Integrated Analysis of miRNAs and Gene Expression Profiles Reveals Potential Biomarkers for Osteoarthritis

Transcriptomic analyses and machine-learning methods reveal dysregulated key genes and potential pathogenesis in human osteoarthritic cartilage

Aims

This study aimed to explore the biological and clinical importance of dysregulated key genes in osteoarthritis (OA) patients at the cartilage level to find potential biomarkers and targets for diagnosing and treating OA.

Methods

Six sets of gene expression profiles were obtained from the Gene Expression Omnibus database. Differential expression analysis, weighted gene coexpression network analysis (WGCNA), and multiple machine-learning algorithms were used to screen crucial genes in osteoarthritic cartilage, and genome enrichment and functional annotation analyses were used to decipher the related categories of gene function. Single-sample gene set enrichment analysis was performed to analyze immune cell infiltration. Correlation analysis was used to explore the relationship among the hub genes and immune cells, as well as markers related to articular cartilage degradation and bone mineralization.

Results

A total of 46 genes were obtained from the intersection of significantly upregulated genes in osteoarthritic cartilage and the key module genes screened by WGCNA. Functional annotation analysis revealed that these genes were closely related to pathological responses associated with OA, such as inflammation and immunity. Four key dysregulated genes (cartilage acidic protein 1 (CRTAC1), iodothyronine deiodinase 2 (DIO2), angiopoietin-related protein 2 (ANGPTL2), and MAGE family member D1 (MAGED1)) were identified after using machine-learning algorithms. These genes had high diagnostic value in both the training cohort and external validation cohort (receiver operating characteristic > 0.8). The upregulated expression of these hub genes in osteoarthritic cartilage signified higher levels of immune infiltration as well as the expression of metalloproteinases and mineralization markers, suggesting harmful biological alterations and indicating that these hub genes play an important role in the pathogenesis of OA. A competing endogenous RNA network was constructed to reveal the underlying post-transcriptional regulatory mechanisms.

Conclusion

The current study explores and validates a dysregulated key gene set in osteoarthritic cartilage that is capable of accurately diagnosing OA and characterizing the biological alterations in osteoarthritic cartilage; this may become a promising indicator in clinical decision-making. This study indicates that dysregulated key genes play an important role in the development and progression of OA, and may be potential therapeutic targets.

MiR-203a-3p attenuates apoptosis and pyroptosis of chondrocytes by regulating the MYD88/NF-κB pathway to alleviate osteoarthritis progression

BACKGROUND

Osteoarthritis (OA) is a degenerative joint disease that imposes a significant socioeconomic burden worldwide. Our previous studies revealed a down-regulation of miR-203a-3p in the knee tissues of OA patients. However, the underlying mechanism through which miR-203a-3p mediates the pathological process of OA remains unknown. Thus, we aimed to determine the effects of miR-203a-3p in the progression of OA.

METHODS

Rat primary chondrocytes were stimulated with 10 μg/mL lipopolysaccharide (LPS) for 24 hours, followed by transfection with 50 nM miR-203a-3p mimic, inhibitor, and siRNA for MYD88 or consistent negative controls for 48 hours. To evaluate the effects of miR-203a-3p on cartilage matrix degradation, oxidative stress, apoptosis, and pyroptosis in chondrocytes, various techniques such as immunofluorescence staining, biochemical analysis, Western blotting, and the TUNEL staining were utilized. In the rat OA model, all rats were randomly divided into four groups: Sham, OA, OA+Agomir negative control (NC), and OA+Agomir. They received intra-articular injections of 25 nmol miR-203a-3p agomir, agomir NC, or normal saline twice a week for the duration of 8 weeks after OA induction. Immunofluorescence staining was performed to evaluate the effects of miR-203a-3p on cartilage matrix degradation in rats.

RESULTS

MiR-203a-3p was down-regulated in LPS-treated rat chondrocytes and OA cartilage, and directly targeted MYD88. Moreover, miR-203a-3p significantly inhibited LPS-induced cartilage matrix degradation, oxidative stress, apoptosis, and pyroptosis of chondrocytes via targeting MYD88. Mechanistically, miR-203a-3p exerted protective effects via the inhibition of the MYD88/NF-κB pathway. In the rat OA model, intra-articular injections of miR-203a-3p agomir also significantly inhibited cartilage matrix degradation, thereby alleviating OA progression. Furthermore, the miR-203a-3p agomir-treated arthritic rat dramatically exhibited better articular tissue morphology and lower OARSI scores.

CONCLUSIONS

MiR-203a-3p plays a role in alleviating the progression of OA by regulating the MYD88/NF-κB pathway, thereby inhibiting cartilage matrix degradation, oxidative stress, apoptosis, and pyroptosis of chondrocytes. It highlights the potential significance of miR-203a-3p as an important regulator of OA.

MicroRNA expression in osteoarthritis: a meta-analysis

Osteoarthritis (OA) is one of the most prevalent musculoskeletal diseases globally, leading to chronic disability and poor prognosis. One of the approaches for optimizing OA treatment is to find early effective diagnostic biomarkers. The contribution of microRNAs (miRNAs) in OA progression is now being increasingly recognized. This review provides a comprehensive summary on studies reporting the expression profiling of miRNAs in OA and associated signaling pathways. We performed a systematic search of the Embase, Web of Science, PubMed, and Cochrane library databases. This systematic review is reported according to the PRISMA checklist. Studies which identified miRNAs with aberrant expression compared to controls during OA progression were included, and a meta-analysis was performed. Results from the random effects model were provided as log10 odds ratios (logORs) and 95% confidence intervals. Sensitivity analysis was conducted to confirm the accuracy of the results. Subgroup analysis was conducted based on tissue source. The target genes of miRNAs identified in this study were extracted from the MiRWalk database, and these target genes were enriched in Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. A total of 191 studies reporting 162 miRNAs were included in our meta-analysis. Among them, 36 miRNAs distributed across 96 studies were expressed in the same direction in at least two studies (13 up-regulated and 23 down-regulated). Subgroup analysis of tissue source revealed that the highest number of studies was conducted using articular cartilage, where the most up-regulated miRNAs were miR-146a-5p (logOR 7.355; P < 0.001) and miR-34a-5p (logOR 6.955; P < 0.001), and the most down-regulated miRNAs were miR-127-5p (logOR 6.586; P < 0.001) and miR-140-5p (logOR 6.373; P < 0.001). Enrichment analysis of 752 downstream target genes of all identified miRNAs was performed, and the regulatory relationships among them were displayed. Mesenchymal stem cells and transforming growth factor-β were found to be the most important downstream effectors regulated by miRNA in OA. This study highlighted the importance of miRNA signaling in OA progression and identified a number of prominent miRNAs including miR-146a-5p, miR-34a-5p, miR-127-5p, and miR-140-5p which might be considered as potential biomarkers for OA.

Exploring the Feasibility of Circulating miRNAs as Diagnostic and Prognostic Biomarkers in Osteoarthritis: Challenges and Opportunities

Osteoarthritis (OA) is a prevalent degenerative joint disease characterized by progressive cartilage degradation and joint inflammation. As the most common aging-related joint disease, OA is marked by inadequate extracellular matrix synthesis and the breakdown of articular cartilage. However, traditional diagnostic methods for OA, relying on clinical assessments and radiographic imaging, often need to catch up in detecting early-stage disease or i accurately predicting its progression. Consequently, there is a growing interest in identifying reliable biomarkers that can facilitate early diagnosis and prognosis of OA. MicroRNAs (miRNAs) have emerged as potential candidates due to their involvement in various cellular processes, including cartilage homeostasis and inflammation. This review explores the feasibility of circulating miRNAs as diagnostic and prognostic biomarkers in OA, focusing on knee OA while shedding light on the challenges and opportunities associated with their implementation in clinical practice.

Identification of susceptibility modules and hub genes of osteoarthritis by WGCNA analysis

Osteoarthritis (OA) is a major cause of pain, disability, and social burden in the elderly throughout the world. Although many studies focused on the molecular mechanism of OA, its etiology remains unclear. Therefore, more biomarkers need to be explored to help early diagnosis, clinical outcome measurement, and new therapeutic target development. Our study aimed to retrieve the potential hub genes of osteoarthritis (OA) by weighted gene co-expression network analysis (WGCNA) and assess their clinical utility for predicting OA. Here, we integrated WGCNA to identify novel OA susceptibility modules and hub genes. In this study, we first selected 477 and 834 DEGs in the GSE1919 and the GSE55235 databases, respectively, from the Gene Expression Omnibus (GEO) website. Genes with p-value&lt;0.05 and | log2FC | &gt; 1 were included in our analysis. Then, WGCNA was conducted to build a gene co-expression network, which filtered out the most relevant modules and screened out 23 overlapping WGCNA-derived hub genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses elucidated that these hub genes were associated with cell adhesion molecules pathway, leukocyte activation, and inflammatory response. In addition, we conducted the protein–protein interaction (PPI) network in 23 hub genes, and the top four upregulated hub genes were sorted out (CD4, SELL, ITGB2, and CD52). Moreover, our nomogram model showed good performance in predicting the risk of OA (C-index = 0.76), and this model proved to be efficient in diagnosis by ROC curves (AUC = 0.789). After that, a single-sample gene set enrichment (ssGSEA) analysis was performed to discover immune cell infiltration in OA. Finally, human primary synoviocytes and immunohistochemistry study of synovial tissues confirmed that those candidate genes were significantly upregulated in the OA groups compared with normal groups. We successfully constructed a co-expression network based on WGCNA and found out that OA-associated susceptibility modules and hub genes, which may provide further insight into the development of pre-symptomatic diagnosis, may contribute to understanding the molecular mechanism study of OA risk genes.