Exploring Endoplasmic Reticulum Stress-Related Genes in Cartilage Defects: Implications for Diagnosis and Therapy.

INTRODUCTION

Cartilage defects (CDs) are orthopedic conditions with limited regenerative potential. This study aimed to identify endoplasmic reticulum (ER) stress-related biomarkers and construct a diagnostic model to enhance the early detection of CD.

METHODS

This study analyzed the transcriptomic dataset GSE129147 to identify ER stressrelated differentially expressed genes (ERSRDEGs) between CD and control tissues using the limma package (version 3.58.1). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were employed for functional enrichment. Immune infiltration was assessed using cell-type identification, which involved estimating the relative subsets of RNA transcripts and single-sample gene set enrichment analysis. Diagnostic models were constructed using logistic regression, support vector machine, and least absolute shrinkage and selection operator regression.

RESULTS

Twenty ERSRDEGs were identified, with CYBB, ATP6V1A, and TNFRSF12A significantly upregulated in CD samples. GO and KEGG analyses highlighted oxidative stress response and extracellular matrix remodeling as key mechanisms in CD pathogenesis. Immune analysis revealed an increase in regulatory T cells and a reduction in CD8. T cells. TNFRSF12A showed strong immune associations and, together with TWIST1 and ATP6V1A, formed the final preliminary diagnostic model. The preliminary LASSO model achieved satisfactory predictive accuracy (AUC: 0.7-0.9).

DISCUSSION: These findings suggest that ER stress and immune imbalance jointly contribute to cartilage degeneration. The identified genes, particularly TNFRSF12A, TWIST1, and ATP6V1A, not only serve as potential biomarkers but also provide preliminary evidence for new mechanistic insights into stress-immune crosstalk in CD.

CONCLUSION

This study reveals the key roles of ER stress and immune dysregulation in CDs. Moreover, the ERSRDEG-based diagnostic model provides preliminary bioinformatics evidence and potential molecular indicators for targeted diagnostics and therapies.