and the impacts of iron and iron metabolism disorders on arthritis. Furthermore, this review aimed to identify potential therapeutic targets and active substances related to iron metabolism, which could provide promising research directions in this field.

Identification and verification of a novel signature that combines cuproptosis-related genes with ferroptosis-related genes in osteoarthritis using bioinformatics analysis and experimental validation

BACKGROUND

Exploring the pathogenesis of osteoarthritis (OA) is important for its prevention, diagnosis, and treatment. Therefore, we aimed to construct novel signature genes (c-FRGs) combining cuproptosis-related genes (CRGs) with ferroptosis-related genes (FRGs) to explore the pathogenesis of OA and aid in its treatment.

MATERIALS AND METHODS

Differentially expressed c-FRGs (c-FDEGs) were obtained using R software. Enrichment analysis was performed and a protein-protein interaction (PPI) network was constructed based on these c-FDEGs. Then, seven hub genes were screened. Three machine learning methods and verification experiments were used to identify four signature biomarkers from c-FDEGs, after which gene set enrichment analysis, gene set variation analysis, single-sample gene set enrichment analysis, immune function analysis, drug prediction, and ceRNA network analysis were performed based on these signature biomarkers. Subsequently, a disease model of OA was constructed using these biomarkers and validated on the GSE82107 dataset. Finally, we analyzed the distribution of the expression of these c-FDEGs in various cell populations.

RESULTS

A total of 63 FRGs were found to be closely associated with 11 CRGs, and 40 c-FDEGs were identified. Bioenrichment analysis showed that they were mainly associated with inflammation, external cellular stimulation, and autophagy. CDKN1A, FZD7, GABARAPL2, and SLC39A14 were identified as OA signature biomarkers, and their corresponding miRNAs and lncRNAs were predicted. Finally, scRNA-seq data analysis showed that the differentially expressed c-FRGs had significantly different expression distributions across the cell populations.

CONCLUSION

Four genes, namely CDKN1A, FZD7, GABARAPL2, and SLC39A14, are excellent biomarkers and prospective therapeutic targets for OA.

Bioinformatics and Integrative Experimental Method to Identifying and Validating Co-Expressed Ferroptosis-Related Genes in OA Articular Cartilage and Synovium

Purpose

Osteoarthritis (OA) is the most common joint disease worldwide and is the primary cause of disability and chronic pain in older adults.Ferroptosis is a type of programmed cell death characterized by aberrant iron metabolism and reactive oxygen species accumulation; however, its role in OA is not known.

Methods

To identify ferroptosis markers co-expressed in articular cartilage and synovium samples from patients with OA, in silico analysis was performed.Signature genes were analyzed and the results were evaluated using a ROC curve prediction model.The biological function, correlation between Signature genes, immune cell infiltration, and ceRNA network analyses were performed. Signature genes and ferroptosis phenotypes were verified through in vivo animal experiments and clinical samples. The expression levels of non-coding RNAs in samples from patients with OA were determined using qRT-PCR. ceRNA network analysis results were confirmed using dual-luciferase assays.

Results

JUN, ATF3, and CDKN1A were identified as OA- and ferroptosis-associated signature genes. GSEA analysis demonstrated an enrichment of these genes in immune and inflammatory responses, and amino acid metabolism. The CIBERSORT algorithm showed a negative correlation between T cells and these signature genes in the cartilage, and a positive correlation in the synovium. Moreover, RP5-894D12.5 and FAM95B1 regulated the expression of JUN, ATF3, and CDKN1A by competitively binding to miR-1972, miR-665, and miR-181a-2-3p. In vivo, GPX4 was downregulated in both OA cartilage and synovium; however, GPX4 and GSH were downregulated, while ferrous ions were upregulated in patient OA cartilage and synovium samples, indicating that ferroptosis was involved in the pathogenesis of OA. Furthermore, JUN, ATF3, and CDKN1A expression was downregulated in both mouse and human OA synovial and cartilage tissues. qRT-PCR demonstrated that miR-1972, RP5-894D12.5, and FAM95B1 were differentially expressed in OA tissues. Targeted interactions between miR-1972 and JUN, and a ceRNA regulatory mechanism between RP5-894D12.5, miR-1972, and JUN were confirmed by dual-luciferase assays.

Conclusion

This study identified JUN, ATF3, and CDKN1A as possible diagnostic biomarkers and therapeutic targets for joint synovitis and OA. Furthermore, our finding indicated that RP5-894D12.5/miR-1972/JUN was a potential ceRNA regulatory axis in OA, providing an insight into the connection between ferroptosis and OA.

Screening of diagnostic biomarkers for ferroptosis-related osteoarthritis and construction of a risk-prognosis model

Background

Osteoarthritis (OA) is the most prevalent and commonly chronic joint disease that frequently develops among the elderly population. It is not just a single tissue that is affected, but rather a pathology involving the entire joint. Among them, synovitis is a key pathological change in OA. Ferroptosis is a newly discovered form of cell death that results from the buildup of lipid peroxidation. However, the role and impact of it in OA are yet to be explored.

Objective

The key to this work is to uncover the mechanisms of ferroptosis-related OA pathogenesis and develop more novel diagnostic biomarkers to facilitate the diagnostic and therapeutic of OA.

Materials and methods

Download ferroptosis-related genes and OA synovial chip datasets separately from the FerrDB and Gene Expression Omnibus databases. Identify ferroptosis differentially expressed genes using R software, obtain the intersection genes through two machine learning algorithms, and obtain diagnostic biomarkers after logistic regression analysis. Verify the diagnostic and therapeutic efficacy of specific genes for OA through the construction of clinical risk prognostic models using ROC curves and nomogram. Simultaneously, correlations between specific genes and OA immune cell infiltration co-expression were constructed. Finally, verify the differential presentation of specific genes in OA and health control synovium.

Results

Obtain 38 ferroptosis differentially expressed genes through screening. Based on machine learning algorithms and logistic regression analysis, select AGPS, BRD4, RBMS1, and EGR1 as diagnostic biomarker genes. The diagnostic and therapeutic efficacy of the four specific genes for OA has been validated by ROC curves and nomogram of clinical risk prognostic models. The analysis of immune cell infiltration and correlation suggests a close association between specific genes and OA immune cell infiltration. Further revealing the diagnostic value of specific genes for OA by the differential presentation analysis of their differential presentation in synovial tissue from OA and health control.

Conclusion

This study identified four diagnostic biomarkers for OA that are associated with iron death. The establishment of a risk-prognostic model is conducive to the premature diagnosis of OA, evaluating functional recovery during rehabilitation, and guidance for subsequent treatment.

Delineation and authentication of ferroptosis genes in ventilator-induced lung injury

BACKGROUND

Mechanical ventilation, a critical support strategy for individuals enduring severe respiratory failure and general anesthesia, paradoxically engenders ventilator-induced lung injury (VILI). Ferrostatin-1 mitigates lung injury via ferroptosis inhibition, yet the specific ferroptosis genes contributing significantly to VILI remain obscure.

METHODS

Leveraging the Gene Expression Omnibus database, we acquired VILI-associated datasets and identified differentially expressed genes (DEGs). To identify the hub genes, we constructed a protein-protein interaction network and used three parameters from CytoHubba. Consequently, we identified hub genes and ferroptosis genes as ferroptosis hub genes for VILI (VFHGs). We conducted enrichment analysis and established receiver operating characteristic (ROC) curves for VFHGs. Subsequently, to confirm the correctness of the VFHGs, control group mice and VILI mouse models, as well as external dataset validation, were established. For further research, a gene-miRNA network was established. Finally, the CIBERSORT algorithm was used to fill the gap in the immune infiltration changes in the lung during VILI.

RESULTS

We identified 64 DEGs and 4 VFHGs (Il6,Ptgs2,Hmox1 and Atf3) closely related to ferroptosis. ROC curves demonstrated the excellent diagnostic performance of VFHGs in VILI. PCR and external dataset validation of the VILI model demonstrated the accuracy of VFHGs. Subsequently, the gene-miRNA network was successfully established. Ultimately, an Immune cell infiltration analysis associated with VILI was generated.

CONCLUSIONS

The results emphasize the importance of 4 VFHGs and their involvement in ferroptosis in VILI, confirming their potential as diagnostic biomarkers for VILI.

Screening of Biomarkers Associated with Osteoarthritis Aging Genes and Immune Correlation Studies

Purpose

Osteoarthritis (OA) is a joint disease with a long and slow course, which is one of the major causes of disability in middle and old-aged people. This study was dedicated to excavating the cellular senescence-associated biomarkers of OA.

Methods

The Gene Expression Omnibus (GEO) database was searched and five datasets pertaining to OA were obtained. After removing the batch effect, the GSE55235, GSE55457, GSE82107, and GSE12021 datasets were integrated together for screening of the candidate genes by differential analysis and weighted gene co-expression network analysis (WGCNA). Next, those genes were further filtered by machine learning algorithms to obtain cellular senescence-associated biomarkers of OA. Subsequently, enrichment analyses based on those biomarkers were conducted, and we profiled the infiltration levels of 22 types immune cells with the ERSORT algorithm. A lncRNA-miRNA-mRNA regulatory and drug-gene network were constructed. Finally, we validated the senescence-associated biomarkers at both in vivo and in vitro levels.

Results

Five genes (BCL6, MCL1, SLC16A7, PIM1, and EPHA3) were authenticated as cellular senescence-associated biomarkers in OA. ROC curves demonstrated the reliable capacity of the five genes as a whole to discriminate OA samples from normal samples. The nomogram diagnostic model based on 5 genes proved to be a reliable predictor of OA. Single-gene GSEA results pointed to the involvement of the five biomarkers in immune-related pathways and oxidative phosphorylation in the development of OA. Immune infiltration analysis manifested that the five genes were significantly correlated with differential immune cells. Subsequently, a lncRNA-miRNA-mRNA network and gene-drug network containing were generated based on five cellular senescence-associated biomarkers in OA.

Conclusion

A foundation for understanding the pathophysiology of OA and new insights into OA diagnosis and treatment were provided by the identification of five genes, namely BCL6, MCL1, SLC16A7, PIM1, and EPHA3, as biomarkers associated with cellular senescence in OA.

Glycolysis: an emerging regulator of osteoarthritis

Osteoarthritis (OA) has been a leading cause of disability in the elderly and there remains a lack of effective therapeutic approaches as the mechanisms of pathogenesis and progression have yet to be elucidated. As OA progresses, cellular metabolic profiles and energy production are altered, and emerging metabolic reprogramming highlights the importance of specific metabolic pathways in disease progression. As a crucial part of glucose metabolism, glycolysis bridges metabolic and inflammatory dysfunctions. Moreover, the glycolytic pathway is involved in different areas of metabolism and inflammation, and is associated with a variety of transcription factors. To date, it has not been fully elucidated whether the changes in the glycolytic pathway and its associated key enzymes are associated with the onset or progression of OA. This review summarizes the important role of glycolysis in mediating cellular metabolic reprogramming in OA and its role in inducing tissue inflammation and injury, with the aim of providing further insights into its pathological functions and proposing new targets for the treatment of OA.

Exploration of the potential mechanism of Duhuo Jisheng Decoction in osteoarthritis treatment by using network pharmacology and molecular dynamics simulation

In this study, the active ingredients of 15 Chinese herbal medicines of Duhuo Jisheng Decoction and their corresponding targets were obtained from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. The microarray data of Osteoarthritis (OA) were obtained through the GEO database for differential analysis and then a drug target-OA-related gene protein-protein interaction (PPI) network was established. The potential targets of Duhuo Jisheng Decoction in the treatment of OA were acquired by intersecting the OA-associated genes with the target genes of active ingredients. Random walk with restart (RWR) analysis of PPI networks was performed using potential targets as seed, and the top 50 genes of affinity coefficients were used as key action genes of Duhuo Jisheng Decoction in the treatment of OA. A drug-active ingredient-gene interaction network was established. AKT1, a key target of Duhuo Jisheng Decoction in the treatment of OA, was obtained by topological analysis of the gene interaction network. Molecular docking and molecular dynamics verified the binding of AKT1 to its corresponding drug active ingredients. CETSA assay demonstrated that the combination of luteolin and AKT1 increased the stability of AKT1, and the combination efficiency was high. In conclusion, the molecular mechanism of Duhuo Jisheng Decoction in treating OA featured by multiple components, targets, and pathways had been further investigated in this study, which is of significance for discovering as well as developing new drugs for this disease. The findings can also offer personalized diagnosis and treatment strategies for patients with OA in clinical practice.

Bioinformatics analysis of hub genes as osteoarthritis prognostic biomarkers

Osteoarthritis (OA) is a progressive cartilage degradation disease, concomitant with synovitis, osteophyte formation, and subchondral bone sclerosis. Over 37% of the elderly population is affected by OA, and the number of cases is increasing as the global population ages. Therefore, the objective of this study was to identify and analyze the hub genes of OA combining with comprehensive bioinformatics analysis tools to provide theoretical basis in further OA effective therapies. Two sample sets of GSE46750 contained 12 pairs OA synovial membrane and normal samples harvested from patients as well as GSE98918 including 12 OA and non-OA patients were downloaded from the Gene Expression Omnibus database (GEO) database. Differentially expressed genes (DEGs) were identified using Gene Expression Omnibus 2R (GEO2R), followed by functional enrichment analysis, protein-protein interaction networks construction. The hub genes were identified and evaluated. An OA rat model was constructed, hematoxylin and eosin staining, safranin O/fast green staining, cytokines concentrations of serum were used to verify the model. The hub genes expression level in the knee OA samples were verified using RT-qPCR. The top 20 significantly up-regulated and down-regulated DEGs were screened out from the two datasets, respectively. The top 18 GO terms and 10 KEGG pathways were enriched. Eight hub genes were identified, namely MS4A6A, C1QB, C1QC, CD74, CSF1R, HLA-DPA1, HLA-DRA and ITGB2. Among them, the hub genes were all up-regulated in in vivo OA rat model, compared with healthy controls. The eight hub genes identified (MS4A6A, C1QB, C1QC, CD74, CSF1R, HLA-DPA1, HLA-DRA and ITGB2) were shown to be associated with OA. These genes can serve as disease markers to discriminate OA patients from healthy controls.

Synovium is a sensitive tissue for mapping the negative effects of systemic iron overload in osteoarthritis: identification and validation of two potential targets

BACKGROUND

The prevention and treatment of osteoarthritis (OA) pose a major challenge in its research. The synovium is a critical tissue in the systematic treatment of OA. The present study aimed to investigate potential target genes and their correlation with iron overload in OA patients.

METHODS

The internal datasets for analysis included the microarray datasets GSE46750, GSE55457, and GSE56409, while the external datasets for validation included GSE12021 and GSE55235. The GSE176308 dataset was used to generate single-cell RNA sequencing profiles. To investigate the expression of the target genes in synovial samples, quantitative reverse transcription-PCR, western blotting, and immunohistochemical assay were conducted. ELISA was used to detect the levels of ferritin and Fe2+ in both serum and synovium.

RESULTS

JUN and ZFP36 were screened from the differentially expressed genes, and their mRNA were significantly reduced in the OA synovium compared to that in normal synovium. Subsequently, complex and dynamically evolving cellular components were observed in the OA synovium. The mRNA level of JUN and ZFP36 differed across various cell clusters of OA synovium and correlated with immune cell infiltration. Moreover, ferritin and Fe2+ were significantly increased in the serum and synovium of OA patients. Further, we found that JUN elevated and ZFP36 decreased at protein level.

CONCLUSIONS

The synovium is a sensitive tissue for mapping the adverse effects of systemic iron overload in OA. JUN and ZFP36 represent potential target genes for attenuating iron overload during OA treatment. Some discrepancies between the transcription and protein levels of JUN suggest that post-transcriptional modifications may be implicated. Future studies should also focus on the roles of JUN and ZFP36 in inducing changes in cellular components in the synovium during OA pathogenesis.

Ferroptosis-related genes LPCAT3 and PGD are potential diagnostic biomarkers for osteoarthritis

BACKGROUND

Osteoarthritis (OA) is the most common chronic joint disease and how ferroptosis contributes to OA has garnered much attention recently. Bioinformatics promoted the discovery of ferroptosis-related biomarkers for OA. But since OA is a whole-joint disease, sensitive biomarkers for OA are still limited. We herein focused on subchondral bone, a joint component often-ignored by existing bioinformatic reports, to identify ferroptosis-related diagnostic biomarkers for OA.

METHOD

Microarray datasets GSE51588 and GSE55457 were downloaded from Gene Expression Omnibus database. Ferroptosis-related differential expression genes (Ferr-DEGs) between OA and normal samples were identified and their functional enrichment was analyzed. Common genes for OA diagnosis were selected from Ferr-DEGs using the combination of SVM-RFE, LASSO regression, and RandomForest machine learning algorithms. Common genes' diagnostic value was verified by receiver operating characteristic (ROC) curve and their association with immune infiltration was analyzed by CIBERSORT. Finally, candidate gene's expression was verified in chondrocytes from OA patients and in an in vitro IL-1β-induced OA model, by RT-PCR.

RESULTS

Two ferroptosis-related genes, LPCAT3 and PGD, were identified as OA diagnostic biomarkers and confirmed by ROC diagnostic test. The association of LPCAT3 and PGD with the infiltration of several types of immune cells was identified. The decreased expression of LPCAT3 and PGD was both confirmed in OA chondrocytes and IL-1β-induced OA condition.

CONCLUSIONS

We identified ferroptosis-related genes LPCAT3 and PGD as potential diagnostic biomarkers for OA, which may offer insight into the role of ferroptosis in OA and provides useful information for the diagnosis and treatment of OA.

Exploring molecular mechanisms underlying the pathophysiological association between knee osteoarthritis and sarcopenia

Objectives

Accumulating evidence indicates a strong link between knee osteoarthritis (KOA) and sarcopenia. However, the mechanisms involved have not yet been elucidated. This study primarily aims to explore the molecular mechanisms that explain the connection between these 2 disorders.

Methods

The gene expression profiles for KOA and sarcopenia were obtained from the Gene Expression Omnibus database, specifically from GSE55235, GSE169077, and GSE1408. Various bioinformatics techniques were employed to identify and analyze common differentially expressed genes (DEGs) across the 3 datasets. The techniques involved the analysis of Gene Ontology and pathways to enhance understanding, examining protein-protein interaction (PPI) networks, and identifying hub genes. In addition, we constructed the network of interactions between transcription factors (TFs) and genes, the co-regulatory network of TFs and miRNAs for hub genes, and predicted potential drugs.

Results

In total, 14 common DEGs were found between KOA and sarcopenia. Detailed information on biological processes and signaling pathways of common DEGs was obtained through enrichment analysis. After performing PPI network analysis, we discovered 4 hub genes (FOXO3, BCL6, CDKN1A, and CEBPB). Subsequently, we developed coregulatory networks for these hub genes involving TF-gene and TF-miRNA interactions. Finally, we identified 10 potential chemical compounds.

Conclusions

By conducting bioinformatics analysis, our study has successfully identified common gene interaction networks between KOA and sarcopenia. The potential of these findings to offer revolutionary understanding into the common development of these 2 conditions could lead to the identification of valuable targets for therapy.

Identification of EGFR as an essential regulator in chondrocytes ferroptosis of osteoarthritis using bioinformatics, in vivo, and in vitro study

Objective

The mechanisms of chondrocytes ferroptosis in osteoarthritis (OA) have not yet been fully elucidated. This study aimed to identify key ferroptosis related genes (FRGs) involved in chondrocytes ferroptosis.

Methods

LASSO, SVM-RFE, and receiver operating characteristic curve (ROC) were performed to screen key differentially expressed FRGs (DEFRGs). Functional analyses were conducted using GO, and KEGG analyses. Unsupervised clustering analysis was used to identify ferroptosis related patterns. The CeRNA network was constructed to predict the upstream miRNAs and lncRNAs. Finally, we validated the role of EGFR in chondrocytes ferroptosis using in vivo and in vitro experiments.

Results

A total of 42 DEFRGs were identified between OA and normal cartilages. GO and KEGG analyses indicated that these DEFRGs were significantly engaged in ferroptosis related biological processes and pathways, such as cellular response to oxidative stress, positive regulation of programmed cell death, MAPK and PI3K-Akt signaling pathways. Moreover, four key DEFRGs, including ACSF2, AURKA, EGFR, and KLHL24, were considered as potential biomarkers of OA. Moreover, two distinct ferroptosis related patterns were determined, and a total of 882 differentially expressed genes were identified which might participate in extracellular matrix degradation and inflammatory response. In addition, the CeRNA network showed that EGFR could be competitively regulated by 3 lncRNAs and 4 miRNAs. Significantly, the expression of EGFR was downregulated in human OA cartilages, OA mouse model, and erastin induced chondrocytes. EGFR inhibition could induce the occurrence of chondrocytes ferroptosis and ECM degradation which could be reversed by the addition of Ferrostatin-1.

Conclusion

Our study has identified ACSF2, AURKA, EGFR, and KLHL24 as ferroptosis-related biomarkers in OA. Furthermore, we have conducted a preliminary investigation into the role of EGFR in regulating chondrocytes ferroptosis. These findings offer novel insights into the molecular mechanisms underlying OA.

Lipid peroxidation in osteoarthritis: focusing on 4-hydroxynonenal, malondialdehyde, and ferroptosis

Osteoarthritis (OA) is a multifactorial and increasingly prevalent degenerative disease that affects the whole joint. The pathogenesis of OA is poorly understood and there is a lack of therapeutic interventions to reverse the pathological process of this disease. Accumulating studies have shown that the overproduction of reactive oxygen species (ROS) and ROS-induced lipid peroxidation are involved in the pathogenesis of OA. 4-Hydroxy-2-nonenal (4-HNE) and malondialdehyde (MDA) have received considerable attention for their role in cartilage degeneration and subchondral bone remodeling during OA development. Ferroptosis is a form of cell death characterized by a lack of control of membrane lipid peroxidation and recent studies have suggested that chondrocyte ferroptosis contributes to OA progression. In this review, we aim to discuss lipid peroxidation-derived 4-HNE and MDA in the progression of OA. In addition, the therapeutic potential for OA by controlling the accumulation of lipid peroxidation and inhibiting chondrocyte ferroptosis are discussed.

Down-regulation of Jun induces senescence through destabilizing chromatin in osteoarthritis chondrocytes

OBJECTIVE

Osteoarthritis (OA) is the most common degenerative joint disease leading to disability worldwide. Cellular senescence is considered to be a fundamental pathogenic mechanism in the development of OA and has attracted increasing attention. However, regulatory mechanisms underlying chondrocyte senescence in OA remain unclear.

METHODS

Bioinformatic methods were used to screen key genes. Immunohistochemistry and the quantitative reverse transcription polymerase chain reaction were used to evaluate gene expression. RNA intervention experiments were performed to explore the functions of key genes.

RESULTS

We used 494 aging-associated genes provided by the Aging Atlas to identify the co-expression modules associated with age and OA. Thirty age-associated differentially expressed genes (ASDEGs) were identified. Using cytoHubba in Cytoscape, we identified Jun as the hub-ASDEG for OA chondrocytes. We confirmed the downregulation of Jun in OA rats and senescent chondrocytes by immunohistochemistry and quantitative reverse transcription polymerase chain reaction, respectively. Inhibition of proliferation and accelerated senescence were observed in chondrocytes treated with siRNA against Jun. Mechanistically, we observed micronuclei formation and reduced expression of H3K9me3 and heterochromatin protein 1gamma in siRNA-Jun-treated chondrocytes, indicating that destabilization of chromatin occurred during this treatment.

CONCLUSION

Jun plays a crucial role in OA development and causes senescence by destabilizing chromatin in chondrocytes. These findings provide new insights into OA progression and suggest promising therapeutic targets.

Identification of 3 key genes as novel diagnostic and therapeutic targets for OA and COVID-19

Background

Corona Virus Disease 2019 (COVID-19) and Osteoarthritis (OA) are diseases that seriously affect the physical and mental health and life quality of patients, particularly elderly patients. However, the association between COVID-19 and osteoarthritis at the genetic level has not been investigated. This study is intended to analyze the pathogenesis shared by OA and COVID-19 and to identify drugs that could be used to treat SARS-CoV-2-infected OA patients.

Methods

The four datasets of OA and COVID-19 (GSE114007, GSE55235, GSE147507, and GSE17111) used for the analysis in this paper were obtained from the GEO database. Common genes of OA and COVID-19 were identified through Weighted Gene Co-Expression Network Analysis (WGCNA) and differential gene expression analysis. The least absolute shrinkage and selection operator (LASSO) algorithm was used to screen key genes, which were analyzed for expression patterns by single-cell analysis. Finally, drug prediction and molecular docking were carried out using the Drug Signatures Database (DSigDB) and AutoDockTools.

Results

Firstly, WGCNA identified a total of 26 genes common between OA and COVID-19, and functional analysis of the common genes revealed the common pathological processes and molecular changes between OA and COVID-19 are mainly related to immune dysfunction. In addition, we screened 3 key genes, DDIT3, MAFF, and PNRC1, and uncovered that key genes are possibly involved in the pathogenesis of OA and COVID-19 through high expression in neutrophils. Finally, we established a regulatory network of common genes between OA and COVID-19, and the free energy of binding estimation was used to identify suitable medicines for the treatment of OA patients infected with SARS-CoV-2.

Conclusion

In the present study, we succeeded in identifying 3 key genes, DDIT3, MAFF, and PNRC1, which are possibly involved in the development of both OA and COVID-19 and have high diagnostic value for OA and COVID-19. In addition, niclosamide, ciclopirox, and ticlopidine were found to be potentially useful for the treatment of OA patients infected with SARS-CoV-2.

Development and validation of cuproptosis-related genes in synovitis during osteoarthritis progress

Osteoarthritis (OA) is one of the most common refractory degenerative joint diseases worldwide. Synovitis is believed to drive joint cartilage destruction during OA pathogenesis. Cuproptosis is a novel form of copper-induced cell death. However, few studies have examined the correlations between cuproptosis-related genes (CRGs), immune infiltration, and synovitis. Therefore, we analyzed CRGs in synovitis during OA. Microarray datasets (GSE55235, GSE55457, GSE12021, GSE82107 and GSE176308) were downloaded from the Gene Expression Omnibus database. Next, we conducted differential and subtype analyses of CRGs across synovitis. Immune infiltration and correlation analyses were performed to explore the association between CRGs and immune cell abundance in synovitis. Finally, single-cell RNA-seq profiling was performed using the GSE176308 dataset to investigate the expression of CRGs in the various cell clusters. We found that the expression of five CRGs (FDX1, LIPT1, PDHA1, PDHB, and CDKN2A) was significantly increased in the OA synovium. Moreover, abundant and various types of immune cells infiltrated the synovium during OA, which was correlated with the expression of CRGs. Additionally, single-cell RNA-seq profiling revealed that the cellular composition of the synovium was complex and that their proportions varied greatly as OA progressed. The expression of CRGs differed across various cell types in the OA synovium. The current study predicted that cuproptosis may be involved in the pathogenesis of synovitis. The five screened CRGs (FDX1, LIPT1, PDHA1, PDHB, and CDKN2A) could be explored as candidate biomarkers or therapeutic targets for OA synovitis.

Identification of SLC3A2 as a Potential Therapeutic Target of Osteoarthritis Involved in Ferroptosis by Integrating Bioinformatics, Clinical Factors and Experiments

Osteoarthritis (OA) is a type of arthritis that causes joint pain and limited mobility. In recent years, some studies have shown that the pathological process of OA chondrocytes is related to ferroptosis. Our study aims to identify and validate differentially expressed ferroptosis-related genes (DEFRGs) in OA chondrocytes and to investigate the potential molecular mechanisms. RNA-sequencing and microarray datasets were downloaded from Gene Expression Omnibus (GEO) data repository. Differentially expressed genes (DEGs) were screened by four methods: limma-voom, edgeR, DESeq2, and Wilcoxon rank-sum test. Weighted correlation network analysis (WGCNA), protein-protein interactions (PPI), and cytoHubba of Cytoscape were applied to identify hub genes. Clinical OA cartilage specimens were collected for quantitative reverse transcription-polymerase chain reaction (qRT-PCR) analysis, western blotting (WB), histological staining, transmission electron microscopy (TEM), and transfection. Sankey diagram was used to visualize the relationships between the expression level of SLC3A2 in the damaged area and clinical factors. Based on bioinformatics analysis, clinical factors, and experiment validation, SLC3A2 was identified as a hub gene. It was down-regulated in OA cartilage compared to normal cartilage (p < 0.05). Functional enrichment analysis revealed that SLC3A2 was associated with ferroptosis-related functions. Spearman correlation analysis showed that the expression level of SLC3A2 in the OA cartilage-damaged area was closely related to BMI, obesity grade, and Kellgren-Lawrence grade. Furthermore, in vitro experiments validated that SLC3A2 inhibited ferroptosis and suppressed cartilage degeneration in OA. In summary, we demonstrated that SLC3A2 inhibited ferroptosis and suppressed cartilage degeneration in OA. These findings provide a new idea for the study of the pathogenesis of OA, thus providing new means for the clinical diagnosis and targeted therapy of OA.