Diagnosing muscle disease in a cohort of classic dermatomyositis patients seen at a rheumatologic dermatology outpatient clinic

Ahmed, Sarah; Concha, Josef Symon S.; Chakka, Srita; Krain, Rebecca L; Zamalin, Danielle; Foulke, Galen; Werth, Victoria P.

doi:10.1016/j.jaad.2021.05.026

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…Ahmed et al. pointed out that skin biopsy was sufficient to diagnose DM without muscle biopsy in the presence of the characteristic skin manifestations and muscle symptoms of DM ( 11 ). Therefore, the biopsy of skin lesions is important for the early diagnosis of DM.…”

Section: Introductionmentioning

confidence: 99%

Identification of Biomarkers Associated With CD4+ T-Cell Infiltration With Gene Coexpression Network in Dermatomyositis

Huang

Zhang

et al. 2022

Front. Immunol.

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BackgroundDermatomyositis is an autoimmune disease characterized by damage to the skin and muscles. CD4+ T cells are of crucial importance in the occurrence and development of dermatomyositis (DM). However, there are few bioinformatics studies on potential pathogenic genes and immune cell infiltration of DM. Therefore, this study intended to explore CD4+ T-cell infiltration–associated key genes in DM and construct a new model to predict the level of CD4+ T-cell infiltration in DM.MethodsGSE46239, GSE142807, GSE1551, and GSE193276 datasets were downloaded. The WGCNA and CIBERSORT algorithms were performed to identify the most correlated gene module with CD4+ T cells. Matascape was used for GO enrichment and KEGG pathway analysis of the key gene module. LASSO regression analysis was used to identify the key genes and construct the prediction model. The correlation between the key genes and CD4+ T-cell infiltration was investigated. GSEA was performed to research the underlying signaling pathways of the key genes. The key gene-correlated transcription factors were identified through the RcisTarget and Gene-motif rankings databases. The miRcode and DIANA-LncBase databases were used to build the lncRNA-miRNA-mRNA network.ResultsIn the brown module, 5 key genes (chromosome 1 open reading frame 106 (C1orf106), component of oligomeric Golgi complex 8 (COG8), envoplakin (EVPL), GTPases of immunity-associated protein family member 6 (GIMAP6), and interferon-alpha inducible protein 6 (IFI6)) highly associated with CD4+ T-cell infiltration were identified. The prediction model was constructed and showed better predictive performance in the training set, and this satisfactory model performance was validated in another skin biopsy dataset and a muscle biopsy dataset. The expression levels of the key genes promoted the CD4+ T-cell infiltration. GSEA results revealed that the key genes were remarkably enriched in many immunity-associated pathways, such as JAK/STAT signaling pathway. The cisbp_M2205, transcription factor-binding site, was enriched in C1orf106, EVPL, and IF16. Finally, 3,835 lncRNAs and 52 miRNAs significantly correlated with key genes were used to build a ceRNA network.ConclusionThe C1orf106, COG8, EVPL, GIMAP6, and IFI6 genes are associated with CD4+ T-cell infiltration. The prediction model constructed based on the 5 key genes may better predict the level of CD4+ T-cell infiltration in damaged muscle and lesional skin of DM. These key genes could be recognized as potential biomarkers and immunotherapeutic targets of DM.

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Section: Introductionmentioning

confidence: 99%