Identification of mitophagy-related biomarkers in osteoarthritis

Abstract

Background: Osteoarthritis (OA) is a common joint disease, and existing drugs cannot cure OA, so there is an urgent need to identify new targets. Mitophagy plays an important role in OA; however, the role of mitophagy in the OA immune system is not yet clear.

Methods: In this study, differential analysis and enrichment analysis were used to identify mitophagy-related genes (MRGs) with differential expression in OA and the functional pathways involved in OA. Subsequently, two machine learning methods, RF and LASSO, were used to screen MRGs with diagnostic value and construct nomograms. At the same time, the relationship between mitophagy and OA immune response was explored by immunoinfiltration analysis.

Results: Forty-three differentially MRGs were identified in OA, of which six MRGs (GABARAPL2, PARL, GABARAPL1, JUN, RRAS, and SNX7) were associated with the diagnosis of OA. The ROC analysis results show that these 6 MRGs have high predictive accuracy in the diagnosis of OA. In immune infiltration analysis, we found that the abundance of significantly different immune cells in OA was mostly upregulated. In addition, the expression of diagnostic-related MRGs is correlated with changes in the abundance of immune cells in OA.

Conclusion: This study demonstrates that six MRGs can be used as diagnostic biomarkers. The expression of diagnostic-related MRGs is correlated with changes in the abundance of immune cells in OA. At the same time, mitophagy may affect the immune microenvironment of OA by regulating immune cells, ultimately leading to the progression of OA.

Keywords

diagnosis / immune cell infiltration / machine learning / mitophagy / osteoarthritis (OA) / quantitative real-time polymerase chain reaction (qPCR)

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2024 The Authors. Animal Models and Experimental Medicine published by John Wiley & Sons Australia, Ltd on behalf of The Chinese Association for Laboratory Animal Sciences.