Identification of Diagnostic Signatures and Immune Cell Infiltration Characteristics in Rheumatoid Arthritis by Integrating Bioinformatic Analysis and Machine-Learning Strategies.

ABSTRACT:

Background

Rheumatoid arthritis (RA) refers to an autoimmune rheumatic disease that imposes a huge burden on patients and society. Early RA diagnosis is critical to preventing disease progression and selecting optimal therapeutic strategies more effectively. In the present study, the aim was at examining RA's diagnostic signatures and the effect of immune cell infiltration in this pathology.

Methods

Gene Expression Omnibus (GEO) database provided three datasets of gene expressions. Firstly, this study adopted R software for identifying differentially expressed genes (DEGs) and conducting functional correlation analyses. Subsequently, we integrated bioinformatic analysis and machine-learning strategies for screening and determining RA's diagnostic signatures and further verif

SUBMITTER: Yu R 

PROVIDER: S-EPMC8526926 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature