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Identification of Mitochondrial-Lipid Metabolism-Related Diagnostic Targets in Osteoarthritis: Integrative Multi-omics Data and Experimental Validation

Preprint Created on 22 Sep 2026 bioRxiv

Objective: Osteoarthritis (OA) is a degenerative joint disease associated with metabolic dysregulation. This study aims to identify mitochondrial-lipid metabolism-related hub genes and explore their mechanisms in OA. Methods: Training (GSE55235) and validation (GSE55457 and GSE82107) cohorts were retrieved from the Gene Expression Omnibus (GEO) database. Candidate genes were derived by overlapping differentially expressed genes, mitochondrial-lipid metabolism-related genes, and weighted gene co-expression network analysis module genes. Machine learning algorithms were applied to screen hub genes. The diagnostic performance, functional pathways, immune infiltration, regulatory networks, drug interactions, and molecular docking of the hub genes were systematically evaluated. An interleukin-1{beta}-stimulated chondrocyte OA model was assayed for cell viability, interleukin-6 (IL-6), apoptosis, and hub-gene expression via Cell Counting Kit-8, enzyme-linked immunosorbent assay, flow cytometry, and reverse transcription-polymerase chain reaction. Results: Twenty-five candidate genes were screened, from which six hub genes (alpha-methylacyl-CoA racemase [AMACR], uncoupling protein 2 [UCP2], acyl-CoA thioesterase 7 [ACOT7], aldehyde dehydrogenase 3 family member A2 [ALDH3A2], armadillo repeat containing X-linked 2 [ARMCX2], and monoamine oxidase A [MAOA]) were further identified. The six hub genes exhibited robust diagnostic ability in the training cohort, correlated with immune-cell infiltration, and participated in lipid-metabolism pathways. In the OA model, chondrocyte viability was reduced, while IL-6 levels and apoptosis rate were elevated; five hub genes were markedly upregulated, whereas MAOA was significantly downregulated. Conclusion: Mitochondrial-lipid metabolism-related hub genes may serve as potential diagnostic biomarkers for OA, providing new insights into OA pathogenesis and potential therapeutic targets.

Huang, M., Tan, X., Yang, J.-B., Yang, L.-D., Yang, J.

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