Abstract
BACKGROUND: Childhood-onset systemic lupus erythematosus (cSLE) is a chronic autoimmune disease that is often more severe than adult-onset SLE and is challenging to diagnose due to its variable presentation and lack of specific diagnostic tests. OBJECTIVES: This study aimed to identify potential diagnostic biomarkers for cSLE by analyzing differentially expressed genes (DEGs) using machine learning algorithms. METHODS: In this study, we utilized the Gene Expression Omnibus database to investigate the DEGs between cSLE and normal samples, conducting a functional enrichment analysis on DEGs. Subsequently, we employed machine learning algorithms, including Least Absolute Shrinkage and Selection Operator regression and Support Vector Machine-Recursive Feature Elimination, to identify hub DEGs, which serve as crucial biomarkers. We delved into the role of these hub DEGs in the pathogenesis of the disease and the correlation between these hub DEGs and immune infiltration by comprehensive immune infiltration analysis using the CIBERSORT algorithm. RESULTS: We identified 110 DEGs in cSLE, including 95 upregulated and 15 downregulated genes. Functional annotation revealed that these DEGs were involved in immune response processes, viral defense mechanisms, and regulation of interferon responses. Machine learning algorithms identified CCR1 and SAMD9L as hub DEGs, which were validated in multiple datasets and demonstrated high diagnostic value for cSLE. Mechanistic exploration suggested that CCR1 and SAMD9L are involved in immune response modulation, particularly in interferon signaling and the innate immune system. Assessment of immune cell infiltration revealed significant differences in immune cell composition between cSLE patients and healthy controls, with cSLE patients exhibiting a higher proportion of neutrophils. Moreover, CCR1 and SAMD9L expression levels showed positive correlations with neutrophil infiltration and other immune cell types. CONCLUSION: CCR1 and SAMD9L were identified as potential diagnostic biomarkers for cSLE using machine learning and were validated in multiple datasets. These findings provide novel insights into the biological underpinnings of cSLE.