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...
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-15370-5 |
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| author | Xinxin Ji Lingyun Li Yuanzhuo Jiao Hui Cheng |
| author_facet | Xinxin Ji Lingyun Li Yuanzhuo Jiao Hui Cheng |
| author_sort | Xinxin Ji |
| collection | DOAJ |
| description | 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 forest (RF), and support vector machine recursive feature elimination (SVM-RFE), were employed to extract feature genes. Single-sample gene set enrichment analysis (ssGSEA) quantified immune cell infiltration, revealing 242 up-regulated and 176 down-regulated differentially expressed genes (DEGs). Notably, high levels of effector memory CD8 T cells and macrophages were found to be associated with robust immune responses. This study successfully identified four biomarkers related to aging in RA, suggesting that STAT1 may serve as a viable therapeutic target. These findings have the potential to enhance treatment strategies and improve patient outcomes while providing valuable insights into immune cell subpopulations in RA. |
| format | Article |
| id | doaj-art-574c39689bcd43bf9ebcef00207354fc |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-574c39689bcd43bf9ebcef00207354fc2025-08-24T11:29:11ZengNature PortfolioScientific Reports2045-23222025-08-0115111310.1038/s41598-025-15370-5Aging associated immunosenescence in rheumatoid arthritis identified by machine learning and single cell profilingXinxin Ji0Lingyun Li1Yuanzhuo Jiao2Hui Cheng3School of Nursing, Shanxi Medical UniversitySchool of Nursing, Shanxi Medical UniversitySchool of Nursing, Shanxi Medical UniversitySchool of Nursing, Shanxi Medical UniversityAbstract 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 forest (RF), and support vector machine recursive feature elimination (SVM-RFE), were employed to extract feature genes. Single-sample gene set enrichment analysis (ssGSEA) quantified immune cell infiltration, revealing 242 up-regulated and 176 down-regulated differentially expressed genes (DEGs). Notably, high levels of effector memory CD8 T cells and macrophages were found to be associated with robust immune responses. This study successfully identified four biomarkers related to aging in RA, suggesting that STAT1 may serve as a viable therapeutic target. These findings have the potential to enhance treatment strategies and improve patient outcomes while providing valuable insights into immune cell subpopulations in RA.https://doi.org/10.1038/s41598-025-15370-5Rheumatoid arthritisAging-related genesMachine learningImmune infiltrationBiomarkers |
| spellingShingle | Xinxin Ji Lingyun Li Yuanzhuo Jiao Hui Cheng Aging associated immunosenescence in rheumatoid arthritis identified by machine learning and single cell profiling Scientific Reports Rheumatoid arthritis Aging-related genes Machine learning Immune infiltration Biomarkers |
| title | Aging associated immunosenescence in rheumatoid arthritis identified by machine learning and single cell profiling |
| title_full | Aging associated immunosenescence in rheumatoid arthritis identified by machine learning and single cell profiling |
| title_fullStr | Aging associated immunosenescence in rheumatoid arthritis identified by machine learning and single cell profiling |
| title_full_unstemmed | Aging associated immunosenescence in rheumatoid arthritis identified by machine learning and single cell profiling |
| title_short | Aging associated immunosenescence in rheumatoid arthritis identified by machine learning and single cell profiling |
| title_sort | aging associated immunosenescence in rheumatoid arthritis identified by machine learning and single cell profiling |
| topic | Rheumatoid arthritis Aging-related genes Machine learning Immune infiltration Biomarkers |
| url | https://doi.org/10.1038/s41598-025-15370-5 |
| work_keys_str_mv | AT xinxinji agingassociatedimmunosenescenceinrheumatoidarthritisidentifiedbymachinelearningandsinglecellprofiling AT lingyunli agingassociatedimmunosenescenceinrheumatoidarthritisidentifiedbymachinelearningandsinglecellprofiling AT yuanzhuojiao agingassociatedimmunosenescenceinrheumatoidarthritisidentifiedbymachinelearningandsinglecellprofiling AT huicheng agingassociatedimmunosenescenceinrheumatoidarthritisidentifiedbymachinelearningandsinglecellprofiling |