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<p>To address the lack of reliable biomarkers for mitochondrial dysfunction that drives secondary injury in spinal cord injury (SCI), this study aimed to identify and validate a robust mitochondria-focused biomarker signature by integrating transcriptomic profiling with machine learning. Gene expression data from 113 mouse spinal cord samples were integrated and analyzed to identify a distinct SCI-associated mitochondrial biomarker signature. Dysregulated mitochondrial genes were screened from this dataset. Multiple machine learning algorithms were then applied to derive a high-performing molecular signature. A quantitative nomogram model was constructed based on this signature, and its accuracy in distinguishing pathological SCI states from controls was assessed using the area under the curve (AUC). The identified gene upregulation was further validated in an independent SCI mouse model. Finally, gene set enrichment analysis (GSEA) was performed to investigate the biological pathways associated with the gene signature. A high-performing molecular signature comprising five mitochondrial genes, Mcl1, Dhrs1, Tspo, Star, and Pmaip1, was identified. A nomogram model was constructed based on this signature to quantify the mitochondrial dysregulation severity following injury. This model demonstrated high accuracy in reflecting the pathological status, achieving an AUC of 0.931. This signature offers a molecular tool to assess the extent of secondary pathological changes, potentially serving as a biomarker for therapeutic monitoring. The upregulation of all five genes was confirmed in an independent mouse SCI model. GSEA revealed strong associations between the identified signature and the activation of innate immune pathways, alongside the suppression of neuronal function pathways. This study established a robust, descriptive mitochondria-associated gene signature that accurately reflects the transcriptomic alterations following SCI. This signature maps a key pathogenic axis linking mitochondrial dysregulation with neuroimmune pathology. These findings provide a prioritized molecular foundation for future mechanistic investigations and prospective translational studies in SCI.</p>

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Identification of a mitochondrial biomarker signature linking neuroinflammation to neuronal dysfunction in spinal cord injury

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月7日

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  • Haipeng Xu,
  • Yaheng Jiang,
  • Luojie Xiong,
  • Ding Tang,
  • Honggen Du,
  • Yunxing Xie

摘要

To address the lack of reliable biomarkers for mitochondrial dysfunction that drives secondary injury in spinal cord injury (SCI), this study aimed to identify and validate a robust mitochondria-focused biomarker signature by integrating transcriptomic profiling with machine learning. Gene expression data from 113 mouse spinal cord samples were integrated and analyzed to identify a distinct SCI-associated mitochondrial biomarker signature. Dysregulated mitochondrial genes were screened from this dataset. Multiple machine learning algorithms were then applied to derive a high-performing molecular signature. A quantitative nomogram model was constructed based on this signature, and its accuracy in distinguishing pathological SCI states from controls was assessed using the area under the curve (AUC). The identified gene upregulation was further validated in an independent SCI mouse model. Finally, gene set enrichment analysis (GSEA) was performed to investigate the biological pathways associated with the gene signature. A high-performing molecular signature comprising five mitochondrial genes, Mcl1, Dhrs1, Tspo, Star, and Pmaip1, was identified. A nomogram model was constructed based on this signature to quantify the mitochondrial dysregulation severity following injury. This model demonstrated high accuracy in reflecting the pathological status, achieving an AUC of 0.931. This signature offers a molecular tool to assess the extent of secondary pathological changes, potentially serving as a biomarker for therapeutic monitoring. The upregulation of all five genes was confirmed in an independent mouse SCI model. GSEA revealed strong associations between the identified signature and the activation of innate immune pathways, alongside the suppression of neuronal function pathways. This study established a robust, descriptive mitochondria-associated gene signature that accurately reflects the transcriptomic alterations following SCI. This signature maps a key pathogenic axis linking mitochondrial dysregulation with neuroimmune pathology. These findings provide a prioritized molecular foundation for future mechanistic investigations and prospective translational studies in SCI.