Identification and validation of biomarkers related to lipid metabolism in osteoarthritis based on machine learning algorithms
Abstract Background Osteoarthritis and lipid metabolism are strongly associated, although the precise targets and regulatory mechanisms are unknown. Methods Osteoarthritis gene expression profiles were acquired from the GEO database, while lipid metabolism-related genes (LMRGs) were sourced from the...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-04-01
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| Series: | Lipids in Health and Disease |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12944-024-02073-5 |
| _version_ | 1797199185604247552 |
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| author | Hang Li Yubao Cui Jian Wang Wei Zhang Yuhao Chen Jijun Zhao |
| author_facet | Hang Li Yubao Cui Jian Wang Wei Zhang Yuhao Chen Jijun Zhao |
| author_sort | Hang Li |
| collection | DOAJ |
| description | Abstract Background Osteoarthritis and lipid metabolism are strongly associated, although the precise targets and regulatory mechanisms are unknown. Methods Osteoarthritis gene expression profiles were acquired from the GEO database, while lipid metabolism-related genes (LMRGs) were sourced from the MigSB database. An intersection was conducted between these datasets to extract gene expression for subsequent differential analysis. Following this, functional analyses were performed on the differentially expressed genes (DEGs). Subsequently, machine learning was applied to identify hub genes associated with lipid metabolism in osteoarthritis. Immune-infiltration analysis was performed using CIBERSORT, and external datasets were employed to validate the expression of these hub genes. Results Nine DEGs associated with lipid metabolism in osteoarthritis were identified. UGCG and ESYT1, which are hub genes involved in lipid metabolism in osteoarthritis, were identified through the utilization of three machine learning algorithms. Analysis of the validation dataset revealed downregulation of UGCG in the experimental group compared to the normal group and upregulation of ESYT1 in the experimental group compared to the normal group. Conclusions UGCG and ESYT1 were considered as hub LMRGs in the development of osteoarthritis, which were regarded as candidate diagnostic markers. The effects are worth expected in the early diagnosis and treatment of osteoarthritis. |
| first_indexed | 2024-04-24T07:11:44Z |
| format | Article |
| id | doaj.art-4d36ca3b6d754a0e9239d8baf9042035 |
| institution | Directory Open Access Journal |
| issn | 1476-511X |
| language | English |
| last_indexed | 2024-04-24T07:11:44Z |
| publishDate | 2024-04-01 |
| publisher | BMC |
| record_format | Article |
| series | Lipids in Health and Disease |
| spelling | doaj.art-4d36ca3b6d754a0e9239d8baf90420352024-04-21T11:28:45ZengBMCLipids in Health and Disease1476-511X2024-04-0123111010.1186/s12944-024-02073-5Identification and validation of biomarkers related to lipid metabolism in osteoarthritis based on machine learning algorithmsHang Li0Yubao Cui1Jian Wang2Wei Zhang3Yuhao Chen4Jijun Zhao5Wuxi Medical Center, Nanjing Medical UniversityClinical Research Center, The Affiliated Wuxi People’s Hospital of Nanjing Medical UniversityWuxi Medical Center, Nanjing Medical UniversityWuxi Medical Center, Nanjing Medical UniversityWuxi Medical Center, Nanjing Medical UniversityDepartment of Orthopedic, The Affiliated Wuxi People’s Hospital of Nanjing Medical UniversityAbstract Background Osteoarthritis and lipid metabolism are strongly associated, although the precise targets and regulatory mechanisms are unknown. Methods Osteoarthritis gene expression profiles were acquired from the GEO database, while lipid metabolism-related genes (LMRGs) were sourced from the MigSB database. An intersection was conducted between these datasets to extract gene expression for subsequent differential analysis. Following this, functional analyses were performed on the differentially expressed genes (DEGs). Subsequently, machine learning was applied to identify hub genes associated with lipid metabolism in osteoarthritis. Immune-infiltration analysis was performed using CIBERSORT, and external datasets were employed to validate the expression of these hub genes. Results Nine DEGs associated with lipid metabolism in osteoarthritis were identified. UGCG and ESYT1, which are hub genes involved in lipid metabolism in osteoarthritis, were identified through the utilization of three machine learning algorithms. Analysis of the validation dataset revealed downregulation of UGCG in the experimental group compared to the normal group and upregulation of ESYT1 in the experimental group compared to the normal group. Conclusions UGCG and ESYT1 were considered as hub LMRGs in the development of osteoarthritis, which were regarded as candidate diagnostic markers. The effects are worth expected in the early diagnosis and treatment of osteoarthritis.https://doi.org/10.1186/s12944-024-02073-5OsteoarthritisLipid metabolismBioinformatics analysisHub genesMachine learningImmune infiltration |
| spellingShingle | Hang Li Yubao Cui Jian Wang Wei Zhang Yuhao Chen Jijun Zhao Identification and validation of biomarkers related to lipid metabolism in osteoarthritis based on machine learning algorithms Lipids in Health and Disease Osteoarthritis Lipid metabolism Bioinformatics analysis Hub genes Machine learning Immune infiltration |
| title | Identification and validation of biomarkers related to lipid metabolism in osteoarthritis based on machine learning algorithms |
| title_full | Identification and validation of biomarkers related to lipid metabolism in osteoarthritis based on machine learning algorithms |
| title_fullStr | Identification and validation of biomarkers related to lipid metabolism in osteoarthritis based on machine learning algorithms |
| title_full_unstemmed | Identification and validation of biomarkers related to lipid metabolism in osteoarthritis based on machine learning algorithms |
| title_short | Identification and validation of biomarkers related to lipid metabolism in osteoarthritis based on machine learning algorithms |
| title_sort | identification and validation of biomarkers related to lipid metabolism in osteoarthritis based on machine learning algorithms |
| topic | Osteoarthritis Lipid metabolism Bioinformatics analysis Hub genes Machine learning Immune infiltration |
| url | https://doi.org/10.1186/s12944-024-02073-5 |
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