Machine learning-enabled identification of nucleus pulposus senescence-associated genes as potential biomarkers for intervertebral disc degeneration.

ABSTRACT:

Background

Intervertebral disc degeneration (IVDD) has risen to become a global health problem, and its mechanisms are not well understood. The aim of this study was to use machine learning methods to identify key medullary senescence genes associated with IVDD and to further screen for potential biomarkers, as well as to explore the pathways by which these genes can be analyzed in both IVDD and normal states.

Methods

Gene expression profiles (GSE70362) of IVDD were analyzed from the extensive Gene Expression Omnibus database, and senescence-related genes (SRGs) were obtained from the cell age database. We utilized the Kyoto Encyclopedia of Genes (KEGG) and Gene Ontology (GO) databases for comprehensive functional enrichment analysis. To identify Hub SRDEGs with highly corre

SUBMITTER: Xiang M 

PROVIDER: S-EPMC12482253 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature