Biomarkers of disease activity in dermatomyositis

Lü, Xin; Peng, Qinglin; Wang, Guochun

doi:10.1097/bor.0000000000000905

Cited by 5 publications

(3 citation statements)

References 43 publications

(54 reference statements)

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“…Typical MSAs identified in patients with DM are anti-Mi-2 Abs, anti-aminoacyl-tRNA synthetase (ARS) Abs, anti-nuclear matrix protein 2 (anti-NXP2) Abs, Abs against transcription intermediary factor 1γ (TIF1γ/α), and anti-small ubiquitin-like modifier activating enzyme Abs (Anti-SAE) [133,154,155]. Abs against Mi-2, NXP2, and SAE appear to correlate with disease activity in DM.…”

Section: Myositis-specific Autoantibodies (Msa)mentioning

confidence: 99%

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Current Biomarker Strategies in Autoimmune Neuromuscular Diseases

Oeztuerk,

Henes,

Schroeter

et al. 2023

Cells

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Inflammatory neuromuscular disorders encompass a diverse group of immune-mediated diseases with varying clinical manifestations and treatment responses. The identification of specific biomarkers has the potential to provide valuable insights into disease pathogenesis, aid in accurate diagnosis, predict disease course, and monitor treatment efficacy. However, the rarity and heterogeneity of these disorders pose significant challenges in the identification and implementation of reliable biomarkers. Here, we aim to provide a comprehensive review of biomarkers currently established in Guillain-Barré syndrome (GBS), chronic inflammatory demyelinating polyneuropathy (CIDP), myasthenia gravis (MG), and idiopathic inflammatory myopathy (IIM). It highlights the existing biomarkers in these disorders, including diagnostic, prognostic, predictive and monitoring biomarkers, while emphasizing the unmet need for additional specific biomarkers. The limitations and challenges associated with the current biomarkers are discussed, and the potential implications for disease management and personalized treatment strategies are explored. Collectively, biomarkers have the potential to improve the management of inflammatory neuromuscular disorders. However, novel strategies and further research are needed to establish clinically meaningful biomarkers.

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Section: Myositis-specific Autoantibodies (Msa)mentioning

confidence: 99%

“…Abs against Mi-2, NXP2, and SAE appear to correlate with disease activity in DM. Moreover, their sequential testing may be supportive for predictive purposes [133]. Anti-Jo-1 Abs are the most frequent Abs reported among the group of anti-ARS Abs (around 15-30%).…”

Section: Myositis-specific Autoantibodies (Msa)mentioning

confidence: 99%

Current Biomarker Strategies in Autoimmune Neuromuscular Diseases

Oeztuerk,

Henes,

Schroeter

et al. 2023

Cells

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“…Adaptive immune mechanisms of DM include B, T cell activation and overexpression of major histocompatibility complex (MHC) class I (HLA-ABC) [11,12]. Of note, plasma cells generate multiple myositis-speci c antibodies (MSAs), such as those against transcription intermediary factor 1γ (anti-TIF-1γ), melanoma differentiation-associated gene 5 (anti-MDA-5), nuclear matrix protein 2 (anti-NXP-2), and small ubiquitinlike modi er-activating enzyme (anti-SAE), which may often imply distinct extramuscular involvement [13][14][15]. Yet, the precise immune mechanisms are still poorly understood.…”

Section: Introductionmentioning

confidence: 99%

Characterization of necroptosis and immune landscape in dermatomyositis by bioinformatics analysis and machine learning

Dai

Zhang

et al. 2023

Preprint

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BackgroundDermatomyositis (DM) is a cell-mediated autoimmune disease of intricate aetiology. Necroptosis is a newly identified form of programmed cell death. This research aimed to explore the value of necroptosis-related genes in DM. Methods DM datasets were obtained from Gene Expression Omnibus (GEO) database. Necroptosis-related differentially expressed genes (NRDEGs) of DM were identified by intersecting differentially expressed genes (DEGs) with necroptosis gene set. Then, signature genes of NRDEGs were determined by the machine learning method of random forest (RF), support vector machine-recursive feature elimination (SVM-RFE), and the least absolute shrinkage and selection operator regression (LASSO). Moreover, immune microenvironment of DM and its correlation with signature genes were created to assess immune dysregulation. Besides, functional enrichment analysis, protein-protein interaction (PPI) co-expression network construction, transcription factor (TF)-miRNA network analysis were collectively performed on signature genes. In addition, the Mfuzz expression pattern clustering and functional enrichment based on the optimal signature was conducted. Results A total of 2524 DEGs in GSE143323 were obtained, including BAX, BIRC3, JAK3, SPATA2L and TNFSF10. Through the intersection with necroptosis gene set, 28 NRDEGs were examined. Furthermore, five signature genes were identified via machine learning and were verified in GSE1551. In immune landscape evaluation, signature genes were positively correlated with most immunocytes, human leukocyte antigen (HLA) genes, and immune checkpoints. Among them, TNFSF10 was the best diagnostic signature of DM. The most highly associated module genes with TNFSF10 by Mfuzz expression pattern clustering mainly enriched in immunity and immunoregulation. Conclusions Necroptosis occurs in DM, and is closely related to DM immune microenvironment, which merits further investigations in the necroptosis of DM pathogenesis.

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Autoimmune inflammatory myopathy biomarkers

Essouma

2024

Clinica Chimica Acta

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Biomarkers of disease activity in dermatomyositis

Cited by 5 publications

References 43 publications

Current Biomarker Strategies in Autoimmune Neuromuscular Diseases

Current Biomarker Strategies in Autoimmune Neuromuscular Diseases

Characterization of necroptosis and immune landscape in dermatomyositis by bioinformatics analysis and machine learning

Autoimmune inflammatory myopathy biomarkers

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