Dipartimento di Scienze della Vita e dell'Ambiente, Università di Cagliari, 09042 Cagliari, Italy.
Fondazione Policlinico Universitario "A. Gemelli"-IRCCS, 00168 Rome, Italy.
Int J Mol Sci. 2023 Jan 4;24(2):959. doi: 10.3390/ijms24020959.
(1) Autoimmune hepatitis (AIH) and primary biliary cholangitis (PBC) are autoimmune liver diseases characterized by chronic hepatic inflammation and progressive liver fibrosis. The possible use of saliva as a diagnostic tool has been explored in several oral and systemic diseases. The use of proteomics for personalized medicine is a rapidly emerging field. (2) Salivary proteomic data of 36 healthy controls (HCs), 36 AIH and 36 PBC patients, obtained by liquid chromatography/mass spectrometry top-down pipeline, were analyzed by multiple Mann-Whitney test, Kendall correlation, Random Forest (RF) analysis and Linear Discriminant Analysis (LDA); (3) Mann-Whitney tests provided indications on the panel of differentially expressed salivary proteins and peptides, namely cystatin A, statherin, histatin 3, histatin 5 and histatin 6, which were elevated in AIH patients with respect to both HCs and PBC patients, while S100A12, S100A9 short, cystatin S1, S2, SN and C showed varied levels in PBC with respect to HCs and/or AIH patients. RF analysis evidenced a panel of salivary proteins/peptides able to classify with good accuracy PBC vs. HCs (83.3%), AIH vs. HCs (79.9%) and PBC vs. AIH (80.2%); (4) RF appears to be an attractive machine-learning tool suited for classification of AIH and PBC based on their different salivary proteomic profiles.
(1) 自身免疫性肝炎（AIH）和原发性胆汁性胆管炎（PBC）是两种自身免疫性肝病，其特征为慢性肝炎症和进行性肝纤维化。在几种口腔和系统性疾病中已经探讨了将唾液用作诊断工具的可能性。蛋白质组学在个性化医疗中的应用是一个迅速发展的领域。(2) 通过液相色谱/质谱自上而下的方法获得了 36 名健康对照者（HCs）、36 名 AIH 患者和 36 名 PBC 患者的唾液蛋白质组学数据，采用多重 Mann-Whitney 检验、Kendall 相关性、随机森林（RF）分析和线性判别分析（LDA）对其进行分析；(3) Mann-Whitney 检验提供了唾液中差异表达的蛋白质和肽的面板的指示，即半胱氨酸蛋白酶抑制剂 A、唾液素、组蛋白 3、组蛋白 5 和组蛋白 6，它们在 AIH 患者中均高于 HCs 和 PBC 患者，而 S100A12、S100A9 短、半胱氨酸蛋白酶抑制剂 S1、S2、SN 和 C 在 PBC 患者中相对于 HCs 和/或 AIH 患者表现出不同的水平。RF 分析证明了一组唾液蛋白质/肽能够以较高的准确度对 PBC 与 HCs（83.3%）、AIH 与 HCs（79.9%）和 PBC 与 AIH（80.2%）进行分类；(4) RF 似乎是一种有吸引力的机器学习工具，适用于基于 AIH 和 PBC 不同的唾液蛋白质组学特征对其进行分类。
