Screening biomarkers for spinal cord injury using weighted gene co-expression network analysis and machine learning

This study investigates immune changes and inflammatory responses in spinal cord injury (SCI) patients by analyzing microRNA and mRNA expression profiles in peripheral blood. Bioinformatics approaches identified differentially expressed microRNAs and genes, revealing abnormal activation or inhibition of immune and inflammation-related signaling pathways. The study identified three potential biomarkers (ANO10, BST1, and ZFP36L2) associated with SCI using weighted gene co-expression network analysis and machine learning algorithms, suggesting new treatment strategies.

• 1
  Identification of 54 differentially expressed microRNAs and 1656 differentially expressed genes in SCI patients compared to healthy controls.
• 2
  Discovery that immune and inflammation-related signaling pathways, such as neutrophil extracellular trap formation, T cell receptor signaling, and NF-κB signaling, are abnormally activated or inhibited in SCI.
• 3
  Validation of three potential biomarkers (ANO10, BST1, and ZFP36L2) for SCI, with ANO10 and BST1 mRNA levels increased and ZFP36L2 mRNA levels decreased in the peripheral blood of SCI patients.