This study aims to investigate the expression differences of peripheral blood mononuclear cells (PBMCs) in patients with elderly rheumatoid arthritis (ERA). Differentially expressed genes (DEGs) of PBMCs between young patients with RA (RA_Y) and elderly patients with RA (RA_A) were identified by RNA sequencing using the DESeq2 package, followed by bioinformatics analysis. The overlapped targets of the current DEGs and proteomic differentially expressed proteins (another set of unpublished data) were identified and further validated. The bioinformatics analysis revealed significant transcriptomic heterogeneity between RA_A and RA_Y. A total of 348 upregulated and 363 downregulated DEGs were identified. Gene functional enrichment analysis indicated that the DEGs, which represented senescence phenotype for patients with ERA, were enriched in pathways such as Phosphatidylinositol3 kinase/AKT serine-threonine protein kinase (PI3K/Akt) signaling, Mitogen-activated protein kinases (MAPK) signaling, toll-like receptor family, neutrophil degranulation, and immune-related pathways. Gene set enrichment analysis further confirmed the activation of humoral immune response pathways in RA_A. Quantitative polymerase chain reaction validated the expression of five representative DEGs such as SPTA1 , SPTB , VNN1 , TNXB , and KRT1 in PBMCs of patients with ERA. Patients with ERA have significant senescence phenotype differences versus the young patients. The DEGs identified may facilitate exploring the biomarkers of senescence in RA.
Keyphrases
- rheumatoid arthritis
- disease activity
- protein kinase
- bioinformatics analysis
- immune response
- toll like receptor
- pi k akt
- signaling pathway
- ankylosing spondylitis
- middle aged
- single cell
- interstitial lung disease
- genome wide
- endothelial cells
- systemic lupus erythematosus
- cell proliferation
- poor prognosis
- stress induced
- inflammatory response
- dendritic cells
- binding protein
- machine learning
- high resolution
- dna methylation
- copy number
- cell death
- end stage renal disease
- rna seq
- cross sectional
- oxidative stress
- gene expression
- artificial intelligence
- long non coding rna
- deep learning
- peritoneal dialysis
- drug induced