Development of a diagnostic model based on glycolysis-related genes and immune infiltration in intervertebral disc degeneration.

Background: The glycolytic pathway and immune response play pivotal roles in the intervertebral disc degeneration (IDD) progression. This study aimed to develop a glycolysis-related diagnostic model and analyze its relationship with the immune response to IDD. Methods: GSE70362, GSE23130, and GSE15227 datasets were collected and merged from the Gene Expression Omnibus, and differential expression analysis was performed. Glycolysis-related differentially expressed genes (GLRDEGs) were identified, and a machine learning-based diagnostic model was constructed and validated, followed by Gene Set Enrichment Analysis (GSEA). Gene Ontology functional enrichment and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed, and mRNA-miRNA and mRNA-transcription factor (TF) interaction networks were constructed. Immune infiltration was analyzed using single-sample GSEA (ssGSEA) and cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm between high- and low-risk groups. Results: In the combined dataset, samples from 31 patients with IDD and 55 normal controls were analyzed, revealing differential expression of 16 GLRDEGs between the two groups. Using advanced machine learning techniques (LASSO, support vector machine, and random forest algorithms), we identified eight common GLRDEGs ( Conclusions: Our study reveals a potential interplay between glycolysis-associated genes and immune infiltration in IDD pathogenesis. These findings contribute to our understanding of IDD and may guide development of novel diagnostic markers and therapeutic interventions.