ArticleBehavioural neurology2026
Identification and Verification of Mitochondria-Related Diagnostic Markers of Spinal Cord Injury by WGCNA and Machine Learning.
Article in Behavioural neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Identification and Verification of Mitochondria-Related Diagnostic Markers of Spinal Cord Injury by WGCNA and Machine Learning.Behavioural neurology · 2026Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSpinal 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.
methodsRNA 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.
resultsA 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.
conclusionThese six genes represent potential biomarkers and therapeutic targets for SCI, providing insights into its molecular mechanisms and immune response.
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