具有方便、无痛和非侵入性收集的优势，唾液被寄予厚望，作为癌症检测、病理评估和治疗监测的宝贵生物标志物来源。近年来，唾液糖蛋白图谱显示出在癌症筛查方面的显著潜力。然而，在癌症诊断中对非癌症部位的良性病变的理解一直被忽视。阐明良性病变对唾液糖蛋白图谱和癌症筛查的影响对推动唾液糖蛋白图谱诊断的发展至关重要。本研究使用凝集素微阵列分析了2885个样本，以确定唾液糖蛋白图谱在不同病变数量、位置和类型下的变化。通过利用我们先前发表的与肿瘤相关的唾液糖蛋白图谱数据，研究了机器学习算法在癌症筛查中的性能，以评估将良性疾病病例添加到对照组中的影响。结果表明，病变的位置和数量对唾液糖蛋白图谱有明显影响。同时，揭示了将广泛的良性疾病纳入对照组可以提高分类器在区分癌症病例和对照组方面的性能。这一发现对于提升基于唾液糖蛋白图谱的癌症筛查具有指导意义，并促进它们在临床设置中的实际应用。版权所有 © 2023. Elsevier B.V.出版。

With the advantages of convenient, painless and non-invasive collection, saliva holds great promise as a valuable biomarker source for cancer detection, pathological assessment and therapeutic monitoring. Salivary glycopatterns have shown significant potential for cancer screening in recent years. However, the understanding of benign lesions at non-cancerous sites in cancer diagnosis has been overlooked. Clarifying the influence of benign lesions on salivary glycopatterns and cancer screening is crucial for advancing the development of salivary glycopattern-based diagnostics. In this study, 2885 samples were analyzed using lectin microarrays to identify variations in salivary glycopatterns according to the number, location, and type of lesions. By utilizing our previously published data of tumor-associated salivary glycopatterns, the performance of machine learning algorithm for cancer screening was investigated to evaluate the effect of adding benign disease cases to the control group. The results demonstrated that both the location and number of lesions had discernible effects on salivary glycopatterns. And it was also revealed that incorporating a broad range of benign diseases into the controls improved the classifier's performance in distinguishing cancer cases from controls. This finding holds guiding significance for enhancing salivary glycopattern-based cancer screening and facilitates their practical implementation in clinical settings.Copyright © 2023. Published by Elsevier B.V.