Meta-SCI app: a comprehensive transcriptional reference for understanding the severity and progression of spinal cord injury

Abstract Background Spinal cord injury (SCI) is a devastating condition that leads to motor, sensory, and autonomic dysfunction. Current therapeutic options remain limited, emphasizing the need for a comprehensive understanding of the underlying SCI-associated molecular mechanisms. Results This study characterized distinct SCI phases and severities at the gene and functional levels, focusing on biomarker gene identification. Our approach involved a systematic review, individual transcriptomic analysis, gene meta-analysis, and functional characterization. We compiled a total of fourteen studies with 273 samples, leading to the identification of severity-specific biomarker genes for injury prognosis (e.g., Srpx2, Hoxb8, Acap1, Snai1, Aadat) and phase-specific genes for the precise classification of the injury profile (e.g., Il6, Fosl1, Cfp, C1qc, Cp). We investigated the potential transferability of severity-associated biomarkers and identified a twelve-gene signature that predicted injury prognosis from human blood samples. We also report the development of MetaSCI-app - an interactive web application designed for researchers - that allows the exploration and visualization of all generated results (https://metasci-cbl.shinyapps.io/metaSCI). Conclusions Overall, we present a transcriptomic reference and provide a comprehensive framework for assessing SCI considering severity and time perspectives, all integrated into a user-friendly tool.