ArticleBrain and behavior2023
Identification of age-specific biomarkers of spinal cord injury: A bioinformatics analysis of young and aged mice models.
Article in Brain and behavior, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed, 2 citations in OpenAlex.
- STAT3 Signaling in Spinal Cord Injury: Neurochemical Mechanisms Linking Neuroinflammation, Mitochondrial Stress, and Glial Remodeling.Neurochemical research · 2026Review
- Machine Learning Identifies FLNA as a Key Molecular Target Regulating Neuronal Apoptosis after Spinal Cord Injury.Journal of molecular neuroscience : MN · 2025Article
- Identification of age-specific biomarkers of spinal cord injury: A bioinformatics analysis of young and aged mice models.Brain and behavior · 2023Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
backgroundSpinal cord injury (SCI) is a debilitating event that often results in long-term physical damage, disability, and a significant impact on a patient's quality of life. Past research has noted an age-dependent decline in regeneration post-SCI. This study seeks to identify potential biomarkers and enriched pathways in young and aged SCI mouse models.
methodsWe retrieved the microarray data of spinal cord samples from SCI mice and control mice from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the R software and the Linear Models for Microarray Data (limma) package. The Gene Set Enrichment Analysis (GSEA) determined enrichment differences among gene sets. The Weighted Gene Co-expression Network Analysis (WGCNA) pinpointed clinically significant modules and hub genes in SCI. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was employed to uncover significantly related pathways of crucial genes in SCI.
resultsWe found 2560 DEGs in the young SCI group, comprised of 1733 upregulated RNAs and 827 downregulated RNAs. In the aged SCI group, 3048 DEGs were revealed including 1856 upregulated and 1192 downregulated genes. The GSEA revealed 12 enriched signaling pathways in the young SCI group, such as IL6/JAK/STAT3 signaling, interferon alpha response, and interferon gamma response, and 21 signaling pathways in the aged SCI group such as IL6/JAK/STAT3 signaling, E2F targets, and angiogenesis-related pathways. The WGCNA identified the turquoise module as significantly associated with the clinical traits of both young and aged SCI, and revealed 3181 hub genes. Ultimately, 1858 significant genes in SCI were found, with associated signaling pathways including epithelial-mesenchymal transition (EMT), interferon gamma response, and KARS signaling.
conclusionOur study explored the RNA expression patterns and enriched signaling pathways in young and aged SCI mice. These findings may provide potential biomarkers for targeted SCI therapy.
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