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Utilising bioinformatics and systems biology methods to uncover the impact of dermatomyositis on interstitial lung disease
R. Ding1, D. Liang2, S. Huang3, X. Huang4, B. Wei5, S. Wan6, H. Zhang7, Z. Wan8
- Department of Rheumatology and Immunology, Zhongshan Hospital Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China.
- Department of Rheumatology and Immunology, Zhongshan Hospital Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China.
- Department of Rheumatology and Immunology, Zhongshan Hospital Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China.
- Department of Rheumatology and Immunology, Zhongshan Hospital Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China.
- Department of Rheumatology and Immunology, Zhongshan Hospital Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China.
- Department of Oncology and Vascular Interventional Radiology, Zhongshan Hospital Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China.
- Department of Oncology and Vascular Interventional Radiology, Zhongshan Hospital Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. zhanghongjian11021@163.com
- Department of Oncology and Vascular Interventional Radiology, Zhongshan Hospital Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China. wanzheng0626@xmu.edu.cn
CER18318
2025 Vol.43, N°2
PI 0282, PF 0289
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PMID: 39808289 [PubMed]
Received: 09/11/2024
Accepted : 16/12/2024
In Press: 03/01/2025
Published: 26/02/2025
Abstract
OBJECTIVES:
Dermatomyositis (DM) is frequently associated with interstitial lung disease (ILD); however, the molecular mechanisms underlying this association remain unclear. This study aimed to employ bioinformatics approaches to identify potential molecular mechanisms linking DM and ILD.
METHODS:
GSE46239 and GSE47162 were analysed to identify common differentially expressed genes (DEGs). These DEGs underwent Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis. A protein-protein interaction (PPI) network was constructed to identify hub genes and transcriptional regulators. Potential therapeutic drugs were predicted using the Drug-Gene Interaction Database (DGIDB).
RESULTS:
A total of 122 common DEGs were identified between the DM and ILD datasets. These DEGs were significantly enriched in signal transduction, transcriptional regulation, inflammation, and cell proliferation. Key pathways included the NOD-like receptor signalling pathway, cytokine-cytokine receptor interaction, and TNF signalling pathway. PPI network analysis revealed the top 10 hub genes: CD163, GZMB, IRF4, CCR7, MMP9, AIF1, CXCL10, CCL5, IRF8, and NLRP3. Additionally, interactions between hub genes and transcription factors/miRNAs were constructed. Eleven drugs targeting four hub genes (CXCL10, MMP9, GZMB, and NLRP3) were predicted using the DGIDB.
CONCLUSIONS:
In summary, the study identified 10 key genes involved in the molecular pathogenesis of DM and ILD. Moreover, 11 potential drugs were identified that may offer viable therapeutic options for treating DM and ILD in the future.
