ArticleFrontiers in genetics2026
Glycolysis-related gene signatures in spinal cord injury pathophysiology identification through integrative gene expression analysis.
Article in Frontiers in genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Spinal cord injury (SCI) is a debilitating condition that significantly impacts patients' mobility and quality of life, posing a substantial economic burden on healthcare systems. Increasing evidence suggests that metabolic reprogramming, particularly glycolysis, is involved in inflammatory responses following SCI. This study aims to systematically investigate the association between the role of glycolysis-related genes (GRGs) and SCI, and to identify potential candidate biomarkers. Methods: The GSE151371 dataset was retrieved from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs), which were subsequently subjected to Weighted Gene Co-expression Network Analysis (WGCNA) to pinpoint glycolysis-associated modules. Hub genes associated with SCI were initially identified through machine learning algorithms and subsequently evaluated using the independent GSE45006 dataset. Immune infiltration in SCI was profiled by single-sample gene set enrichment analysis (ssGSEA) and correlated with hub gene expression. After establishing TF-miRNA-mRNA and protein-chemical networks, hub gene expression patterns were characterized by scRNA-seq and further validated experimentally Results: From 1,138 DEGs, WGCNA identified 704 in glycolysis-associated modules. Intersecting these with GRGs yielded 13 candidates. Subsequent machine learning pinpointed six hub genes ( Conclusion: Our integrative transcriptomic and experimental analyses suggest that
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