Evidence map›Paper›PMID 41079379›Full record

ArticleCentral-European journal of immunology2025

Construction and validation of a diagnostic model for dermatomyositis based on the LASSO algorithm.

Changyi Lin, Peicheng Wu, Xuelan You, Minghui Song, Youtian Liu, Qiong Deng, Xueyan Huang, Zhongxiao Fan, Damei Ye, Ruimin Lin and 1 more

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Article in Central-European journal of immunology, 2025. 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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5 · Who and what money

Authors and funding

11 authors.

Changyi Lin *Sanming First Hospital Affiliated to Fujian Medical University, China.
Peicheng WuSanming First Hospital Affiliated to Fujian Medical University, China.
Xuelan YouSanming First Hospital Affiliated to Fujian Medical University, China.
Minghui SongSanming First Hospital Affiliated to Fujian Medical University, China.
Youtian LiuSanming First Hospital Affiliated to Fujian Medical University, China.
Qiong DengSanming First Hospital Affiliated to Fujian Medical University, China.
Xueyan HuangSanming First Hospital Affiliated to Fujian Medical University, China.
Zhongxiao FanSanming First Hospital Affiliated to Fujian Medical University, China.
Damei YeSanming First Hospital Affiliated to Fujian Medical University, China.
Ruimin LinSanming First Hospital Affiliated to Fujian Medical University, China.
Chaoyan XuSanming First Hospital Affiliated to Fujian Medical University, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Dermatomyositis (DM) is the most prevalent disease among myositis patients. The immune response is crucial in DM development. Bioinformatics research on immune-related genes in DM is limited. This study attempted to construct a diagnostic model and investigate immune characteristics of immune-related differentially expressed genes (DEGs), which could aid in DM diagnosis. Material and methods: GSE46239 and GSE39454 datasets were from the GEO database, and batch effects were eliminated for use as the DM training set. DEG were identified and enrichment analysis was conducted between DM and normal samples. Intersection of DEGs and immune-related genes yielded immune-related DEGs, which were utilized to generate a PPI network. The diagnostic model was built by the LASSO method. The diagnostic model and effectiveness of model genes were evaluated through GSE143323. The correlation between immune cell infiltration in DM and diagnostic genes was analyzed. Finally, expression levels of HLA genes in DM and their correlation with diagnostic genes were examined. Results: A total of 350 DEGs were identified. Seventy-one immune-related DEGs were screened. LASSO regression identified 5 immune-related DEGs (ACKR1, DHX58, IRF7, ISG15, and PSMB8) for constructing the DM diagnostic model. The model showed good effectiveness in training and validation sets (AUC of 0.99 and 0.958, respectively), and 5 immune-related DEGs also exhibited good effectiveness (AUC > 0.784). Diagnostic genes in DM were associated with M1 macrophages, M2 macrophages, resting dendritic cells, and certain HLA genes. Conclusions: We constructed a DM diagnostic model using ACKR1, DHX58, IRF7, ISG15, and PSMB8, which were closely related to immune cells and HLA. This model could contribute to research in DM diagnosis.

Indexed as

dermatomyositisdiagnostic modelHLAimmune characteristicsimmune-related genes

Identifiers

PMID41079379
PMCPMC12512114

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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.