Unsupervised clustering to differentiate rheumatoid arthritis patients based on proteomic signatures.
Maria Betânia FerreiraM KobayashiR Q CostaT FonsecaM BrandãoJ C OliveiraA MarinhoH Cyrne CarvalhoP RodriguesF ZannadP RossignolA S BarrosJoao Pedro FerreiraPublished in: Scandinavian journal of rheumatology (2023)
Using unsupervised cluster analysis based on proteomic phenotypes, we identified two clusters of RA patients with distinct biomarkers profiles, clinical characteristics, and different outcomes that could reflect different pathophysiological backgrounds. TNF receptor superfamily-related proteins may be used to distinguish subgroups.
Keyphrases
- rheumatoid arthritis patients
- disease activity
- rheumatoid arthritis
- machine learning
- systemic lupus erythematosus
- label free
- ankylosing spondylitis
- single cell
- rna seq
- metabolic syndrome
- adipose tissue
- interstitial lung disease
- skeletal muscle
- dna methylation
- gene expression
- transcription factor
- insulin resistance
- glycemic control