ArticleMolecular neurobiology2025
Identification of Autophagy-Related Genes in Patients with Acute Spinal Cord Injury and Analysis of Potential Therapeutic Targets.
Article in Molecular neurobiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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
4 citing papers in PubMed.
- Transcriptomics Insights into Spinal Cord Injury for Therapy Development.International journal of molecular sciences · 2026Review
- A zinc-coordinated cascade-responsive therapeutic nanoassembly for remodeling the pathological microenvironment and restoring mitochondrial homeostasis in spinal cord injury.Journal of nanobiotechnology · 2026Article
- Mitophagy: A key regulator in the pathophysiology and treatment of spinal cord injury.Neural regeneration research · 2026Article
- Multi-omic studies on the pathogenesis of Sepsis.Journal of translational medicine · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Autophagy has been implicated in the pathogenesis and progression of spinal cord injury (SCI); however, its specific mechanisms remain unclear. This study is aimed at identifying potential molecular biomarkers related to autophagy in SCI through bioinformatics analysis and exploring potential therapeutic targets. The mRNA expression profile dataset GSE151371 was obtained from the GEO database, and R software was used to screen for differentially expressed autophagy-related genes (DE-ARGs) in SCI. A total of 39 DE-ARGs were detected in this study. Enrichment analysis, protein-protein interaction (PPI) network, TF-mRNA-miRNA regulatory network analysis, and the DSigDB database were used to investigate the regulatory mechanisms between DE-ARGs and identify potential drugs for SCI. Enrichment analysis revealed associations with autophagy, apoptosis, and cell death. PPI analysis identified the highest-scoring module and selected 10 hub genes to construct the TF-mRNA-miRNA network, revealing regulatory mechanisms. Analysis of the DSigDB database indicated that 1,9-Pyrazoloanthrone may be a potential therapeutic drug. Machine learning algorithms identified 3 key genes as candidate biomarkers. Additionally, immune cell infiltration results revealed significant correlations between PINK1, NLRC4, VAMP3, and immune cell accumulation. Molecular docking simulations revealed that imatinib can exert relatively strong regulatory effects on the three key proteins. Finally, in vivo experimental data revealed that the overall biological process of autophagy was disrupted. In summary, this study successfully identified 39 DE-ARGs and discovered several promising biomarkers, significantly contributing to our understanding of the underlying mechanisms of autophagy in SCI. These findings offer valuable insights for the development of novel therapeutic strategies.
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