Abstract

Rheumatoid arthritis (RA) is a systemic autoimmune inflammatory disorder. KLRB1 (killer cell lectin like receptor B1), which is intricately linked to immune modulation and inflammatory responses, represents a promising biomarker for the identification of RA. This study mainly explores the relationship between KLRB1 and RA, and identifies biomarkers related to KLRB1 in RA, providing theoretical support for the diagnosis and treatment of RA. The transcriptome data of RA were sourced from the public database. Differential expression analysis was used to identify differentially expressed genes (DEGs) and KLRB1-related DEGs. Additionally, key module genes associated with RA were determined using weighted gene co-expression network analysis (WGCNA). Subsequently, the DEGs, KLRB1-related DEGs, and key module genes were subjected to an intersection analysis to identify candidate genes. Afterwards, machine learning, expression validation, and diagnostic evaluation of the aforementioned genes were conducted to identify biomarkers, and a nomogram was constructed to evaluate the diagnostic value of the biomarkers. Furthermore, enrichment analysis and immune microenvironment analysis were carried out for further evaluation of the role of biomarkers in the regulatory mechanisms in RA. Ultimately, the expression of biomarkers in clinical samples was validated through the utilization of reverse transcription quantitative polymerase chain reaction (RT-qPCR). The study identified 1,264 DEGs, 293 KLRB1-related DEGs, and 1,379 key module genes, which resulted in the selection of 36 candidate genes. Thereafter, 2 biomarkers (ADAMDEC1 and CXCL13) associated with KLRB1 in RA were identified through machine learning, expression validation, and diagnostic evaluation. The nomogram model indicated that these biomarkers possess considerable diagnostic value for patients with RA.