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

Background: Dermatomyositis (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. Methods: GSE5370 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. Results: In this study, a total of 414 DEGs were screened. Conclusion: The study identified that