Zhuang Duan-Rong, Hu Xin, Huang Hui-Bin
Endocrinology Department of the Second Affiliated Hospital of Fujian, Medical University, 1602,Tower 4, One Pacific Place, Donghai Street, Fengze District, Quanzhou City, Fujian Province, 362000, China.
Hormones (Athens). 2025 May 19. doi: 10.1007/s42000-025-00668-w.
This study aims to identify hub genes associated with the onset and progression of Graves' disease (GD) with the goal of developing novel biomarkers to enhance diagnosis and improve patient outcomes.
mRNA profiles from thyroid tissue samples (24 GD vs. 24 normal controls) were obtained from GEO (GSE9340), ArrayExpress (E-MEXP-2612), and GTEx (Thyroid dataset). After batch correction via SVA algorithm, 366 differentially expressed genes (DEGs) were identified using limma. Functional enrichment, protein-protein interaction networks, and immune microenvironment analysis were performed. Hub genes were validated in clinical thyroid specimens (3 GD vs. 3 controls) using RT-qPCR.
A total of 366 DEGs were identified in the diseased and normal groups. Among these, eight hub genes (TYROBP, CSF1R, CD163, ITGAM, CD86, FCGR3B, ITGB2, and IL10RA) showed strong correlations with immune cell content. These genes were predominantly enriched in pathways related to amino acid metabolism, viral protein interactions with cytokines and cytokine receptors, phagosome, chemokine signaling, programmed cell death, NF-κB, and other pathways. Additionally, these hub genes were linked to 39 regulatory factors. mRNA levels of these hub genes were validated in clinical samples through RT-qPCR. It is noteworthy that eight genes were found to be upregulated in GD samples.
The study highlights the potential impact of ITGB 2, TYROBP, CSF1R, CD163, ITGAM, CD86, FCGR3B, and IL10RA on the development and progression of GD, supporting their role as potential biomarkers.
本研究旨在识别与格雷夫斯病（GD）发病及进展相关的枢纽基因，以期开发新的生物标志物来加强诊断并改善患者预后。
从基因表达综合数据库（GEO，GSE9340）、ArrayExpress数据库（E-MEXP-2612）和基因型组织表达数据库（GTEx，甲状腺数据集）获取甲状腺组织样本的mRNA谱（24例GD患者与24例正常对照）。通过SVA算法进行批次校正后，使用limma软件识别出366个差异表达基因（DEG）。进行了功能富集、蛋白质-蛋白质相互作用网络和免疫微环境分析。使用逆转录定量聚合酶链反应（RT-qPCR）在临床甲状腺标本（3例GD患者与3例对照）中验证枢纽基因。
在患病组和正常组中总共识别出366个DEG。其中，八个枢纽基因（酪氨酸结合蛋白（TYROBP）、集落刺激因子1受体（CSF1R）、CD163分子、整合素αM（ITGAM）、CD86分子、Fc段γ受体Ⅲb（FCGR3B）、整合素β2（ITGB2）和白细胞介素10受体α（IL10RA））与免疫细胞含量显示出强相关性。这些基因主要富集于与氨基酸代谢、病毒蛋白与细胞因子及细胞因子受体的相互作用、吞噬体、趋化因子信号传导、程序性细胞死亡、核因子κB（NF-κB）等相关的通路。此外，这些枢纽基因与39个调节因子相关联。通过RT-qPCR在临床样本中验证了这些枢纽基因的mRNA水平。值得注意的是，发现八个基因在GD样本中上调。
该研究突出了整合素β2（ITGB2）、酪氨酸结合蛋白（TYROBP）、集落刺激因子1受体（CSF1R）、CD163分子、整合素αM（ITGAM）、CD86分子、Fc段γ受体Ⅲb（FCGR3B）和白细胞介素10受体α（IL10RA）对GD发生和进展的潜在影响，支持它们作为潜在生物标志物的作用。
