ArticleFrontiers in bioinformatics2025
Cross-disease transcriptomic meta-analysis and network pharmacology reveal key therapeutic targets in rheumatoid arthritis, systemic lupus erythematosus and multiple sclerosis.
Article in Frontiers in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Autoimmune disease has a complex etiology that remains not fully understood. We aimed to identify highly perturbed DEGs and hub genes associated with autoimmune disease Rheumatoid Arthritis (RA), Systemic Lupus Erythematosus (SLE) and Multiple Sclerosis (MS). To find potentially lead to more effective therapies that target the root causes of these diseases. Materials and methods: Datasets for autoimmune diseases (RA, SLE, and MS) were collected from the GEO database. Differentially expressed genes were identified and subjected to meta-analysis to obtain common DEGs, which were then used for functional enrichment analysis GO and pathway analysis. A PPI network was constructed, and topology-based ranking identified hub genes. These hub genes were further analyzed through regulatory network analysis (TF and miRNA), gene-disease association studies, and drug-gene interaction analysis. Finally, molecular docking and molecular dynamics (MD) simulations were performed on the hub genes. Results: A total of 341 differentially expressed genes were identified, with 172 upregulated and 169 downregulated genes. Among these, eight hub genes Conclusion: These findings explored identifying the DEGs among shade of the autoimmune disease RA, SLE, MS, and this hub gene are associated with transcription factors are most crucial role play in the disease potentially clinical therapeutic targets of the autoimmune disease.
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