Bioinformatics analysis of differentially expressed genes in subchondral bone in early experimental osteoarthritis using microarray data

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.

Silencing BLNK protects against interleukin-1β-induced chondrocyte injury through the NF-κB signaling pathway

BACKGROUND

Osteoarthritis (OA) is the most common joint disease in the elderly and is characterized by the progressive degeneration of articular cartilage. It is necessary to study the molecular pathology of OA. This study aimed to explore the role and mechanism of BLNK in regulating interleukin-1β (IL-1β)-induced chondrocyte injury and OA progression.

METHODS

GSE1919 (5 normal samples and 5 OA samples) was downloaded from the Gene Expression Omnibus (GEO) database. The limma package in R software was used to identify differentially expressed genes (DEGs) between control and OA-affected cartilage. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the differentially expressed genes were also performed. Apoptosis was assessed by flow cytometry. An OA rat model was established, and the relative expression of BLNK was assessed by real time quantitative PCR (qRT-PCR) and immunohistochemical staining. The expression of collagen II, MMP9, p65 and p-p65 was measured by Western blot analysis. Moreover, inflammatory factors (TNF-α and IL-18) were assessed by ELISA. The NF-κB inhibitor JSH-23 was used to assess the impact of BLNK on the NF-κB signaling pathway.

RESULTS

In total, 1318 DEGs were identified between normal and OA-affected cartilage according to the criteria (P-value <0.05 and |logFC > 1|). These DEGs were mainly enriched in the NF-κB pathway. BLNK was highly expressed in OA cartilage tissue and injured chondrocytes. Silencing BLNK significantly downregulated the IL-1β-induced apoptosis of chondrocytes. Silencing BLNK partially increased collagen II expression and downregulated MMP13 expression. Moreover, silencing BLNK partially decreased TNF-α and IL-18 expression. BLNK silencing inhibited the activation of NF-κB in OA. Silencing BLNK delayed OA progression through the NF-κB signaling pathway.

CONCLUSION

Silencing BLNK delayed OA progression and IL-1β-induced chondrocyte injury by regulating the NF-κB pathway.