Identification of a novel disulfidptosis-related gene signature in osteoarthritis using bioinformatics analysis and experimental validation
Abstract Osteoarthritis (OA) is a degenerative bone disease characterized by the destruction of joint cartilage and synovial inflammation, involving intricate immune regulation processes. Disulfidptosis, a novel form of programmed cell death, has recently been identified; however, the effects and ro...
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2025-01-01
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| author | Mingjie Wei Xiangwen Shi Wenbao Tang Qian Lv Yipeng Wu Yongqing Xu |
| author_facet | Mingjie Wei Xiangwen Shi Wenbao Tang Qian Lv Yipeng Wu Yongqing Xu |
| author_sort | Mingjie Wei |
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| description | Abstract Osteoarthritis (OA) is a degenerative bone disease characterized by the destruction of joint cartilage and synovial inflammation, involving intricate immune regulation processes. Disulfidptosis, a novel form of programmed cell death, has recently been identified; however, the effects and roles of disulfidptosis-related genes (DR-DEGs) in OA remain unclear. We obtained six OA datasets from the GEO database, using four as training sets and two as validation sets. Differential expression analysis was employed to identify DR-DEGs, and unique molecular subtypes of OA were constructed based on these DR-DEGs. Subsequently, the immune microenvironment of OA patients was comprehensively analyzed using the “CIBERSORT” algorithm for immune infiltration. Various machine learning algorithms were utilized to screen characteristic DR-DEGs, and nomogram models and ROC curves were built based on these genes. The scRNA dataset (GSE169454) was used to classify chondrocytes in OA samples into distinct cell types, further exploring the gene distribution and correlation of characteristic DR-DEGs with specific cell subpopulations. Moreover, the expression levels of four characteristic DR-DEGs were validated through OA cell models and rat models. In our study, we identified 10 DR-DEGs with significant differences in expression within OA samples. Based on these DR-DEGs, two distinct molecular subtypes were recognized (cluster 1 and 2). ZNF484 and NDUFS1 were found to be significantly overexpressed in subtype 1, while the infiltration abundance of activated mast cells was markedly elevated in subtype 2. Moreover, significant differences were observed in the infiltration proportions of 11 immune cell types between OA and control samples, with 9 DR-DEGs demonstrating substantial correlations with immune cell infiltration levels. Further analysis of the scRNA dataset revealed that SLC3A2 and NDUFC1 were predominantly expressed in the preHTC subpopulation. All 10 DR-DEGs exhibited notably higher expression in the EC subpopulation across various cell types. The proportion of EC subgroups with high SLC3A2 expression increased, mainly enriching pathways related to inflammation, such as the IL-17 signaling pathway and TGF-beta signaling pathway. Using machine learning, we identified four characteristic DR-DEGs, which, in combination with the nomogram and ROC models, demonstrated promising performance in the diagnosis of OA. Additionally, in vivo validation confirmed a significant elevation of PPM1F expression in OA models. This study identified DR-DEGs as potential biomarkers for the diagnosis and classification of OA and provided a preliminary understanding of their role in the immune microenvironment. However, further experimental and clinical studies are required to validate their diagnostic value and therapeutic potential. |
| format | Article |
| id | doaj-art-2a84d55b93d245dc918c6e27ca0b94e1 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
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| spelling | doaj-art-2a84d55b93d245dc918c6e27ca0b94e12025-01-12T12:19:14ZengNature PortfolioScientific Reports2045-23222025-01-0115111910.1038/s41598-025-85569-zIdentification of a novel disulfidptosis-related gene signature in osteoarthritis using bioinformatics analysis and experimental validationMingjie Wei0Xiangwen Shi1Wenbao Tang2Qian Lv3Yipeng Wu4Yongqing Xu5Department of Orthopedic Surgery, 920th Hospital of Joint Logistics Support Force of PLADepartment of Orthopedic Surgery, 920th Hospital of Joint Logistics Support Force of PLADepartment of Orthopedic Surgery, 920th Hospital of Joint Logistics Support Force of PLADepartment of Orthopedic Surgery, 920th Hospital of Joint Logistics Support Force of PLADepartment of Orthopedic Surgery, 920th Hospital of Joint Logistics Support Force of PLADepartment of Orthopedic Surgery, 920th Hospital of Joint Logistics Support Force of PLAAbstract Osteoarthritis (OA) is a degenerative bone disease characterized by the destruction of joint cartilage and synovial inflammation, involving intricate immune regulation processes. Disulfidptosis, a novel form of programmed cell death, has recently been identified; however, the effects and roles of disulfidptosis-related genes (DR-DEGs) in OA remain unclear. We obtained six OA datasets from the GEO database, using four as training sets and two as validation sets. Differential expression analysis was employed to identify DR-DEGs, and unique molecular subtypes of OA were constructed based on these DR-DEGs. Subsequently, the immune microenvironment of OA patients was comprehensively analyzed using the “CIBERSORT” algorithm for immune infiltration. Various machine learning algorithms were utilized to screen characteristic DR-DEGs, and nomogram models and ROC curves were built based on these genes. The scRNA dataset (GSE169454) was used to classify chondrocytes in OA samples into distinct cell types, further exploring the gene distribution and correlation of characteristic DR-DEGs with specific cell subpopulations. Moreover, the expression levels of four characteristic DR-DEGs were validated through OA cell models and rat models. In our study, we identified 10 DR-DEGs with significant differences in expression within OA samples. Based on these DR-DEGs, two distinct molecular subtypes were recognized (cluster 1 and 2). ZNF484 and NDUFS1 were found to be significantly overexpressed in subtype 1, while the infiltration abundance of activated mast cells was markedly elevated in subtype 2. Moreover, significant differences were observed in the infiltration proportions of 11 immune cell types between OA and control samples, with 9 DR-DEGs demonstrating substantial correlations with immune cell infiltration levels. Further analysis of the scRNA dataset revealed that SLC3A2 and NDUFC1 were predominantly expressed in the preHTC subpopulation. All 10 DR-DEGs exhibited notably higher expression in the EC subpopulation across various cell types. The proportion of EC subgroups with high SLC3A2 expression increased, mainly enriching pathways related to inflammation, such as the IL-17 signaling pathway and TGF-beta signaling pathway. Using machine learning, we identified four characteristic DR-DEGs, which, in combination with the nomogram and ROC models, demonstrated promising performance in the diagnosis of OA. Additionally, in vivo validation confirmed a significant elevation of PPM1F expression in OA models. This study identified DR-DEGs as potential biomarkers for the diagnosis and classification of OA and provided a preliminary understanding of their role in the immune microenvironment. However, further experimental and clinical studies are required to validate their diagnostic value and therapeutic potential.https://doi.org/10.1038/s41598-025-85569-zOsteoarthritisDisulfidptosisChondrocyteSingle-cell sequencingDiagnosis |
| spellingShingle | Mingjie Wei Xiangwen Shi Wenbao Tang Qian Lv Yipeng Wu Yongqing Xu Identification of a novel disulfidptosis-related gene signature in osteoarthritis using bioinformatics analysis and experimental validation Scientific Reports Osteoarthritis Disulfidptosis Chondrocyte Single-cell sequencing Diagnosis |
| title | Identification of a novel disulfidptosis-related gene signature in osteoarthritis using bioinformatics analysis and experimental validation |
| title_full | Identification of a novel disulfidptosis-related gene signature in osteoarthritis using bioinformatics analysis and experimental validation |
| title_fullStr | Identification of a novel disulfidptosis-related gene signature in osteoarthritis using bioinformatics analysis and experimental validation |
| title_full_unstemmed | Identification of a novel disulfidptosis-related gene signature in osteoarthritis using bioinformatics analysis and experimental validation |
| title_short | Identification of a novel disulfidptosis-related gene signature in osteoarthritis using bioinformatics analysis and experimental validation |
| title_sort | identification of a novel disulfidptosis related gene signature in osteoarthritis using bioinformatics analysis and experimental validation |
| topic | Osteoarthritis Disulfidptosis Chondrocyte Single-cell sequencing Diagnosis |
| url | https://doi.org/10.1038/s41598-025-85569-z |
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