Liu Yingzhao, Zhu Zhangwei, Xu Qian, Xu Juan, Xing Jie, Wang Shengjun, Peng Huiyong
Department of Endocrinology, The Affiliated People's Hospital of Jiangsu University, Zhenjiang Medical School of Nanjing Medical University, Zhenjiang, 212002, China.
Department of Critical Care Medicine, The Affiliated People's Hospital of Jiangsu University, Zhenjiang Medical School of Nanjing Medical University, Zhenjiang, 212002, China.
BMC Immunol. 2025 Feb 28;26(1):11. doi: 10.1186/s12865-025-00691-x.
Hashimoto's thyroiditis (HT) is one of the most common autoimmune disorders characterized by diffuse enlargement of the thyroid gland, lymphocyte infiltration, and thyroid-specific autoantibodies. Cellular and humoral immune disorders have been implicated in the development of HT. However, little is known regarding the role of immune-related molecules in HT. This study was aimed to identify key immune-related biomarkers in HT by using bioinformatic analysis.
Integration of the sequencing data from HT and normal control (NC) in the GSA and GTEx databases yielded a dataset named NGS. The GSE138198 dataset from the GEO database was downloaded as a validation set. WGCNA analysis was performed to identify key modules associated with HT. Lasso regression analysis (LASSO) and random forest (RF) were performed to determine potential diagnostic biomarkers. The potential value was assessed by using receiver operating characteristic (ROC) curve analysis. CIBERSORT algorithm was used to evaluate the infiltration of immune cells in HT and NC samples. The transcript levels of verified genes from expanded samples were detected by quantitative real-time PCR.
A total of 1,401 differentially expressed genes (DEGs) were identified in HT patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses indicated that these DEGs were predominantly enriched in immune-related pathways. Furthermore, 192 immune-related genes were identified in HT through the intersection of WGCNA modules, DEGs, and the IRGs. Among them, two upregulated genes ((Bruton's tyrosine kinase, BTK) and CD19) showed the potential diagnostic value for HT by using machine learning. The ROC curve analysis revealed that BTK had a higher diagnostic value than CD19 across two datasets. Intriguingly, only BTK expression was upregulated in the peripheral blood mononuclear cells of HT patients, and was significantly positively correlated with the serum levels of thyroid autoantibodies. Further studies confirmed a significant positive correlation between BTK and increased proportions of plasma cells in HT patients.
This study identified BTK was significantly increased in HT patients, which might be the involved in the pathogenesis of HT by regulating plasma cells and represented a potential immune-related biomarker of HT.
桥本甲状腺炎（HT）是最常见的自身免疫性疾病之一，其特征为甲状腺弥漫性肿大、淋巴细胞浸润以及甲状腺特异性自身抗体。细胞免疫和体液免疫紊乱与HT的发病机制有关。然而，关于免疫相关分子在HT中的作用知之甚少。本研究旨在通过生物信息学分析确定HT中关键的免疫相关生物标志物。
整合GSA和GTEx数据库中HT和正常对照（NC）的测序数据，得到一个名为NGS的数据集。从GEO数据库下载GSE138198数据集作为验证集。进行加权基因共表达网络分析（WGCNA）以确定与HT相关的关键模块。进行套索回归分析（LASSO）和随机森林（RF）以确定潜在的诊断生物标志物。使用受试者工作特征（ROC）曲线分析评估其潜在价值。使用CIBERSORT算法评估HT和NC样本中免疫细胞的浸润情况。通过定量实时PCR检测扩增样本中验证基因的转录水平。
在HT患者中总共鉴定出1401个差异表达基因（DEG）。基因本体（GO）和京都基因与基因组百科全书（KEGG）通路分析表明，这些DEG主要富集在免疫相关通路中。此外，通过WGCNA模块、DEG和免疫相关基因（IRG）的交集，在HT中鉴定出192个免疫相关基因。其中，两个上调基因（布鲁顿酪氨酸激酶，BTK和CD19）通过机器学习显示出对HT的潜在诊断价值。ROC曲线分析显示，在两个数据集中BTK的诊断价值高于CD19。有趣的是，仅BTK表达在HT患者的外周血单个核细胞中上调，并且与甲状腺自身抗体的血清水平显著正相关。进一步研究证实，HT患者中BTK与浆细胞比例增加之间存在显著正相关。
本研究发现HT患者中BTK显著增加，其可能通过调节浆细胞参与HT的发病机制，并代表HT潜在的免疫相关生物标志物。
