Abstract
BackgroundRheumatoid arthritis (RA) is an autoimmune rheumatic disease that carries a substantial burden for both patients and society. Early diagnosis of RA is essential to prevent disease progression and select an optimal therapeutic strategy. However, RA diagnosis is challenging, partly due to a lack of reliable biomarkers. Here, we aimed to explore the diagnostic signature and establish a predictive model of RA.MethodsThe mRNA expression profiling data of GSE17755, containing blood samples of 112 RA patients and 53 healthy control patients, were obtained from the Gene Expression Omnibus (GEO) database, followed by differential expression, GO (Gene Ontology), and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis. A PPI network was constructed to select candidate hub genes, then logistic regression and random forest models were established based on the identified genes.ResultsSignificantly, we identified 52 differentially expressed genes (DEGs), including 16 upregulated genes and 36 downregulated genes in RA samples compared with control samples. GO and KEGG analysis showed that several immune-related cellular processes were particularly enriched. We identified nine hub genes in the PPI network, including CFL1, COTL1, ACTG1, PFN1, LCP1, LCK, HLA-E, FYN, and HLA-DRA. The logistic regression and random forest models based on the nine identified genes reliably distinguished the RA samples from the healthy samples with substantially high AUC.ConclusionThe diagnostic logistic regression and random forest models based on nine hub genes reliably predicted the occurrence of RA. Our findings could provide new insights into RA diagnostics.
Highlights
Rheumatoid arthritis (RA) is an autoimmune rheumatic inflammatory disorder that influences several organs and tissues and causes chronic synovial inflammation, resulting in chronic disability, joint destruction, and decreased life expectancy [1,2,3]
We aimed to identify blood-derived mRNA-based diagnostic signatures by integrating bioinformatics analysis and machine learning algorithms based on the mRNA expression profiling data of GSE17755 from the Gene Expression Omnibus (GEO) database, containing blood samples of 112 RA patients and 53 healthy control patients
We identified a total of 52 differential expression genes (DEGs) in the RA patients compared with the controls and identified nine hub genes, including CFL1, COTL1, ACTG1, PFN1, LCP1, LCK, HLA-E, FYN, and HLA-DRA
Summary
Rheumatoid arthritis (RA) is an autoimmune rheumatic inflammatory disorder that influences several organs and tissues and causes chronic synovial inflammation, resulting in chronic disability, joint destruction, and decreased life expectancy [1,2,3]. Early diagnosis of RA is essential to prevent the progression of radiologic variations and select the optimal therapeutic strategy [9]. Rheumatoid factor (RF) serum biomarkers have been used as preferred diagnostic criteria for RA for decades of years [10]. Because of the lack of sensitivity (50–90%) and specificity (50–95%) [11] of auxiliary biomarkers, anti-citrullinated protein antibody (ACPA) was included in the diagnostic criteria for RA as developed by the American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) in 2010 [12]. Rheumatoid arthritis (RA) is an autoimmune rheumatic disease that carries a substantial burden for both patients and society. Diagnosis of RA is essential to prevent disease progression and select an optimal therapeutic strategy. We aimed to explore the diagnostic signature and establish a predictive model of RA
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