comprehensive analysis of core genes, key pathways, and immune infiltration in intervertebral disc degeneration using machine learning and experimental validation

This study integrated and analyzed two sets of gene expression data related to intervertebral disc degeneration (ivdd) to elucidate its key molecular mechanisms. through screening and enrichment analysis of differentially expressed genes (degs), 112 degs were identified, primarily involved in extracellular matrix remodeling, cytoplasmic translation, and signaling pathways such as pi3k-akt. a protein-protein interaction network combined with lasso and svm-rfe machine learning algorithms identified 13 hub genes. immune infiltration analysis revealed reduced infiltration of suppressor cells and monocytes in ivdd samples. in an il-1β-induced human nucleus pulposus cell degeneration model, qpcr and western blot experiments confirmed significant downregulation of adm, itgb5, rtn4, slpi, and csnk1e expression. this study systematically reveals the potential molecular networks and immune characteristics of ivdd, providing new candidate biomarkers and therapeutic insights for subsequent targeted drug development.