Identification of telomere-related diagnostic markers in osteoarthritis based on bioinformatics analysis and machine learning.

ABSTRACT: Osteoarthritis (OA) is one of the most prevalent joint disorders, with aging considered a primary, irreversible factor contributing to its progression. Telomere-related cellular senescence may be a crucial factor influencing the OA process, yet biomarkers for OA based on telomere-related genes have not been clearly identified. The datasets GSE51588, GSE12021, and GSE55457 were retrieved from the Gene Expression Omnibus database. Initially, R software was utilized to identify differentially expressed genes between OA and normal samples. Subsequently, differentially expressed telomere-related genes (DETMRGs) were obtained, and their functional enrichment was analyzed. Feature genes for OA diagnosis were selected from DETMRGs using a combination of least absolute shrinkage and selection opera

SUBMITTER: Xu S 

PROVIDER: S-EPMC12012318 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature