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 operator, support vector machine-recursive feature elimination, and Random Forest algorithms. The diagnostic value of these feature genes was then validated through receiver operating characteristic (ROC) curves and decision curve analysis. Additionally, CIBERSORT and xCell were employed to assess the infiltration of immune cells in OA tissues. Finally, potential drugs targeting candidate genes were predicted. Three telomere-related genes,
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Research summary
The paper identifies telomere-related diagnostic markers for osteoarthritis using bioinformatics and machine learning. The focus on telomere-related genes in the context of osteoarthritis suggests a connection to cellular senescence, which is a key aspect of aging and longevity research.
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