Identification of CIRBP and TRPV4 as Immune-Related Diagnostic Biomarkers in Osteoarthritis

RNA-binding proteins regulate osteoarthritis via RNA metabolism regulation

Osteoarthritis (OA) is a common chronic degenerative disease of the skeletal system and muscular system, and its pathogenesis remains unclear, leading to a lack of effective therapeutic strategies. Ribonucleic acid binding proteins (RBP), as key regulators of post-transcriptional processes, can specifically bind to targeted ribonucleic acids (RNA) and modulate their function and fate. By regulating various aspects of RNA metabolism, including transcription, splicing, modification, stabilization, and translation, RBPs influence the onset and progression of OA. Exploring the regulatory mechanisms of RBPs under physiological and pathological conditions, elucidating the role of RBPs in the occurrence and development of OA, and discussing current challenges and future directions in RBPs research, hold significant importance for the treatment of OA by targeting RBPs.

Innate Immunity and Synovitis: Key Players in Osteoarthritis Progression

Osteoarthritis (OA) is a chronic progressive disease of the joint. Although representing the most frequent cause of disability in the elderly, OA remains partly obscure in its pathogenic mechanisms and is still the orphan of resolutive therapies. The concept of what was once considered a "wear and tear" of articular cartilage is now that of an inflammation-related disease that affects over time the whole joint. The attention is increasingly focused on the synovium. Even from the earliest clinical stages, synovial inflammation (or synovitis) is a crucial factor involved in OA progression and a major player in pain onset. The release of inflammatory molecules in the synovium mediates disease progression and worsening of clinical features. The activation of synovial tissue-resident cells recalls innate immunity cells from the bloodstream, creating a proinflammatory milieu that fuels and maintains a damaging condition of low-grade inflammation in the joint. In such a context, cellular and molecular inflammatory behaviors in the synovium could be the primum movens of the structural and functional alterations of the whole joint. This paper focuses on and discusses the involvement of innate immunity cells in synovitis and their role in the progression of OA.

Identification of HTRA1, DPT and MXRA5 as potential biomarkers associated with osteoarthritis progression and immune infiltration

BACKGROUND

Our study aimed to identify potential specific biomarkers for osteoarthritis (OA) and assess their relationship with immune infiltration.

METHODS

We utilized data from GSE117999, GSE51588, and GSE57218 as training sets, while GSE114007 served as a validation set, all obtained from the GEO database. First, weighted gene co-expression network analysis (WGCNA) and functional enrichment analysis were performed to identify hub modules and potential functions of genes. We subsequently screened for potential OA biomarkers within the differentially expressed genes (DEGs) of the hub module using machine learning methods. The diagnostic accuracy of the candidate genes was validated. Additionally, single gene analysis and ssGSEA was performed. Then, we explored the relationship between biomarkers and immune cells. Lastly, we employed RT-PCR to validate our results.

RESULTS

WGCNA results suggested that the blue module was the most associated with OA and was functionally associated with extracellular matrix (ECM)-related terms. Our analysis identified ALB, HTRA1, DPT, MXRA5, CILP, MPO, and PLAT as potential biomarkers. Notably, HTRA1, DPT, and MXRA5 consistently exhibited increased expression in OA across both training and validation cohorts, demonstrating robust diagnostic potential. The ssGSEA results revealed that abnormal infiltration of DCs, NK cells, Tfh, Th2, and Treg cells might contribute to OA progression. HTRA1, DPT, and MXRA5 showed significant correlation with immune cell infiltration. The RT-PCR results also confirmed these findings.

CONCLUSIONS

HTRA1, DPT, and MXRA5 are promising biomarkers for OA. Their overexpression strongly correlates with OA progression and immune cell infiltration.

Identification of four-gene signature to diagnose osteoarthritis through bioinformatics and machine learning methods

BACKGROUND

Although osteoarthritis (OA) is one of the most prevalent joint disorders, effective biomarkers to diagnose OA are still unavailable. This study aimed to acquire some key synovial biomarkers (hub genes) and analyze their correlation with immune infiltration in OA.

METHODS

Gene expression profiles and clinical characteristics of OA and healthy synovial samples were retrieved from the Gene Expression Omnibus (GEO) database. Hub genes for OA were mined based on a combination of weighted gene co-expression network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) algorithms. A diagnostic nomogram model for OA prediction was developed based on the hub genes. Receiver operating characteristic curves (ROC) were performed to confirm the abnormal expression of hub genes in the experimemtal and validation datasets. qRT-PCR using patients' samples were conducted as well. In addition, the infiltration level of 28 immune cells in the expression profile and their relationship with hub genes were analyzed using single-sample GSEA (ssGSEA).

RESULTS

4 hub genes (ZBTB16, TNFSF11, SCRG1 and KDELR3) were obtained by WGCNA, lasso, SVM-RFE, RF algorithms as potential biomarkers for OA. The immune infiltration analyses revealed that hub genes were most correlated with regulatory T cell and natural killer cell.

CONCLUSION

A machine learning model to diagnose OA based on ZBTB16, TNFSF11, SCRG1 and KDELR3 using synovial tissue was constructed, providing theoretical foundation and guideline for diagnostic and treatment targets in OA.

RNA-binding proteins in degenerative joint diseases: A systematic review

RNA-binding proteins (RBPs), which are conserved proteins comprising multiple intermediate sequences, can interact with proteins, messenger RNA (mRNA) of coding genes, and non-coding RNAs to perform different biological functions, such as the regulation of mRNA stability, selective polyadenylation, and the management of non-coding microRNA (miRNA) synthesis to affect downstream targets. This article will highlight the functions of RBPs, in degenerative joint diseases (intervertebral disc degeneration [IVDD] and osteoarthritis [OA]). It will reviews the latest advancements on the regulatory mechanism of RBPs in degenerative joint diseases, in order to understand the pathophysiology, early diagnosis and treatment of OA and IVDD from a new perspective.

Expression patterns of eight RNA-modified regulators correlating with immune infiltrates during the progression of osteoarthritis

Background

RNA modifications in eukaryotic cells have emerged as an exciting but under-explored area in recent years and are considered to be associated with many human diseases. While several studies have been published relating to m6A in osteoarthritis (OA), we only have limited knowledge of other kinds of RNA modifications. Our study investigated eight RNA modifiers' specific roles in OA including A-to-I, APA, m5C, m6A, m7G, mcm5s2U, Nm and Ψ together with their relationship with immune infiltration.

Methods

RNA modification patterns in OA samples were identified based on eight-type RNA modifiers and their correlation with the degree of immune infiltration was also methodically investigated. Receiver operating characteristic curves (ROC) and qRT-PCR was performed to confirm the abnormal expression of hub genes. The RNA modification score (Rmscore) was generated by the applications of principal component analysis (PCA) algorithm in order to quantify RNA modification modes in individual OA patients.

Results

We identified 21 differentially-expressed RNA modification related genes between OA and healthy samples. For example, CFI, CBLL1 and ALKBH8 were expressed at high levels in OA (P<0.001), while RPUSD4, PUS1, NUDT21, FBL and WDR4 were expressed at low levels (P<0.001). Two candidate RNA modification regulators (WDR4 and CFI) were screened out utilizing a random forest machine learning model. We then identified two distinctive RNA modification modes in OA which were found to display distinctive biological features. High Rmscore, characterized by increased immune cell infiltration, indicated an inflamed phenotype.

Conclusions

Our study was the first to systematically reveal the crosstalk and dysregulations eight-type of RNA modifications in OA. Assessing individuals' RNA modification patterns will be conductive to enhance our understanding of the properties of immune infiltration, provide novel diagnostic and prognostic biomarkers, and guide more effective immunotherapy strategies in the future.

Timing Expression of miR203a-3p during OA Disease: Preliminary In Vitro Evidence

Osteoarthritis (OA) is a degenerative bone disease that involves the microenvironment and macroenvironment of joints. Progressive joint tissue degradation and loss of extracellular matrix elements, together with different grades of inflammation, are important hallmarks of OA disease. Therefore, the identification of specific biomarkers to distinguish the stages of disease becomes a primary necessity in clinical practice. To this aim, we investigated the role of miR203a-3p in OA progression starting from the evidence obtained by osteoblasts isolated from joint tissues of OA patients classified according to different Kellgren and Lawrence (KL) grading (KL ≤ 3 and KL > 3) and hMSCs treated with IL-1β. Through qRT-PCR analysis, it was found that osteoblasts (OBs) derived from the KL ≤ 3 group expressed high levels of miR203a-3p and low levels of ILs compared with those of OBs derived from the KL > 3 group. The stimulation with IL-1β improved the expression of miR203a-3p and the methylation of the IL-6 promoter gene, favoring an increase in relative protein expression. The gain and loss of function studies showed that the transfection with miR203a-3p inhibitor alone or in co-treatments with IL-1β was able to induce the expression of CX-43 and SP-1 and to modulate the expression of TAZ, in OBs derived from OA patients with KL ≤ 3 compared with KL > 3. These events, confirmed also by qRT-PCR analysis, Western blot, and ELISA assay performed on hMSCs stimulated with IL-1β, supported our hypothesis about the role of miR203a-3p in OA progression. The results suggested that during the early stage, miR203a-3p displayed a protective role reducing the inflammatory effects on CX-43, SP-1, and TAZ. During the OA progression the downregulation of miR203a-3p and consequently the upregulation of CX-43/SP-1 and TAZ expression improved the inflammatory response and the reorganization of the cytoskeleton. This role led to the subsequent stage of the disease, where the aberrant inflammatory and fibrotic responses determined the destruction of the joint.

Cross-Tissue Analysis Using Machine Learning to Identify Novel Biomarkers for Knee Osteoarthritis

Background

Knee osteoarthritis (KOA) is a common degenerative joint disease. In this study, we aimed to identify new biomarkers of KOA to improve the accuracy of diagnosis and treatment.

Methods

GSE98918 and GSE51588 were downloaded from the Gene Expression Omnibus database as training sets, with a total of 74 samples. Gene differences were analyzed by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway, and Disease Ontology enrichment analyses for the differentially expressed genes (DEGs), and GSEA enrichment analysis was carried out for the training gene set. Through least absolute shrinkage and selection operator regression analysis, the support vector machine recursive feature elimination algorithm, and gene expression screening, the range of DEGs was further reduced. Immune infiltration analysis was carried out, and the prediction results of the combined biomarker logistic regression model were verified with GSE55457.

Results

In total, 84 DEGs were identified through differential gene expression analysis. The five biomarkers that were screened further showed significant differences in cartilage, subchondral bone, and synovial tissue. The diagnostic accuracy of the model synthesized using five biomarkers through logistic regression was better than that of a single biomarker and significantly better than that of a single clinical trait.

Conclusions

CX3CR1, SLC7A5, ARL4C, TLR7, and MTHFD2 might be used as novel biomarkers to improve the accuracy of KOA disease diagnosis, monitor disease progression, and improve the efficacy of clinical treatment.