Unveiling the ageing-related genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning
Ageing significantly contributes to osteoarthritis (OA) and metabolic syndrome (MetS) pathogenesis, yet the underlying mechanisms remain unknown. This study aimed to identify ageing-related biomarkers in OA patients with MetS. OA and MetS datasets and ageing-related genes (ARGs) were retrieved from...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Artificial Cells, Nanomedicine, and Biotechnology |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/21691401.2025.2471762 |
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| author | Jian Huang Lu Wang Jiangfei Zhou Tianming Dai Weicong Zhu Tianrui Wang Hongde Wang Yingze Zhang |
| author_facet | Jian Huang Lu Wang Jiangfei Zhou Tianming Dai Weicong Zhu Tianrui Wang Hongde Wang Yingze Zhang |
| author_sort | Jian Huang |
| collection | DOAJ |
| description | Ageing significantly contributes to osteoarthritis (OA) and metabolic syndrome (MetS) pathogenesis, yet the underlying mechanisms remain unknown. This study aimed to identify ageing-related biomarkers in OA patients with MetS. OA and MetS datasets and ageing-related genes (ARGs) were retrieved from public databases. The limma package was used to identify differentially expressed genes (DEGs), and weighted gene coexpression network analysis (WGCNA) screened gene modules, and machine learning algorithms, such as random forest (RF), support vector machine (SVM), generalised linear model (GLM), and extreme gradient boosting (XGB), were employed. The nomogram and receiver operating characteristic (ROC) curve assess the diagnostic value, and CIBERSORT analysed immune cell infiltration. We identified 20 intersecting genes among DEGs of OA, key module genes of MetS, and ARGs. By comparing the accuracy of the four machine learning models for disease prediction, the SVM model, which includes CEBPB, PTEN, ARPC1B, PIK3R1, and CDC42, was selected. These hub ARGs not only demonstrated strong diagnostic values based on nomogram data but also exhibited a significant correlation with immune cell infiltration. Building on these findings, we have identified five hub ARGs that are associated with immune cell infiltration and have constructed a nomogram aimed at early diagnosing OA patients with MetS. |
| format | Article |
| id | doaj-art-533e91d3cef24146b01745a9d938ee6c |
| institution | OA Journals |
| issn | 2169-1401 2169-141X |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Artificial Cells, Nanomedicine, and Biotechnology |
| spelling | doaj-art-533e91d3cef24146b01745a9d938ee6c2025-08-20T02:38:13ZengTaylor & Francis GroupArtificial Cells, Nanomedicine, and Biotechnology2169-14012169-141X2025-12-01531576810.1080/21691401.2025.2471762Unveiling the ageing-related genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learningJian Huang0Lu Wang1Jiangfei Zhou2Tianming Dai3Weicong Zhu4Tianrui Wang5Hongde Wang6Yingze Zhang7Department of Orthopedics, The Affiliated Hospital of Qingdao University, Qingdao, ChinaDepartment of Neurology, The Central Hospital of Xiaogan, Xiaogan, ChinaDepartment of Orthopedics, Guangzhou Red Cross Hospital of Jinan University, Guangzhou, ChinaGuangzhou Institute of Traumatic Surgery, Guangzhou Red Cross Hospital of Jinan University, Guangzhou, ChinaGuangzhou Institute of Traumatic Surgery, Guangzhou Red Cross Hospital of Jinan University, Guangzhou, ChinaDepartment of Orthopedics, The Affiliated Hospital of Qingdao University, Qingdao, ChinaDepartment of Sports Medicine, Peking University Third Hospital, Institute of Sports Medicine of Peking University, Beijing, ChinaDepartment of Orthopedics, The Affiliated Hospital of Qingdao University, Qingdao, ChinaAgeing significantly contributes to osteoarthritis (OA) and metabolic syndrome (MetS) pathogenesis, yet the underlying mechanisms remain unknown. This study aimed to identify ageing-related biomarkers in OA patients with MetS. OA and MetS datasets and ageing-related genes (ARGs) were retrieved from public databases. The limma package was used to identify differentially expressed genes (DEGs), and weighted gene coexpression network analysis (WGCNA) screened gene modules, and machine learning algorithms, such as random forest (RF), support vector machine (SVM), generalised linear model (GLM), and extreme gradient boosting (XGB), were employed. The nomogram and receiver operating characteristic (ROC) curve assess the diagnostic value, and CIBERSORT analysed immune cell infiltration. We identified 20 intersecting genes among DEGs of OA, key module genes of MetS, and ARGs. By comparing the accuracy of the four machine learning models for disease prediction, the SVM model, which includes CEBPB, PTEN, ARPC1B, PIK3R1, and CDC42, was selected. These hub ARGs not only demonstrated strong diagnostic values based on nomogram data but also exhibited a significant correlation with immune cell infiltration. Building on these findings, we have identified five hub ARGs that are associated with immune cell infiltration and have constructed a nomogram aimed at early diagnosing OA patients with MetS.https://www.tandfonline.com/doi/10.1080/21691401.2025.2471762Ageing-related genesosteoarthritismetabolic syndromemachine learning |
| spellingShingle | Jian Huang Lu Wang Jiangfei Zhou Tianming Dai Weicong Zhu Tianrui Wang Hongde Wang Yingze Zhang Unveiling the ageing-related genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning Artificial Cells, Nanomedicine, and Biotechnology Ageing-related genes osteoarthritis metabolic syndrome machine learning |
| title | Unveiling the ageing-related genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning |
| title_full | Unveiling the ageing-related genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning |
| title_fullStr | Unveiling the ageing-related genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning |
| title_full_unstemmed | Unveiling the ageing-related genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning |
| title_short | Unveiling the ageing-related genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning |
| title_sort | unveiling the ageing related genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning |
| topic | Ageing-related genes osteoarthritis metabolic syndrome machine learning |
| url | https://www.tandfonline.com/doi/10.1080/21691401.2025.2471762 |
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