Identification of Mitochondrial-Lipid Metabolism-Related Diagnostic Targets in Osteoarthritis: Integrative Multi-omics Data and Experimental Validation
Huang, M.; Tan, X.; Yang, J.-B.; Yang, L.-D.; Yang, J.
Show abstract
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.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- An ex vivo tissue model of cartilage degradation suggests that cartilage state can be determined from secreted key protein patterns 95%
- RORβ modulates a gene program that is protective against articular cartilage damage 95%
- Detection of apoptosis and matrical degeneration within the intervertebral discs of rats due to passive cigarette smoking. 93%
Similar papers in this journal
- Transcriptional response of human articular chondrocytes treated with fibronectin fragments: an in vitro model of the osteoarthritis phenotype 94%
- Joint Instability Causes Catabolic Enzyme Production in Chondrocytes prior to Synovial Cells in Novel Non-Invasive ACL ruptured Mouse Model 94%
- Mechanical loading rescues mechanoresponsiveness in a human osteoarthritis explant model despite Wnt activation 94%
Similar papers in this journal
- Down-regulated GAS6 impairs synovial macrophage efferocytosis and promotes obesity-associated osteoarthritis 96%
- Cbfβ regulates Wnt/β-catenin, Hippo/Yap, and TGFβ signaling pathways in articular cartilage homeostasis and protects from ACLT surgery-induced osteoarthritis 96%
- Aberrant methylation and expression of TNXB promote chondrocyte apoptosis and extracullar matrix degradation in hemophilic arthropathy via AKT signaling 95%
Similar papers in this journal
- Mast cells differentiated in synovial fluid and resident in osteophytes exalt the inflammatory pathology of osteoarthritis 95%
- Transcriptomic changes during the replicative senescence of human articular chondrocytes 94%
- Joint degeneration in a mouse model of pseudoachondroplasia: ER stress, inflammation and autophagy blockage 93%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.