Evidence map›Paper›PMID 42154423›Full record

ArticleClinical rheumatology2026

SLA and DDX60L as potential diagnostic biomarkers for subclinical atherosclerosis in systemic lupus erythematosus: a transcriptomic and clinical validation study.

Yumin Huang, Wei Liu, Xichao Yang, Hui Wang, Jia Chen, Xue Cao, Ying Ren, Yuan Feng

Abstract readValidation Study
PubMed Publisher
In one paragraph

Article in Clinical rheumatology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Yumin Huang *Department of Rheumatology and Immunology, Tangdu Hospital, The Fourth Military Medical University, No. 569 Xinsi Road, Baqiao District, Xi'an City, Shaanxi Province, 710038, China.
Wei Liu *Department of Rheumatology and Immunology, Tangdu Hospital, The Fourth Military Medical University, No. 569 Xinsi Road, Baqiao District, Xi'an City, Shaanxi Province, 710038, China.
Xichao YangDepartment of Rheumatology and Immunology, Honghui Hospital, Xi'an Jiaotong University, No. 555 Youyi East Road, Beilin District, Xi'an City, Shaanxi Province, 710061, China.
Hui WangDepartment of Rheumatology and Immunology, Tangdu Hospital, The Fourth Military Medical University, No. 569 Xinsi Road, Baqiao District, Xi'an City, Shaanxi Province, 710038, China.
Jia ChenThe College of Life Sciences, Northwest University, Shaanxi Province, No. 229 Taibai North Road, Xi'an City, 710127, China.
Xue CaoDepartment of Rheumatology and Immunology, Tangdu Hospital, The Fourth Military Medical University, No. 569 Xinsi Road, Baqiao District, Xi'an City, Shaanxi Province, 710038, China.
Ying RenDepartment of Rheumatology and Immunology, Tangdu Hospital, The Fourth Military Medical University, No. 569 Xinsi Road, Baqiao District, Xi'an City, Shaanxi Province, 710038, China.
Yuan FengDepartment of Rheumatology and Immunology, Tangdu Hospital, The Fourth Military Medical University, No. 569 Xinsi Road, Baqiao District, Xi'an City, Shaanxi Province, 710038, China. 154568808@qq.com.ORCID http://orcid.org/0000-0003-4925-8179

Funding

The Youth Independent Innovation Science Foundation of Tangdu Hospital, The Fourth Military Medical University 2023CTDON029
6 · The paper itself

Abstract

backgroundSystemic 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.

methodsTranscriptomic 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.

resultsA 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).

conclusionDDX60L 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.

Indexed as

AtherosclerosisDEAD-box RNA HelicasesLupus Erythematosus, SystemicBiomarkersGene Expression ProfilingHumansMachine LearningTranscriptomeBiomarkersDEAD-box RNA HelicasesAtherosclerosisDiagnostic genesIntegrated transcriptomic analysisMachine learningSystemic lupus erythematosus

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.