Machine learning-driven prediction model for cuproptosis-related genes in spinal cord injury: construction and experimental validation
IntroductionSpinal cord injury (SCI) severely affects the central nervous system. Copper homeostasis is closely related to mitochondrial regulation, and cuproptosis is a novel form of cell death associated with mitochondrial metabolism. This study aimed to explore the relationship between SCI and cu...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2025.1525416/full |
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| author | Yimin Zhou Xin Li Zixiu Wang Liqi Ng Rong He Chaozong Liu Gang Liu Xiao Fan Xiaohong Mu Yu Zhou Yu Zhou |
| author_facet | Yimin Zhou Xin Li Zixiu Wang Liqi Ng Rong He Chaozong Liu Gang Liu Xiao Fan Xiaohong Mu Yu Zhou Yu Zhou |
| author_sort | Yimin Zhou |
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| description | IntroductionSpinal cord injury (SCI) severely affects the central nervous system. Copper homeostasis is closely related to mitochondrial regulation, and cuproptosis is a novel form of cell death associated with mitochondrial metabolism. This study aimed to explore the relationship between SCI and cuproptosis and construct prediction models.MethodsGene expression data of SCI patient samples from the GSE151371 dataset were analyzed. The differential expression and correlation of 13 cuproptosis-related genes (CRGs) between SCI and non-SCI samples were identified, and the ssGSEA algorithm was used for immunological infiltration analysis. Unsupervised clustering was performed based on differentially expressed CRGs, followed by weighted gene co-expression network analysis (WGCNA) and enrichment analysis. Three machine learning models (RF, LASSO, and SVM) were constructed to screen candidate genes, and a Nomogram model was used for verification. Animal experiments were carried out on an SCI rat model, including behavioral scoring, histological staining, electron microscopic observation, and qRT-PCR.ResultsSeven CRGs showed differential expression between SCI and non-SCI samples, and there were significant differences in immune cell infiltration levels. Unsupervised clustering divided 38 SCI samples into two clusters (Cluster C1 and Cluster C2). WGCNA identified key modules related to the clusters, and enrichment analysis showed involvement in pathways such as the Ribosome and HIF-1 signaling pathway. Four candidate genes (SLC31A1, DBT, DLST, LIAS) were obtained from the machine learning models, with SLC31A1 performing best (AUC = 0.958). Animal experiments confirmed a significant decrease in the behavioral scores of rats in the SCI group, pathological changes in tissue sections, and differential expression of candidate genes in the SCI rat model.DiscussionThis study revealed a close association between SCI and cuproptosis. Abnormal expression of the four candidate genes affects mitochondrial function, energy metabolism, oxidative stress, and the immune response, which is detrimental to the recovery of neurological function in SCI. However, this study has some limitations, such as unidentified SRGs, a small sample size. Future research requires more in vitro and in vivo experiments to deeply explore regulatory mechanisms and develop intervention methods. |
| format | Article |
| id | doaj-art-bffdcda3097a45cf9b7aa38fbdbabafa |
| institution | OA Journals |
| issn | 1664-2295 |
| language | English |
| publishDate | 2025-04-01 |
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| series | Frontiers in Neurology |
| spelling | doaj-art-bffdcda3097a45cf9b7aa38fbdbabafa2025-08-20T02:12:07ZengFrontiers Media S.A.Frontiers in Neurology1664-22952025-04-011610.3389/fneur.2025.15254161525416Machine learning-driven prediction model for cuproptosis-related genes in spinal cord injury: construction and experimental validationYimin Zhou0Xin Li1Zixiu Wang2Liqi Ng3Rong He4Chaozong Liu5Gang Liu6Xiao Fan7Xiaohong Mu8Yu Zhou9Yu Zhou10Department of Orthopedics, Dongzhimen Hospital of Beijing University of Chinese Medicine, Beijing, ChinaPostdoctoral Research Workstation, Orthopedic Hospital, Chonqqing University of Chinese Medicine, Chongqing, ChinaCollege of Pharmacy, Gannan Medical University, Ganzhou, ChinaInstitute of Orthopaedics and Musculoskeletal Science, University College London, Royal National Orthopaedic Hospital, London, United KingdomCollege of Integrated Chinese and Western Medicine, Changchun University of Chinese Medicine, Changchun, ChinaInstitute of Orthopaedics and Musculoskeletal Science, University College London, Royal National Orthopaedic Hospital, London, United KingdomDepartment of Orthopedics, Dongzhimen Hospital of Beijing University of Chinese Medicine, Beijing, ChinaDepartment of Orthopedics, Qingdao Municipal Hospital, Qingdao, Shandong, ChinaDepartment of Orthopedics, Dongzhimen Hospital of Beijing University of Chinese Medicine, Beijing, ChinaPostdoctoral Research Workstation, Orthopedic Hospital, Chonqqing University of Chinese Medicine, Chongqing, ChinaDepartment of Orthopedics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaIntroductionSpinal cord injury (SCI) severely affects the central nervous system. Copper homeostasis is closely related to mitochondrial regulation, and cuproptosis is a novel form of cell death associated with mitochondrial metabolism. This study aimed to explore the relationship between SCI and cuproptosis and construct prediction models.MethodsGene expression data of SCI patient samples from the GSE151371 dataset were analyzed. The differential expression and correlation of 13 cuproptosis-related genes (CRGs) between SCI and non-SCI samples were identified, and the ssGSEA algorithm was used for immunological infiltration analysis. Unsupervised clustering was performed based on differentially expressed CRGs, followed by weighted gene co-expression network analysis (WGCNA) and enrichment analysis. Three machine learning models (RF, LASSO, and SVM) were constructed to screen candidate genes, and a Nomogram model was used for verification. Animal experiments were carried out on an SCI rat model, including behavioral scoring, histological staining, electron microscopic observation, and qRT-PCR.ResultsSeven CRGs showed differential expression between SCI and non-SCI samples, and there were significant differences in immune cell infiltration levels. Unsupervised clustering divided 38 SCI samples into two clusters (Cluster C1 and Cluster C2). WGCNA identified key modules related to the clusters, and enrichment analysis showed involvement in pathways such as the Ribosome and HIF-1 signaling pathway. Four candidate genes (SLC31A1, DBT, DLST, LIAS) were obtained from the machine learning models, with SLC31A1 performing best (AUC = 0.958). Animal experiments confirmed a significant decrease in the behavioral scores of rats in the SCI group, pathological changes in tissue sections, and differential expression of candidate genes in the SCI rat model.DiscussionThis study revealed a close association between SCI and cuproptosis. Abnormal expression of the four candidate genes affects mitochondrial function, energy metabolism, oxidative stress, and the immune response, which is detrimental to the recovery of neurological function in SCI. However, this study has some limitations, such as unidentified SRGs, a small sample size. Future research requires more in vitro and in vivo experiments to deeply explore regulatory mechanisms and develop intervention methods.https://www.frontiersin.org/articles/10.3389/fneur.2025.1525416/fullspinal cord injurycuproptosismachine learningpredictive modelsunsupervised clustering |
| spellingShingle | Yimin Zhou Xin Li Zixiu Wang Liqi Ng Rong He Chaozong Liu Gang Liu Xiao Fan Xiaohong Mu Yu Zhou Yu Zhou Machine learning-driven prediction model for cuproptosis-related genes in spinal cord injury: construction and experimental validation Frontiers in Neurology spinal cord injury cuproptosis machine learning predictive models unsupervised clustering |
| title | Machine learning-driven prediction model for cuproptosis-related genes in spinal cord injury: construction and experimental validation |
| title_full | Machine learning-driven prediction model for cuproptosis-related genes in spinal cord injury: construction and experimental validation |
| title_fullStr | Machine learning-driven prediction model for cuproptosis-related genes in spinal cord injury: construction and experimental validation |
| title_full_unstemmed | Machine learning-driven prediction model for cuproptosis-related genes in spinal cord injury: construction and experimental validation |
| title_short | Machine learning-driven prediction model for cuproptosis-related genes in spinal cord injury: construction and experimental validation |
| title_sort | machine learning driven prediction model for cuproptosis related genes in spinal cord injury construction and experimental validation |
| topic | spinal cord injury cuproptosis machine learning predictive models unsupervised clustering |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2025.1525416/full |
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