ArticleBMC immunology2024
Transcriptomics explores potential mechanisms for the development of Primary Sjogren's syndrome to diffuse large B-cell lymphoma in B cells.
Article in BMC immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Prevalence of solid tumors and lymphoma in the Brazilian Sjögren's Disease Registry (BRAS): an important concern for clinical practice.Advances in rheumatology (London, England) · 2025Article
- Identification and differential expression analysis of microRNAs in the liver and spleen tissues of Yunnan Zebu and Holstein cattle.Tropical animal health and production · 2025Article
- Transcriptomic insights into the mechanism of action of telomere-related biomarkers in rheumatoid arthritis.Frontiers in immunology · 2025Article
- Autoimmune disease-associated lymphomas: research progress and review.Frontiers in immunology · 2025Review
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4 authors.
Funding
Abstract
purposePrimary Sjogren's syndrome (pSS) is a prevalent autoimmune disease. The immune dysregulation it causes often leads to the development of diffuse large B-cell lymphoma (DLBCL) in clinical practice. However, how it contributes to these two disorders at the molecular level is not yet known. This study explored the potential molecular mechanisms associated with the differences between DLBCL and pSS. PATIENTS AND
methodsGene expression matrices from discovery cohort 1, discovery cohort 2, and the validation cohort were downloaded from the GEO and TCGA databases. Weighted gene coexpression network analysis (WGCNA) was performed to identify the coexpression modules of DLBCL and pSS in discovery cohort 1 and obtain shared genes. GO and KEGG enrichment analyses and PPI network analysis were performed on the shared genes. Immune-related genes (IRGs) were intersected with shared genes to obtain common genes. Afterward, common genes were identified via machine learning methods. The immune infiltration analysis, miRNA-TF-hub gene regulatory chart, gene interactions of the hub genes, and gene‒drug target analysis were performed. Finally, STAT1 was identified as a possible essential gene by the above analysis, and immune infiltration and GSEA pathway analyses were performed in the high- and low-expression groups in discovery cohort 2. The diagnostic efficacy of the hub genes was assessed in the validation cohort, and clinical samples were collected for validation.
resultsBy WGCNA, one modular gene in each group was considered highly associated with the disease, and we obtained 28 shared genes. Enrichment analysis revealed shared genes involved in the viral response and regulation. We obtained four hub genes (ISG20, STAT1, TLR7, and RSAD2) via the algorithm. Hub genes and similar genes are primarily involved in regulating type I IFNs. The construction of a miRNA-TF-hub gene regulatory chart revealed that hsa-mir-155-5p, hsa-mir-146b-5p, hsa-mir-21-3p, and hsa-mir-126-3p play essential roles in both diseases. Hub genes were differentially expressed in B-cell memory according to immune infiltration analysis. Hub genes had a strong diagnostic effect on both diseases. STAT1 plays an essential role in immune cells in both diseases.
conclusionWe identified hub susceptibility genes for DLBCL and pSS and identified hub genes and potential therapeutic targets that may act as biomarkers.
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