Transcriptomics explores potential mechanisms for the development of Primary Sjogren's syndrome to diffuse large B-cell lymphoma in B cells.

IF 2.9 4区 医学 Q3 IMMUNOLOGY BMC Immunology Pub Date : 2024-07-30 DOI:10.1186/s12865-024-00646-8
Yanan Xu, Jianxing Han, Ziyi Fan, Shufen Liang
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Abstract

Purpose: Primary 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 methods: Gene 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.

Results: By 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.

Conclusion: We 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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转录组学探索了 B 细胞从原发性 Sjogren's 综合征发展为弥漫性大 B 细胞淋巴瘤的潜在机制。
目的:原发性斯约格伦综合征(pSS)是一种常见的自身免疫性疾病。在临床实践中,它所导致的免疫失调往往会导致弥漫大 B 细胞淋巴瘤(DLBCL)的发生。然而,它如何在分子水平上导致这两种疾病尚未可知。本研究探讨了与 DLBCL 和 pSS 之间差异相关的潜在分子机制:从 GEO 和 TCGA 数据库下载发现队列 1、发现队列 2 和验证队列的基因表达矩阵。进行加权基因共表达网络分析(WGCNA),以确定发现队列1中DLBCL和pSS的共表达模块,并获得共享基因。对共有基因进行了 GO 和 KEGG 富集分析以及 PPI 网络分析。将免疫相关基因(IRGs)与共有基因交叉,以获得共有基因。之后,通过机器学习方法确定共同基因。进行了免疫浸润分析、miRNA-TF-枢纽基因调控图、枢纽基因的基因相互作用和基因-药物靶点分析。最后,通过上述分析将 STAT1 确定为可能的重要基因,并对发现队列 2 中的高表达组和低表达组进行了免疫浸润和 GSEA 通路分析。在验证队列中评估了中枢基因的诊断效果,并收集了临床样本进行验证:通过 WGCNA,每组中有一个模块基因被认为与疾病高度相关,我们获得了 28 个共享基因。富集分析揭示了参与病毒反应和调控的共有基因。通过该算法,我们获得了四个枢纽基因(ISG20、STAT1、TLR7 和 RSAD2)。枢纽基因和类似基因主要参与调控 IFNs。构建的 miRNA-TF 中枢基因调控图显示,hsa-mir-155-5p、hsa-mir-146b-5p、hsa-mir-21-3p 和 hsa-mir-126-3p 在这两种疾病中发挥着重要作用。根据免疫浸润分析,枢纽基因在 B 细胞记忆中的表达存在差异。枢纽基因对这两种疾病都有很强的诊断作用。STAT1在两种疾病的免疫细胞中都发挥着重要作用:我们发现了DLBCL和pSS的枢纽易感基因,并确定了可作为生物标志物的枢纽基因和潜在治疗靶点。
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来源期刊
BMC Immunology
BMC Immunology 医学-免疫学
CiteScore
5.50
自引率
0.00%
发文量
54
审稿时长
1 months
期刊介绍: BMC Immunology is an open access journal publishing original peer-reviewed research articles in molecular, cellular, tissue-level, organismal, functional, and developmental aspects of the immune system as well as clinical studies and animal models of human diseases.
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