Five genes as diagnostic biomarkers of dermatomyositis and their correlation with immune cell infiltration

Abstract

BackgroundDermatomyositis (DM) is a rare autoimmune disease characterized by severe muscle dysfunction, and the immune response of the muscles plays an important role in the development of DM. Currently, the diagnosis of DM relies on symptoms, physical examination, and biopsy techniques. Therefore, we used machine learning algorithm to screen key genes, and constructed and verified a diagnostic model composed of 5 key genes. In terms of immunity, The relationship between 5 genes and immune cell infiltration in muscle samples was analyzed. These diagnostic and immune-cell-related genes may contribute to the diagnosis and treatment of DM.MethodsGSE5370 and GSE128470 datasets were utilised from the Gene Expression Omnibus database as DM test sets. And we also used R software to merge two datasets and to analyze the results of differentially expressed genes (DEGs) and functional correlation analysis. Then, we could detect diagnostic genes adopting least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine recursive feature elimination (SVM-RFE) analyses. The validity of putative biomarkers was assessed using the GSE1551 dataset, and we confirmed the area under the receiver operating characteristic curve (AUC) values. Finally, CIBERSORT was used to evaluate immune cell infiltration in DM muscles and the correlations between disease-related biomarkers and immune cells.ResultsIn this study, a total of 414 DEGs were screened. ISG15, TNFRSF1A, GUSBP11, SERPINB1 and PTMA were identified as potential DM diagnostic biomarkers(AUC > 0.85),and the expressions of 5 genes in DM group were higher than that in healthy group (p < 0.05). Immune cell infiltration analyses indicated that identified DM diagnostic biomarkers may be associated with M1 macrophages, activated NK cells, Tfh cells, resting NK cells and Treg cells.ConclusionThe study identified that ISG15, TNFRSF1A, GUSBP11, SERPINB1 and PTMA as potential diagnostic biomarkers of DM and these genes were closely correlated with immune cell infiltration.This will contribute to future studies in diagnosis and treatment of DM.

Serum LGALS3BP is a potential biomarker for interstitial lung disease in dermatomyositis

“…However, no concrete evidence supports the theory that macrophages cause elevated serum LGALS3BP levels in DM-ILD patients. Furthermore, Xiaohu Zhao et al [34] showed increased in ltration of activated NK cells, M1, and M2 macrophages in DM muscle by immune cell in ltration analysis. In our ndings, Serum…”

Novel endotypes of antisynthetase syndrome identified independent of anti-aminoacyl transfer RNA synthetase antibody specificity that improve prognostic stratification

“…Previous studies have shown that viral infection is an important environmental trigger for DM, while the activation of the innate immune system,45 particularly highly expressed interferons (IFNs) and IFN-regulated proteins, are essential pathological hallmarks of DM 46 47. Among the key genes, ISG15 was strongly upregulated and could be considered a good diagnostic biomarker in DM48; and LGALS3BP encodes galectin 3 binding protein, which is virus-induced, leading to IFN and pro-inflammatory production 49. Accordingly, our findings suggest significant activation of IFN in this endotype, which may explain the dominant skin lesions and muscle involvement in patients within this group.…”

Predicting the Progress of Tuberculosis by Inflammatory Response-Related Genes Based on Multiple Machine Learning Comprehensive Analysis

“…For instance, Lee et al [ 19 ] employed the least absolute contraction and selection operator (LASSO) algorithm to identify genes with high predictive value for treatment response after the first real flare. Similarly, Zhao and Si [ 20 ] utilized the Lasso and support vector machine recursive feature elimination (SVM-RFE) algorithms to identify key diagnostic genes for dermatomyositis. Thus, machine learning holds promise in identifying inflammatory response-related genes with significant implications in the progression of LTBI to ATB.…”