ArticleMolecular neurobiology2026
Identification and Experimental Validation of Biomarkers Associated with PI3K/AKT Signaling Pathway in Spinal Cord Injury.
Article in Molecular neurobiology, 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
Spinal cord injury (SCI) refers to trauma to the spinal cord resulting in functional deficits. Dysregulation of the phosphoinositide 3-kinase/serine-threonine kinase (PI3K/AKT) pathway significantly contributes to the pathogenesis of SCI. This study evaluated the role of PI3K/AKT-associated biomarkers in SCI. Transcriptomic data from SCI samples deposited in the Gene Expression Omnibus (GEO) database were analyzed. An integrative approach combining differential expression profiling, machine learning, and experimental validation was employed to identify PI3K/AKT-related biomarkers. Combinatorial strategies-including functional enrichment analysis, immune microenvironment characterization, in silico drug prediction, and ligand-receptor docking-were used to elucidate potential biomarker-driven pathological mechanisms. Quantitative reverse transcription PCR (RT-qPCR) was performed to validate biomarker expression. Four biomarkers-FGF2, IL-6, PIK3R5, and TLR2-were successfully identified. Additionally, ELOVL6, IDI1, and SQLE were co-enriched in multiple pathways, including those associated with graft-versus-host disease (GVHD) in mice. TLR2 expression exhibited the strongest positive correlation with M2 macrophages (ρ = 0.74, P < 0.001) and the strongest negative correlation with neurons (ρ = -0.73, P < 0.001). Protein-ligand interaction analysis showed the highest binding scores of TLR2 with CHEMBL1836411, resveratrol hexanoic acid, and diprovocim-1. Molecular docking further confirmed a strong binding affinity between the TLR2 receptor and these compounds. RT-qPCR demonstrated significantly elevated transcript levels of Fgf2, Il6, Pik3r5, and Tlr2 in SCI samples (P < 0.01), corroborating the bioinformatic predictions. This study identifies FGF2, IL-6, PIK3R5, and TLR2 as key biomarkers in SCI, providing potential therapeutic targets for SCI treatment.
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