Construction of a glycolysis-related diagnostic model for osteoarthritis through integrated bioinformatics analysis and machine learning
Abstract Background Osteoarthritis (OA) is a prevalent degenerative joint disease that significantly contributes to global disability. Glycolysis, a fundamental process in cellular energy metabolism, is particularly vital for chondrocytes in OA. This study aims to explore the intrinsic relationship...
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| Format: | Article |
| Language: | English |
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BMC
2025-07-01
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| Series: | Journal of Orthopaedic Surgery and Research |
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| Online Access: | https://doi.org/10.1186/s13018-025-06072-9 |
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| author | Wangnan Mao Zhengsheng Bao Bingbing Zhang Lianguo Wu |
| author_facet | Wangnan Mao Zhengsheng Bao Bingbing Zhang Lianguo Wu |
| author_sort | Wangnan Mao |
| collection | DOAJ |
| description | Abstract Background Osteoarthritis (OA) is a prevalent degenerative joint disease that significantly contributes to global disability. Glycolysis, a fundamental process in cellular energy metabolism, is particularly vital for chondrocytes in OA. This study aims to explore the intrinsic relationship between glycolysis-related genes (GRGs) and OA. Methods We incorporated three publicly available datasets from the Gene Expression Omnibus (GEO) database, which included 64 OA samples and 34 normal controls. By utilizing differential expression analysis, weighted gene co-expression network analysis, protein-protein interaction networks, and machine learning methods, we identified three diagnostic biomarkers of OA patients. The expression levels of these biomarkers were validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and immunohistochemical (IHC). Additionally, a competing endogenous RNA (ceRNA) network was constructed to explore potential regulatory interactions. Results Through bioinformatics and machine learning approaches, three glycolysis-related biomarkers—HMGB2, SLC7A5, and ADM—were identified. The diagnostic model based on these GRGs demonstrated high predictive accuracy, with an AUC of 0.92 in the training set and 0.85 in the validation set. Subsequently, qRT-PCR and IHC confirmed the differential expression of hub genes in human cartilage samples. Furthermore, immunocyte infiltration analysis revealed distinct immune cell infiltration profiles between OA and HC groups. Notably, lncRNA XIST was found to regulate all three biomarkers, indicating its potential as a therapeutic target for OA. Conclusion This study provides novel insights into the role of glycolysis in OA pathogenesis and highlights its potential as a target for diagnosis, prevention, and treatment strategies. |
| format | Article |
| id | doaj-art-c93e9d4973a14d4c9280ea8d7d815bc8 |
| institution | Kabale University |
| issn | 1749-799X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Orthopaedic Surgery and Research |
| spelling | doaj-art-c93e9d4973a14d4c9280ea8d7d815bc82025-08-20T04:03:00ZengBMCJournal of Orthopaedic Surgery and Research1749-799X2025-07-0120111610.1186/s13018-025-06072-9Construction of a glycolysis-related diagnostic model for osteoarthritis through integrated bioinformatics analysis and machine learningWangnan Mao0Zhengsheng Bao1Bingbing Zhang2Lianguo Wu3The Second Clinical College, Zhejiang Chinese Medical UniversityDepartment of Orthopedic Surgery, The Second Affiliated Hospital of Zhejiang, Chinese Medical UniversityThe Second Clinical College, Zhejiang Chinese Medical UniversityThe Second Clinical College, Zhejiang Chinese Medical UniversityAbstract Background Osteoarthritis (OA) is a prevalent degenerative joint disease that significantly contributes to global disability. Glycolysis, a fundamental process in cellular energy metabolism, is particularly vital for chondrocytes in OA. This study aims to explore the intrinsic relationship between glycolysis-related genes (GRGs) and OA. Methods We incorporated three publicly available datasets from the Gene Expression Omnibus (GEO) database, which included 64 OA samples and 34 normal controls. By utilizing differential expression analysis, weighted gene co-expression network analysis, protein-protein interaction networks, and machine learning methods, we identified three diagnostic biomarkers of OA patients. The expression levels of these biomarkers were validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and immunohistochemical (IHC). Additionally, a competing endogenous RNA (ceRNA) network was constructed to explore potential regulatory interactions. Results Through bioinformatics and machine learning approaches, three glycolysis-related biomarkers—HMGB2, SLC7A5, and ADM—were identified. The diagnostic model based on these GRGs demonstrated high predictive accuracy, with an AUC of 0.92 in the training set and 0.85 in the validation set. Subsequently, qRT-PCR and IHC confirmed the differential expression of hub genes in human cartilage samples. Furthermore, immunocyte infiltration analysis revealed distinct immune cell infiltration profiles between OA and HC groups. Notably, lncRNA XIST was found to regulate all three biomarkers, indicating its potential as a therapeutic target for OA. Conclusion This study provides novel insights into the role of glycolysis in OA pathogenesis and highlights its potential as a target for diagnosis, prevention, and treatment strategies.https://doi.org/10.1186/s13018-025-06072-9OsteoarthritisGlycolysisBioinformatics analysisMachine learning algorithmsImmune cells |
| spellingShingle | Wangnan Mao Zhengsheng Bao Bingbing Zhang Lianguo Wu Construction of a glycolysis-related diagnostic model for osteoarthritis through integrated bioinformatics analysis and machine learning Journal of Orthopaedic Surgery and Research Osteoarthritis Glycolysis Bioinformatics analysis Machine learning algorithms Immune cells |
| title | Construction of a glycolysis-related diagnostic model for osteoarthritis through integrated bioinformatics analysis and machine learning |
| title_full | Construction of a glycolysis-related diagnostic model for osteoarthritis through integrated bioinformatics analysis and machine learning |
| title_fullStr | Construction of a glycolysis-related diagnostic model for osteoarthritis through integrated bioinformatics analysis and machine learning |
| title_full_unstemmed | Construction of a glycolysis-related diagnostic model for osteoarthritis through integrated bioinformatics analysis and machine learning |
| title_short | Construction of a glycolysis-related diagnostic model for osteoarthritis through integrated bioinformatics analysis and machine learning |
| title_sort | construction of a glycolysis related diagnostic model for osteoarthritis through integrated bioinformatics analysis and machine learning |
| topic | Osteoarthritis Glycolysis Bioinformatics analysis Machine learning algorithms Immune cells |
| url | https://doi.org/10.1186/s13018-025-06072-9 |
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