ArticleMolecular neurobiology2025
Machine Learning and Experiments Revealed Key Genes Related to PANoptosis Linked to Drug Prediction and Immune Landscape in Spinal Cord Injury.
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 5 papers.
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Who cites it
5 citing papers in PubMed.
- Mitochondrial homeostasis imbalance-triggered PANoptosis in traumatic brain and spinal cord injury: from mechanism to therapeutic strategies.Redox biology · 2026Review
- Machine learning-based identification of potential diagnostic signatures in spinal cord injury.Spinal cord · 2026Article
- PANoptosis nexus in ischemia-reperfusion injury: from integrated cell death mechanisms to novel therapeutic opportunities.Frontiers in immunology · 2026Review
- PANoptosis: a new perspective for targeting programmed cell death after spinal cord injury.Frontiers in immunology · 2026Review
- Deciphering the mechanistic involvement of PANoptosis in FC-MDD comorbidity through the gut-brain axis.Frontiers in immunology · 2026Review
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Authors and funding
9 authors.
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Abstract
Spinal cord injury (SCI) is a severe central nervous system injury without effective therapies. PANoptosis is involved in the development of many diseases, including brain and spinal cord injuries. However, the biological functions and molecular mechanisms of PANoptosis-related genes in spinal cord injury remain unclear. In the bioinformatics analysis of public data of SCI, the differentially expressed genes (DEGs) identified by GSE151371 were hybridized with PANoptosis-related genes (PRGs) to obtain differentially expressed PANoptosis-related genes (DE-PRGs). Through three machine learning algorithms, we obtained the hub genes. Then, we constructed functional analysis, drug prediction, regulatory network construction, and immune infiltrating cell analysis. Finally, the expression of the hub gene was verified in GSE93561, GSE45376, and qRT-PCR analysis. Through the above analysis, 14 DE-PRGs were obtained by intersecting 3582 DEGs with 46 PRGs. Five key hub genes, CASP4, GSDMB, NAIP, NLRC4, and NLRP3, were obtained by 3 machine learning algorithms. All five hub genes were enriched in phagocytosis mediated by FC GAMMA R. The 11 immune cells were significantly different between spinal cord injury (SCI) group and human control (HC) group, such as mast cell and gamma delta T cell. The transcription factor (TF)-hub gene network contained 10-nodes (4 hub genes and 6 TFs) and 8-edges. The miRNA-hub gene network consisting of 5-nodes (3 hub genes and 2 miRNAs) and 3-edges was constructed. Moreover, the CASP4 predicted 1 small molecule drug and NLRP3 predicted 9 small molecule drugs. Finally, the expression of 5 hub genes were significantly different in GSE45376 and GSE93561 (SCI vs. HC) and mice SCI model (Sham vs. SCI). Collectively, we identified 5 hub genes (CASP4, GSDMB, NAIP, NLRC4, and NLRP3) associated with PANoptosis, providing potential directions for treating spinal cord injury.
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