ArticlePloS one2025
Cellular senescence-associated genes in rheumatoid arthritis: Identification and functional analysis.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Geroscience insights into difficult-to-treat rheumatoid arthritis: the role of unhealthy aging, comorbidity, and therapeutic complexity.GeroScience · 2026Review
- GeneCytNet: an interpretable deep learning framework for rheumatoid arthritis classification andFrontiers in immunology · 2026Article
- Integrated genomics reveals cellular senescence-driven molecular networks and immune crosstalk in myopia pathogenesis.Journal of cell communication and signaling · 2025Article
- Rapamycin reveals neuropeptide Y as a regulator of senescence and inflammatory pathways in arthritis.Neuropeptides · 2025Article
- Mechanistic and therapeutic insights into the function of different cell death modalities in rheumatoid arthritis: emphasis on the crosstalk with non-coding RNAs.Frontiers in immunology · 2025Review
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Authors and funding
5 authors.
Funding
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Abstract
Rheumatoid arthritis (RA), a long-term autoinflammatory condition causing joint damage and deformities, involves a multifaceted pathogenesis with genetic, epigenetic, and immune factors, including early immune aging. However, its precise cause remains elusive. Cellular senescence, a hallmark of aging marked by a permanent halt in cell division due to damage and stress, is crucial in aging and related diseases. In our study, we analyzed RA microarray data from the Gene Expression Omnibus (GEO) and focused on cellular senescence genes from the CellAge database. We started by selecting five RA datasets from GEO. Next, we pinpointed 29 differentially expressed genes (DEGs) linked to cellular senescence in RA, aligning them with genes from CellAge. We explored the roles of these DEGs in cellular senescence through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. We then pinpointed three key genes (DHX9, CYR61, and ITGB) using random forest and LASSO Cox regression machine learning techniques. An integrated diagnostic model was created using these genes. We also examined the variance in immune cell infiltration and immune checkpoint gene expression between RA and normal samples. Our methodology's predictive accuracy was confirmed in external validation cohorts. Subsequently, RA samples were classified into three distinct subgroups based on the cellular senescence-associated DEGs, and we compared their immune landscapes. Our findings reveal a significant impact of cellular senescence-related DEGs on immune cell infiltration in RA samples. Hence, a deeper understanding of cellular senescence in RA could offer new perspectives for diagnosis and treatment.
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