Ten genes are considered as potential biomarkers for the diagnosis of dermatomyositis.

OBJECTIVE

This study aimed to identify the biomarkers and mechanisms for dermatomyositis (DM) progression at the transcriptome level through a combination of microarray and bioinformatic analyses.

METHOD: Microarray datasets for skeletal muscle of DM and healthy control (HC) were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified by using GEO2R. Enrichment analyses were performed to understand the functions and enriched pathways of DEGs. A protein-protein interaction network was constructed to identify hub genes. The top 10 hub genes were validated by other GEO datasets. The diagnostic accuracy of the top 10 hub genes for DM was evaluated using the area under the curve of the receiver operating characteristic curve.

RESULT: A total of 63 DEGs were identified between 10 DM samples and 9 HC samples. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis indicated that DEGs are mostly enriched in response to virus, defense response to virus, and type I interferon signaling pathway. 10 hub genes and 3 gene cluster modules were identified by Cytoscape. The identified hub genes were verified by GSE1551 and GSE11971 datasets and proven to be potential biomarkers for the diagnosis of DM.

CONCLUSION

Our work identified 10 valuable genes as potential biomarkers for the diagnosis of DM and explored the potential underlying molecular mechanism of the disease.