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. 2024 Jan 21;10(3):e24537.
doi: 10.1016/j.heliyon.2024.e24537. eCollection 2024 Feb 15.

Screening and validation of differentially expressed genes in polymyositis

Linmang Qin  1 , Haobo Lin  1 , Guangfeng Zhang  1 , Jieying Wang  1 , Tianxiao Feng  1 , Yunxia Lei  1 , Yuesheng Xie  1 , Ting Xu  1 , Xiao Zhang  1   2
Affiliations

Screening and validation of differentially expressed genes in polymyositis

Linmang Qin et al. Heliyon. .

Abstract

Background: Polymyositis (PM), a prevalent inflammatory myopathy, currently lacks defined pathogenic mechanism. To illuminate its pathogenesis, we integrated bioinformatics and clinical specimens to examine potential aberrant gene expression patterns and their localization.

Methods: We obtained GSE128470 and GSE3112 dataset from the Gene Expression Omnibus, performed Gene Set Enrichment Analysis (GSEA) and immune infiltration analysis using CiberSort, identified differentially expressed genes with Limma, conducted functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, constructed a Protein-Protein Interaction network, and identified hub genes using Cytoscape. ROC analysis evaluated hub gene diagnostic accuracy for PM, validated their expression levels with clinical specimens.

Results: DEG analysis revealed 51 upregulated and 779 downregulated genes. Gene Ontology (GO) analysis implicated Type I interferon (IFN-Ⅰ) signaling, while KEGG pointed to cell adhesion molecule activation and oxidative phosphorylation inhibition. Protein-Protein Interaction (PPI) analysis identified 8 diagnosffftic hub genes. Clinical samples confirmed their upregulation in PM, especially IRF1 and IRF9 between muscle fibers. Different immune cell infiltrations were observed in PM patients versus controls.

Conclusions: Our study explores potential pathogenic factors, diagnostic markers, and immune cells in PM, with a focus on verifying IRF1 and IRF9 upregulation in the IFN-I signaling pathway. These findings bear significance for PM diagnosis and treatment.

Keywords: Bioinformatics; DEGs; HLA; IFN-I; Polymyositis.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Analysis flow chart.
Fig. 2
Fig. 2
Sample quality control and differential expression analysis. (A–B) Principal component analysis of two data sets before and after correction of the batch; (C–D) Volcanic and heat maps of expression patterns of DEGs.
Fig. 3
Fig. 3
GO Functional enrichment and KEGG analysis. (A) cellular components; (B) molecular functions; (C) biological processes. (D–E) Enrichment analysis of KEGG signaling pathway.
Fig. 4
Fig. 4
PPI network diagram and identified hub genes. (A–B) PPI network graph; (C) 20 nodes; (D) Venn diagram of Hub genes.
Fig. 5
Fig. 5
Hub gene expression heat map and diagnostic model. (A)Heat maps of Hub gene expression patterns; (B–D) disease diagnostic models of Hub genes.
Fig. 6
Fig. 6
Immune infiltration and GSEA analysis. (A) The relative proportion of 22 kinds of immune cells; (B) differentially expressed immune cells; (C) Correlation between immune cells; (D) GSEA based on 2 merge datasets.
Fig. 7
Fig. 7
Validation of Hub Genes. (A) H&E staining results of muscle tissue; (B) Immunohistochemical staining results of IRF1 and IRF9 in muscle tissue (C–E) IRF1 and IRF9 Protein expression levels in muscle tissue (F) 8 hub gene mRNA expression levels in muscle tissue. Mean ± SD. *P < 0.05, **P < 0.01.
See this image and copyright information in PMC

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