摘要：

目的 唾液腺是原发性干燥综合征（pSS）的主要靶器官，因此唾液被认为是腺体病理生理学和疾病状态的镜子。本研究旨在说明pSS患者的唾液蛋白质组学特征，并鉴定可能辅助诊断的潜在生物标志物。

方法 发现集包含49个样本[24个来自pSS，25个来自年龄和性别匹配的健康对照(HCs）]，验证集包括25个样本（12个来自pSS，13个来自HCs）。36 例 pSS 患者和 38 例健康对照者以 2：1 的比例集中随机分配至 Discovery 组或验证组。在2D LC-HRMS/MS平台上使用数据非依赖性采集（DIA）策略分析来自pSS患者和健康对照组的未刺激性全唾液样本，以揭示差异蛋白。根据基因本体（GO）分析和国际药学文摘（IPA）分析的蛋白质注释，使用DIA分析验证了关键蛋白质。随机森林用于建立SS的预测模型。

结果 共发现1,963个蛋白，其中136个蛋白在pSS患者中表现出差异性。生物信息学研究表明这些蛋白质主要与免疫功能、新陈代谢和炎症有关。一组19个蛋白质生物标志物通过基于P 值和随机森林的排序顺序进行鉴定，并验证为具有特殊曲线下面积 （AUC） 值（发现集：0.817；验证集：0.882）的潜在生物标志物，可用于鉴别pSS 患者和健康对照人群。

结论 新发现的候选蛋白组合可能有助于pSS的诊断。唾液蛋白质组学分析是一种很有前途的无创方法，可用于对pSS患者进行预后评估以及早期和精确治疗。DIA具备最佳的时间效率和数据可靠性，可望成为未来唾液蛋白质组研究的合适选择。

Abstract:

Objective As primary Sjögren’s syndrome (pSS) primarily affects the salivary glands, saliva can serve as an indicator of the glands’ pathophysiology and the disease’s status. This study aims to illustrate the salivary proteomic profiles of pSS patients and identify potential candidate biomarkers for diagnosis.

Methods The discovery set contained 49 samples (24 from pSS and 25 from age- and gender-matched healthy controls [HCs]) and the validation set included 25 samples (12 from pSS and 13 from HCs). Totally 36 pSS patients and 38 HCs were centrally randomized into the discovery set or to the validation set at a 2:1 ratio. Unstimulated whole saliva samples from pSS patients and HCs were analyzed using a data-independent acquisition (DIA) strategy on a 2D LC-HRMS/MS platform to reveal differential proteins. The crucial proteins were verified using DIA analysis and annotated using gene ontology (GO) and International Pharmaceutical Abstracts (IPA) analysis. A prediction model for SS was established using random forests.

Results A total of 1,963 proteins were discovered, and 136 proteins exhibited differential representation in pSS patients. The bioinformatic research indicated that these proteins were primarily linked to immunological functions, metabolism, and inflammation. A panel of 19 protein biomarkers was identified by ranking order based on P-value and random forest algorichm, and was validated as the predictive biomarkers exhibiting good performance with area under the curve (AUC) of 0.817 for discovery set and 0.882 for validation set.

Conclusions The candidate protein panel discovered may aid in pSS diagnosis. Salivary proteomic analysis is a promising non-invasive method for prognostic evaluation and early and precise treatments for pSS patients. DIA offers the best time efficiency and data dependability and may be a suitable option for future research on the salivary proteome.