AI Predicts Molecular Pathways in Spinal Cord Injury

Published Date : 13/10/2025 

Discover how artificial intelligence is revolutionizing the prediction and modeling of molecular pathways in spinal cord injury, offering new hope for regenerative and precision medicine. 

Spinal cord injury (SCI) is a devastating neurological condition with a high global incidence and limited curative options. The pathobiology of SCI is multifactorial, involving acute mechanical injury, sustained neuroinflammation, oxidative stress, mitochondrial dysfunction, and glial scarring. These complex processes are orchestrated by molecular signaling pathways, which have been challenging to exploit clinically due to the intricate gene-environment interactions and heterogeneous patient profiles.

Artificial intelligence (AI) is emerging as a powerful tool to integrate multidimensional omics datasets, enabling the prediction of pathway activities, identification of key biomarkers, and prioritization of therapeutic targets. This article systematically synthesizes current evidence on AI-driven prediction and modeling of molecular pathway processes in SCI, evaluates the performance and methodological quality of these approaches, and highlights translational opportunities for regenerative and precision medicine.

Following PRISMA guidelines, we conducted a comprehensive search of PubMed, Scopus, and Web of Science from 2000 to 2025. The protocol was registered in PROSPERO (CRD420251115723). Inclusion criteria encompassed studies applying AI algorithms to predict or model molecular pathway activity in SCI. The risk of bias was assessed using the PROBAST tool. Data on study design, cohort characteristics, AI methodology, pathway findings, biomarker signatures, and validation approaches were extracted and synthesized.

Of the 86 records identified, 11 studies met the criteria. Most were observational, bioinformatics-driven investigations published after 2020, predominantly from China. These studies heavily relied on the GSE151371 blood transcriptomic dataset and small validation cohorts. Six studies achieved AUC values ranging from 0.79 to 1.00 for diagnostic and severity classification. Recurrent biomarkers included FCER1G, NFATC2, S100A8, and IL2RB. However, the risk of overfitting was high due to dataset reuse and limited external validation.

Mechanistic insights from seven studies converged on immune dysregulation (NF-κB, VEGF, JAK-STAT, Toll-like receptor), novel cell death modalities (PANoptosis, cuproptosis), and metabolic/autophagy disruptions (PINK1/SQSTM1). Five studies proposed therapeutic interventions, including drug repurposing (Emricasan, Alaproclate, Imatinib), cuproptosis-targeted agents, ZnO nanoparticles, and mesenchymal stem cell transplantation. All validations were restricted to preclinical models.

The GRADE assessment rated the evidence as very low to low certainty, primarily due to the risk of bias, indirectness, and imprecision. Despite these limitations, AI is beginning to map the intricate immune-metabolic-degenerative network underlying SCI, revealing candidate biomarkers and therapeutic targets with potential regenerative relevance.

However, current evidence is constrained by small, homogeneous datasets, preclinical bias, and a lack of longitudinal human validation. Scaling to multicenter, multi-omics cohorts and advancing promising candidates into early phase trials are essential next steps to realize the translational promise of AI in SCI management. 

Frequently Asked Questions (FAQS): 

Q: What is the primary goal of using AI in spinal cord injury research?

A: The primary goal is to predict and model molecular pathway activities, identify key biomarkers, and prioritize therapeutic targets to enhance regenerative and precision medicine.

Q: What are the key biomarkers identified in the studies?

A: Key biomarkers identified include FCER1G, NFATC2, S100A8, and IL2RB, which are associated with immune dysregulation and other molecular processes in spinal cord injury.

Q: What are some of the proposed therapeutic interventions?

A: Proposed therapeutic interventions include drug repurposing (Emricasan, Alaproclate, Imatinib), cuproptosis-targeted agents, ZnO nanoparticles, and mesenchymal stem cell transplantation.

Q: What are the main limitations of current AI-driven studies in SCI?

A: The main limitations include small, homogeneous datasets, preclinical bias, and a lack of longitudinal human validation, which affect the reliability and generalizability of the findings.

Q: What are the next steps to realize the translational promise of AI in SCI management?

A: The next steps include scaling to multicenter, multi-omics cohorts and advancing promising candidates into early phase clinical trials to validate and implement AI-driven findings. 

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