Department of Ultrasonography, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
Department of Ultrasonography, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
Clin Immunol. 2023 Oct;255:109739. doi: 10.1016/j.clim.2023.109739. Epub 2023 Aug 14.
Diagnosing primary Sjögren's syndrome (pSS) is difficult due to clinical heterogeneity and the absence of non-invasive specific biomarkers. To develop non-invasive pSS diagnosis methods that integrate classic clinical indexes, major salivary gland ultrasonography (SGUS), and gene expression profiles shared by labial gland and peripheral blood, we conducted a study on a cohort of 358 subjects. We identified differentially expressed genes (DEGs) in glands and blood that were enriched in defense response to virus and type I interferon production pathways. Four upregulated DEGs common in glands and blood were identified as hub genes based on the protein-protein interaction networks. A random forest model was trained using features, including SGUS, anti-SSA/Ro60, keratoconjunctivitis sicca tests, and gene expression levels of MX1 and RSAD2. The model achieved comparable pSS diagnosis accuracy to the golden standard method based on labial gland biopsy. Our findings implicate this novel model as a promising diagnosis technique of pSS.
原发性干燥综合征（pSS）的诊断较为困难，因为其临床表现存在异质性，且缺乏非侵入性的特异性生物标志物。为了开发能够整合经典临床指标、大唾液腺超声（SGUS）以及唇腺和外周血共享基因表达谱的非侵入性 pSS 诊断方法，我们对 358 名受试者进行了一项研究。我们在腺体和血液中鉴定出差异表达的基因（DEGs），这些基因在防御病毒和产生 I 型干扰素的反应途径中富集。基于蛋白质-蛋白质相互作用网络，我们确定了 4 个在腺体和血液中上调的共同 DEGs 作为枢纽基因。使用包括 SGUS、抗 SSA/Ro60、干燥性角膜结膜炎测试以及 MX1 和 RSAD2 的基因表达水平等特征，我们训练了一个随机森林模型。该模型在 pSS 诊断中的准确性可与基于唇腺活检的金标准方法相媲美。我们的研究结果表明，这种新模型是一种很有前途的 pSS 诊断技术。
