Aging associated immunosenescence in rheumatoid arthritis identified by machine learning and single cell profiling.

ABSTRACT: Rheumatoid arthritis (RA) is increasingly prevalent among older adults, who often experience more severe symptoms and face significant treatment challenges. This study aims to identify specific genes associated with aging in RA and to analyze their immune infiltration using machine learning techniques. We sourced senescent genes from the HARG database and utilized three RA patient datasets obtained from the GEO database. Differential analysis revealed 50 age-related differentially expressed genes (ARDEGs) that intersected with senescent genes. Hub genes were identified through protein-protein interaction (PPI) network analysis as well as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Machine learning methods, including LASSO regression, random fo

SUBMITTER: Ji X 

PROVIDER: S-EPMC12375117 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature