From molecular subgroups to molecular targeted therapy in rheumatoid arthritis: A bioinformatics approach.

ABSTRACT:

1background

Rheumatoid Arthritis (RA) is a heterogeneous autoimmune disease with multiple unidentified pathogenic factors. The inconsistency between molecular subgroups poses challenges for early diagnosis and personalized treatment strategies. In this study, we aimed to accurately distinguish RA patients at the transcriptome level using bioinformatics methods.

2methods

We collected a total of 362 transcriptome datasets from RA patients in three independent samples from the GEO database. Consensus clustering was performed to identify molecular subgroups, and clinical features were assessed. Differential analysis was employed to annotate the biological functions of specifically upregulated genes between subgroups.

3results

Based on consensus clustering of RA samples, w

SUBMITTER: Xu Y 

PROVIDER: S-EPMC11365346 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature