# Exploring hypoxia-related genes in spinal cord injury: a pathway to new therapeutic targets

## TL;DR

This study identifies key hypoxia-related genes in spinal cord injury that could lead to new treatments.

## Contribution

A diagnostic model using LASSO and Random Forest identifies novel hypoxia-related biomarkers for spinal cord injury.

## Key findings

- Casp6, Pkm, Cxcr4, and Hexa are critical hypoxia-related biomarkers in spinal cord injury.
- The model achieved high accuracy with an area under the curve exceeding 0.9.
- Distinct pathways were found in low- and high-risk SCI groups, suggesting potential for clinical stratification.

## Abstract

Spinal cord injury (SCI) remains a debilitating condition with limited therapeutic options. Exploring hypoxia-related genes in SCI may reveal potential therapeutic targets and improve our understanding of its pathogenesis.

We developed a diagnostic model using LASSO regression and Random Forest algorithms to investigate hypoxia-related genes in SCI. The model identified critical biomarkers by analyzing differentially expressed genes (DEGs) and hypoxia-related DEGs (HRDEGs). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and Gene Set Variation Analysis (GSVA) were conducted to explore the biological roles of HRDEGs. The model’s accuracy was validated using receiver operating characteristic curves, calibration plots, decision curves, and qPCR experiments.

The diagnostic model identified Casp6, Pkm, Cxcr4, and Hexa as critical biomarkers among 186 HRDEGs out of 9,732 altered genes in SCI. These biomarkers were significantly associated with SCI pathogenesis. GO and KEGG analyses highlighted their roles in hypoxia responses, particularly through the hypoxia-inducible factor 1 pathway. The model demonstrated high accuracy, with an area under the curve exceeding 0.9. GSEA and GSVA revealed distinct pathways in low- and high-risk SCI groups, suggesting potential clinical stratification strategies.

This study constructed a diagnostic model that confirmed Casp6, Pkm, Cxcr4, and Hexa as important biomarkers for SCI. The findings provide valuable insights into SCI pathogenesis and pave the way for novel treatment strategies. The integration of multi-omics data and comprehensive bioinformatics analyses offers a robust framework for identifying therapeutic targets and improving patient outcomes.