Machine learning-driven prediction model for cuproptosis-related genes in spinal cord injury: construction and experimental validation

作者：Yimin Zhou, Yimin Zhou, Xin Li, Zixiu Wang, Liqi Ng, Rong‐Rong He, Chaozong Liu, Gang Liu, Xiao Fan, Xiaohong Mu, Yu Zhou, Yu Zhou · 发表于：Frontiers in Neurology · 年份：2025 · DOI：10.3389/fneur.2025.1525416 · 被引用次数：5 · 研究领域：Spinal Cord Injury Research、Trace Elements in Health、Ferroptosis and cancer prognosis

Introduction: Spinal 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. Methods: Gene 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. Results: Seven 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 an...