Introduction:
The aim of this study was to explore the immune-related competitive endogenous RNA (ceRNA) network in osteoarthritis (OA), focusing on identifying differentially expressed long non-coding RNAs (lncRNAs), constructing a classification model, and uncovering the associations between these lncRNAs and immune cell subsets in OA.

Material and methods:
Microarray data from the Gene Expression Omnibus (GEO) database was used to identify differentially expressed genes (DEGs) in synovial tissue of OA. A classification model was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression with selected lncRNAs. Computational methods like CIBERSORT were employed to quantify immune cell infiltration patterns, and weighted gene co-expression network analysis (WGCNA) was conducted to delineate co-expression modules. Finally, a ceRNA network was constructed to elucidate the regulatory interactions among lncRNAs, miRNAs, and mRNAs.

Results:
We identified 5927 DEGs, among which 47 were differentially expressed long non-coding RNAs (DELs). Seven DELs (DGCR11, FAM215A, HCG9, PART1, FAM106A, NOP14-AS1, PRORY) formed the basis of a classification model with an area under the receiver operating characteristic (ROC) curve of 1. We also examined the infiltration patterns of immune cells in OA tissues and found significant differences compared to healthy controls, indicating a strong immunological component in OA pathogenesis. WGCNA identified a turquoise module with a high correlation to OA traits.

Conclusions:
The study highlights the importance of ceRNA networks in understanding the complex pathogenesis of OA and offers a comprehensive framework for future research into potential diagnostic biomarkers and therapeutic targets.