Evidence map›Paper›PMID 42062698›Full record

ArticleClinical rheumatology2026

Integrated multi-dimensional analyses reveal a BTN3A2-centered diagnostic risk score and a monocyte-T cell axis as central drivers of dermatomyositis.

Jing Yang, Long-Kuan Yin, Jin Tang

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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.

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3 authors.

Jing YangDepartment of Rheumatology and Immunology, Capital Medical University Affiliated Beijing Anzhen Hospital - Nanchong Hospital, Nanchong Central Hospital, Nan chong, China.
Long-Kuan YinDepartment of Gastrointestinal Surgery, Fushun People's Hospital, Fushun, Sichuan, 643200, China.
Jin TangDepartment of Gastrointestinal Surgery, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, 637000, China. zhaotang1022@163.com.ORCID http://orcid.org/0000-0002-4810-7849

Funding

Social Science Research Project of the 14th Five-Year Plan of Nanchong City NC21B082Southwest Medical University School-level Online Education and Teaching Reform Special Research Project 2020XSJG-C02-17Special Research Project of Orthopedics of Sichuan Medical Association 2021SAT27
6 · The paper itself

Abstract

BACKGROUND AND

aimDermatomyositis (DM) is an immune-mediated myopathy marked by chronic inflammation and heterogeneous clinical trajectories. The molecular determinants driving disease onset and progression remain poorly defined. This study aimed to construct a high-accuracy diagnostic risk model, identify causally relevant genes, and uncover cell-type-specific immune circuits contributing to DM pathogenesis, with emphasis on BTN3A2.

methodsWe integrated three bulk transcriptomic datasets to identify dysregulated genes and performed functional enrichment analyses. An 18-gene diagnostic risk score was generated using LASSO and validated externally. Summary-based Mendelian randomization with tissue-specific eQTLs was applied to identify causal genes. Single-cell RNA sequencing (GSE190684) was analyzed to map cellular heterogeneity, quantify risk-score and BTN3A2 distributions, and evaluate ligand-receptor communication across clinical states and expression subgroups.

resultsBulk analyses identified 2,051 dysregulated genes enriched in antiviral responses, antigen presentation, and immune activation. Summary-data based Mendelian Randomization (SMR) uncovered 85 causal genes shared across skin and muscle, converging on MHC-related and autoimmune pathways. Integrating causal and transcriptional evidence yielded 16 core DM genes, highlighting BTN3A2 for its potential dual diagnostic and suggestive causal relevance. The 18-gene risk score demonstrated excellent predictive performance (AUC 0.957/0.724). Single-cell profiling revealed that both the risk score and BTN3A2 were predominantly enriched in CD14⁺/CD16⁺ monocytes and T-cell subsets. Cell-cell communication analysis identified a monocyte-derived LGALS9-CD44/CD45 signaling axis as the key interaction pathway distinguishing clinical states and risk groups.

conclusionThis multi-omics study establishes a precise diagnostic risk score and suggests that BTN3A2 may serve as a mechanistically relevant biomarker in DM. The discovery of an LGALS9-CD44/CD45 monocyte-T cell circuit provides new insight into DM immunopathology and highlights actionable targets for precision diagnosis and therapy. Key points • An externally validated, high-accuracy 18-gene diagnostic risk score was established, providing a robust molecular tool for precision risk stratification in dermatomyositis. • BTN3A2 emerged as a gene with both diagnostic significance and genetic causal relevance, positioning it as a pivotal regulator of dermatomyositis immunopathology. • Single-cell RNA sequencing identified CD14⁺/CD16⁺ monocytes and T-cell subsets as the major cellular sources driving elevated risk and BTN3A2 overexpression, while intercellular communication analysis uncovered a monocyte-derived LGALS9-CD44/CD45 axis that mechanistically links genetic risk, transcriptional activation, and immune dysfunction. • These findings delineate a mechanistic, multi-dimensional framework that integrates diagnostic markers, genetic susceptibility, and cell-cell signaling, offering translational insights for precision diagnostics and targeted immunotherapies in dermatomyositis.

Indexed as

DermatomyositisMonocytesT-LymphocytesGenetic Predisposition to DiseaseHumansBTN3A2DermatomyositisDiagnostic risk scoreLGALS9-CD44/CD45 signal axisMulti-dimensional analysis

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