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<p>Spinal cord injury (SCI) is a severe neurological disorder, with glucocorticoids like methylprednisolone commonly used for treatment. However, their efficacy and risks remain controversial. Programmed cell death (PCD) mechanisms have been increasingly implicated in SCI pathology. This study aimed to identify differentially expressed genes (DEGs) related to glucocorticoid receptors and PCD and to construct a diagnostic model to guide glucocorticoid use in SCI treatment. SCI datasets (GSE5296, GSE47681, GSE151371, and GSE45550) were analyzed using protein-protein interaction networks, consensus clustering, GSVA for PCD pathway enrichment, and WGCNA. A total of 113 diagnostic models were developed through 12 machine learning algorithms, with the optimal model, “Lasso + Stepglm[both],” featuring six genes: Abca1, Cdh1, Glipr1, Glt8d2, Il10ra, and Pde5a. Validation through qRT-PCR confirmed the differential expression of four genes (Abca1, Glipr1, Il10ra, and Cdh1), which demonstrated strong predictive performance. Pathway enrichment of GRRDEGs was analyzed using GO, KEGG, and Bayesian network methods, and immune cell infiltration was assessed via CIBERSORT. In this study, we identified GR- and PCD-related DEGs in SCI and constructed a diagnostic model that may improve understanding of SCI molecular mechanisms and inform future investigations of glucocorticoid use.</p>

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Identification and validation of glucocorticoid receptor and programmed cell death-related genes in spinal cord injury using machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月7日

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  • Feng Lu,
  • Yingying Liu,
  • Zhen Chen,
  • Shuning Chen,
  • Weidong Liang,
  • Fuzhou Hua,
  • Maolin Zhong,
  • Lifeng Wang

摘要

Spinal cord injury (SCI) is a severe neurological disorder, with glucocorticoids like methylprednisolone commonly used for treatment. However, their efficacy and risks remain controversial. Programmed cell death (PCD) mechanisms have been increasingly implicated in SCI pathology. This study aimed to identify differentially expressed genes (DEGs) related to glucocorticoid receptors and PCD and to construct a diagnostic model to guide glucocorticoid use in SCI treatment. SCI datasets (GSE5296, GSE47681, GSE151371, and GSE45550) were analyzed using protein-protein interaction networks, consensus clustering, GSVA for PCD pathway enrichment, and WGCNA. A total of 113 diagnostic models were developed through 12 machine learning algorithms, with the optimal model, “Lasso + Stepglm[both],” featuring six genes: Abca1, Cdh1, Glipr1, Glt8d2, Il10ra, and Pde5a. Validation through qRT-PCR confirmed the differential expression of four genes (Abca1, Glipr1, Il10ra, and Cdh1), which demonstrated strong predictive performance. Pathway enrichment of GRRDEGs was analyzed using GO, KEGG, and Bayesian network methods, and immune cell infiltration was assessed via CIBERSORT. In this study, we identified GR- and PCD-related DEGs in SCI and constructed a diagnostic model that may improve understanding of SCI molecular mechanisms and inform future investigations of glucocorticoid use.