Clinical rheumatology · 2026
SLA and DDX60L as potential diagnostic biomarkers for subclinical atherosclerosis in systemic lupus erythematosus: a transcriptomic and clinical validation study.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Huang Yumin, Liu Wei, Yang Xichao, Wang Hui, Chen Jia, Cao Xue, Ren Ying, Feng Yuan
Аннотация
BACKGROUND: Systemic lupus erythematosus (SLE) patients face a 5-tenfold increased risk of atherosclerosis (AS), with subclinical lesions often progressing asymptomatically until life-threatening cardiovascular events occur. This study aimed to identify reliable molecular biomarkers for the early detection of SLE-associated AS.
METHODS: Transcriptomic datasets (GSE154851 for SLE, GSE100927 for AS) were retrieved from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) and differential gene expression (DEG) analysis were performed to screen disease-related genes. Three machine learning algorithms (LASSO, SVM-RFE, Random Forest) were integrated to identify core diagnostic genes. The diagnostic efficacy was validated using external datasets (GSE37356, GSE43292) and clinical peripheral blood samples via quantitative real-time PCR (qPCR). Gene set enrichment analysis (GSEA) and CIBERSORT immune infiltration analysis were conducted to explore the underlying molecular mechanisms.
RESULTS: A total of 47 shared pathogenic genes were identified between SLE and AS. Subsequent screening by machine learning algorithms pinpointed DDX60L and SLA as core diagnostic biomarkers. Both genes exhibited robust diagnostic performance in external datasets: DDX60L had an AUC of 0.761 (SLE) and 0.731 (AS), while SLA showed an AUC of 0.971 (SLE) and 0.748 (AS). qPCR validation confirmed a distinct expression gradient (healthy < SLE < AS < SLE + AS). GSEA revealed enrichment of the JAK-STAT signaling pathway, immune regulation, and metabolic pathways (folate biosynthesis, lysine degradation) associated with these two genes. Immune infiltration analysis indicated that DDX60L and SLA expression correlated with the abundance of pro-atherogenic immune cells (neutrophils, M0 macrophages).
CONCLUSION: DDX60L and SLA serve as reliable diagnostic biomarkers for subclinical AS in SLE patients, potentially regulating disease progression via the JAK-STAT pathway and immune cell dysregulation. These findings provide a theoretical basis for early risk stratification and targeted intervention in high-risk SLE populations.. Key Points • DDX60L and SLA are identified as novel diagnostic biomarkers for subclinical atherosclerosis in SLE patients. • The two genes exhibit a distinct expression gradient correlating with disease severity (Healthy healthy< SLE < AS < SLE+AS) • DDX60L and SLA may regulate SLE-associated AS progression via the JAK-STAT signaling pathway and immune cell dysregulation. • The findings provide a theoretical basis for early screening and prevention of cardiovascular complications in SLE patients.
METHODS: Transcriptomic datasets (GSE154851 for SLE, GSE100927 for AS) were retrieved from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) and differential gene expression (DEG) analysis were performed to screen disease-related genes. Three machine learning algorithms (LASSO, SVM-RFE, Random Forest) were integrated to identify core diagnostic genes. The diagnostic efficacy was validated using external datasets (GSE37356, GSE43292) and clinical peripheral blood samples via quantitative real-time PCR (qPCR). Gene set enrichment analysis (GSEA) and CIBERSORT immune infiltration analysis were conducted to explore the underlying molecular mechanisms.
RESULTS: A total of 47 shared pathogenic genes were identified between SLE and AS. Subsequent screening by machine learning algorithms pinpointed DDX60L and SLA as core diagnostic biomarkers. Both genes exhibited robust diagnostic performance in external datasets: DDX60L had an AUC of 0.761 (SLE) and 0.731 (AS), while SLA showed an AUC of 0.971 (SLE) and 0.748 (AS). qPCR validation confirmed a distinct expression gradient (healthy < SLE < AS < SLE + AS). GSEA revealed enrichment of the JAK-STAT signaling pathway, immune regulation, and metabolic pathways (folate biosynthesis, lysine degradation) associated with these two genes. Immune infiltration analysis indicated that DDX60L and SLA expression correlated with the abundance of pro-atherogenic immune cells (neutrophils, M0 macrophages).
CONCLUSION: DDX60L and SLA serve as reliable diagnostic biomarkers for subclinical AS in SLE patients, potentially regulating disease progression via the JAK-STAT pathway and immune cell dysregulation. These findings provide a theoretical basis for early risk stratification and targeted intervention in high-risk SLE populations.. Key Points • DDX60L and SLA are identified as novel diagnostic biomarkers for subclinical atherosclerosis in SLE patients. • The two genes exhibit a distinct expression gradient correlating with disease severity (Healthy healthy< SLE < AS < SLE+AS) • DDX60L and SLA may regulate SLE-associated AS progression via the JAK-STAT signaling pathway and immune cell dysregulation. • The findings provide a theoretical basis for early screening and prevention of cardiovascular complications in SLE patients.