Research Article Integrative Transcriptomics Identifies Diagnostic Biomarkers Related to Adenosine Metabolism and Their Molecular Mechanisms in Knee Osteoarthritis
by Wenwen Deng1, Yueran Zhang2, LingYan Zhou1, Zhi Xiao1, Lili Peng1, Du Chen1*
1The Third Hospital of Changsha (Changsha Hospital Affiliated to Hunan University), People’s Republic of China
2Chinese Academy of Medical Sciences & Peking Union Medical College, China
*Corresponding author: Du Chen, The Third Hospital of Changsha (Changsha Hospital Affiliated to Hunan University), Changsha City, Hunan Province, 410015, People’s Republic of China
Received Date: 09 February 2026
Accepted Date: 23 February, 2026
Published Date: 27 February, 2026
Citation: Deng W, Zhang Y, Zhou LY, Xiao Z, Peng L, Chen D (2026) Integrative Transcriptomics Identifies Diagnostic Biomarkers Related to Adenosine Metabolism and Their Molecular Mechanisms in Knee Osteoarthritis. J Orthop Res Ther 11: 1419. https://doi.org/10.29011/2575-8241.001419
Abstract
Background: Knee osteoarthritis (KOA) is a common degenerative joint disease in middle-aged and elderly populations, characterized by cartilage degradation and synovial inflammation. Adenosine (ADO) plays a crucial role in the pathogenesis of KOA. Based on adenosine metabolism-related genes (ADO-MRGs), this study screened KOA-related biomarkers through bioinformatics analysis, aiming to provide new references for the diagnosis and treatment of KOA. Methods: In this study, two transcriptomic datasets (GSE51588 and GSE114007) were analysed, and 22 ADO-MRGs were extracted from the literature. Biomarkers were screened using methods including differential expression analysis, machine learning, and expression validation. A neural network was constructed to evaluate the predictive ability of the biomarkers. Furthermore, a series of analyses including immune infiltration, chromosomal localization, Gene Set Enrichment Analysis (GSEA), drug prediction, and molecular docking- were conducted to comprehensively explore their biological functions and clinical translational potential. Results: PNP and NT5E were identified as biomarkers, with PNP significantly downregulated and NT5E significantly upregulated in KOA, respectively. The neural network model exhibited favorable predictive performance. Immune infiltration analysis revealed that PNP was positively correlated with activated CD4⁺ T cells and activated dendritic cells, while NT5E was negatively correlated with myeloid-derived suppressor cells and neutrophils. GSEA results showed that PNP was significantly enriched in metabolism-related pathways such as oxidative phosphorylation and proteasome, and NT5E was enriched in pathways including TGF-β signalling pathway and ECM-receptor interaction. In drug prediction, multiple drugs (e.g., oleclumab, forodesine hydrochloride) showed high binding affinity with PNP/NT5E (binding energy < -5 kcal/mol), indicating their potential targeting value. Conclusion: This study identified PNP and NT5E as biomarkers for KOA associated with adenosine metabolism. Both may play a critical role in the pathogenesis of KOA, providing new research directions and theoretical support for its early identification and targeted intervention.
Keywords: Knee osteoarthritis, Adenosine metabolism, Biomarkers, Machine learning, Immune infiltration
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