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Bioinformatics analyses of gene expression profile identify key genes and functional pathways involved in cutaneous lupus erythematosus

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Abstract

Background

Lupus erythematosus is an autoimmune disease that causes damage to multiple organs ranging from skin lesions to systemic manifestations. Cutaneous lupus erythematosus (CLE) is a common type of lupus erythematosus (LE), but its molecular mechanisms are currently unknown. The study aimed to explore changes in the gene expression profiles and identify key genes involved in CLE, hoping to uncover its molecular mechanism and identify new targets for CLE.

Method

We analyzed the microarray dataset (GSE109248) derived from the Gene Expression Omnibus (GEO) database, which was a transcriptome profiling of CLE cutaneous lesions. The differentially expressed genes (DEGs) were identified, and the functional annotation of DEGs was performed with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Protein–protein interaction (PPI) network was also constructed to identify hub genes involved in CLE.

Result

A total of 755 up-regulated DEGs and 405 down-regulated DEGs were identified. GO enrichment analysis showed that defense response to virus, immune response, and type I interferon signaling pathway were the most significant enrichment items in DEGs. The KEGG pathway analysis identified 51 significant enrichment pathways, which mainly included systemic lupus erythematosus, osteoclast differentiation, cytokine-cytokine receptor interaction, and primary immunodeficiency. Based on the PPI network, the study identified the top 10 hub genes involved in CLE, which were CXCL10, CCR7, FPR3, PPARGC1A, MMP9, IRF7, IL2RG, SOCS1, ISG15, and GSTM3. By comparison between subtypes, the results showed that ACLE had the least DEGs, while CCLE showed the most gene and functional changes.

Conclusion

The identified hub genes and functional pathways found in this study may expand our understanding on the underlying pathogenesis of CLE and provide new insights into potential biomarkers or targets for the diagnosis and treatment of CLE.

Key Points

• The bioinformatics analysis based on CLE patients and healthy controls was performed and 1160 DEGs were identified

• The 1160 DEGs were mainly enriched in biological processes related to immune responses, including innate immune response, type I interferon signaling pathway, interferon-γ-mediated signaling pathway, positive regulation of T cell proliferation, regulation of immune response, antigen processing, and presentation via MHC class Ib and so on

• KEGG pathway enrichment analysis indicated that DEGs were mainly enriched in several immune-related diseases and virus infection, including systemic lupus erythematosus, primary immunodeficiency, herpes simplex infection, measles, influenza A, and so on

• The hub genes such as CXCL10, IRF7, MMP9, CCR7, and SOCS1 may become new markers or targets for the diagnosis and treatment of CLE

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Data availability

The data used for analysis in this study are available from the Gene Expression Omnibus database freely.

Code availability

The code used for analysis in this study is available from the limma packages in R language freely.

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Acknowledgements

We gratefully thank many researchers for providing technical assistance.

Funding

This work was supported by grants from The Affiliated Hospital of Hubei Minzu University, Enshi, Hubei Province, China.

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Authors and Affiliations

  1. Department of Rheumatology and Immunology, The Affiliated Hospital of Hubei Minzu University, Enshi, Hubei Province, China

    Zhen-yu Gao, Lin-chong Su, Qing-chao Wu, Jiao-e Sheng, Yun-long Wang, Yu-fang Dai, An-ping Chen, San-shan He, Xia Huang & Guo-qing Yan

  2. Hubei Provincial Key Laboratory of Occurrence and Intervention of Rheumatic Disease, The Affiliated Hospital of Hubei Minzu University, Enshi, Hubei Province, China

    Zhen-yu Gao, Lin-chong Su, Qing-chao Wu, Jiao-e Sheng, Yun-long Wang, Yu-fang Dai, An-ping Chen, San-shan He, Xia Huang & Guo-qing Yan

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Gao, Zy., Su, Lc., Wu, Qc. et al. Bioinformatics analyses of gene expression profile identify key genes and functional pathways involved in cutaneous lupus erythematosus. Clin Rheumatol 41, 437–452 (2022). https://doi.org/10.1007/s10067-021-05913-2

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