Identification of Vital Hub Genes and Potential Molecular Pathways of Dermatomyositis by Bioinformatics Analysis

Transcriptome analysis of skeletal muscle in dermatomyositis, polymyositis, and dysferlinopathy, using a bioinformatics approach

Background

Polymyositis (PM) and dermatomyositis (DM) are two distinct subgroups of idiopathic inflammatory myopathies. Dysferlinopathy, caused by a dysferlin gene mutation, usually presents in late adolescence with muscle weakness, degenerative muscle changes are often accompanied by inflammatory infiltrates, often resulting in a misdiagnosis as polymyositis.

Objective

To identify differential biological pathways and hub genes related to polymyositis, dermatomyositis and dysferlinopathy using bioinformatics analysis for understanding the pathomechanisms and providing guidance for therapy development.

Methods

We analyzed intramuscular ribonucleic acid (RNA) sequencing data from seven dermatomyositis, eight polymyositis, eight dysferlinopathy and five control subjects. Differentially expressed genes (DEGs) were identified by using DESeq2. Enrichment analyses were performed to understand the functions and enriched pathways of DEGs. A protein-protein interaction (PPI) network was constructed, and clarified the gene cluster using the molecular complex detection tool (MCODE) analysis to identify hub genes.

Results

A total of 1,048, 179 and 3,807 DEGs were detected in DM, PM and dysferlinopathy, respectively. Enrichment analyses revealed that upregulated DEGs were involved in type 1 interferon (IFN1) signaling pathway in DM, antigen processing and presentation of peptide antigen in PM, and cellular response to stimuli in dysferlinopathy. The PPI network and MCODE cluster identified 23 genes related to type 1 interferon signaling pathway in DM, 4 genes (PDIA3, HLA-C, B2M, and TAP1) related to MHC class 1 formation and quality control in PM, and 7 genes (HSPA9, RPTOR, MTOR, LAMTOR1, LAMTOR5, ATP6V0D1, and ATP6V0B) related to cellular response to stress in dysferliniopathy.

Conclusion

Overexpression of genes related to the IFN1 signaling pathway and major histocompatibility complex (MHC) class I formation was identified in DM and PM, respectively. In dysferlinopathy, overexpression of HSPA9 and the mTORC1 signaling pathway genes was detected.

Roles of interferon induced protein with tetratricopeptide repeats (IFIT) family in autoimmune disease

Interferon-induced tetrapeptide repeat (IFIT) family proteins are an important component of the antiviral immune response. There are four known members of the human IFIT family, namely IFIT1, IFIT2, IFIT3 and IFIT5. More and more evidence shows that IFIT family members are involved in a variety of pathophysiological processes in vivo, regulate the homeostasis and differentiation of a variety of cells including immune cells, and are closely related to a variety of autoimmune diseases, which is expected to become a new therapeutic target. This review reviews the biological roles of different IFIT proteins in various autoimmune diseases, and highlights the potential use of these molecules as biomarkers and prognostic factors in autoimmune diseases, with a view to providing ideas for exploring the diagnosis and treatment of autoimmune diseases.

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. 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.

Conclusion

The 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.

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.