Identification and Verification of Mitochondria-Related Diagnostic Markers of Spinal Cord Injury by WGCNA and Machine Learning.

Abstract

Background: Spinal cord injury (SCI) significantly impacts patients, with mitochondrial dysfunction playing a critical role in its pathology. Identifying mitochondria-related genes may offer new therapeutic and prognostic insights.

Methods: RNA sequencing data from the GEO database were analyzed to identify differentially expressed genes (DEGs). Functional enrichment analyses were conducted, and weighted gene coexpression network analysis (WGCNA) alongside machine learning algorithms was used to identify key mitochondria-related genes. Immune infiltration was assessed using the EPIC algorithm, and single-cell RNA sequencing (scRNA-seq) data were analyzed for cellular diversity.

Results: A total of 2566 upregulated and 2634 downregulated genes were identified in SCI versus control samples. GO and KEGG enrichment analyses revealed these DEGs were primarily involved in oxidative stress, mitochondrial function, and immune pathways, including necroptosis and T cell receptor signaling. Then, 1578 genes with the strongest correlation to SCI were selected by WGCNA. By integrating DEGs, WGCNA module genes, and mitochondria-related genes, 76 candidate genes were obtained and used to construct a PPI network. Six hub genes (NDUFB3, SLC25A24, SLC25A40, GSTZ1, MAOA, and MRPL12) were identified by machine learning, all showing strong diagnostic potential (AUC > 0.77). Immune infiltration analysis indicated reduced B and T cell infiltration and increased macrophage activity in SCI samples. scRNA-seq analysis further revealed higher expression of NDUFB3 in dendritic cells and MAOA in pro-B cells, suggesting their involvement in immune regulation and mitochondrial dysfunction.

Conclusion: These six genes represent potential biomarkers and therapeutic targets for SCI, providing insights into its molecular mechanisms and immune response.