Laboratory for Functional Glycomics, College of Life Sciences, Faculty of Life Science & Medicine, Northwest University, Xi'an, China; School of Medicine, Faculty of Life Science & Medicine, Northwest University, Xi'an, China.
Laboratory for Functional Glycomics, College of Life Sciences, Faculty of Life Science & Medicine, Northwest University, Xi'an, China.
Int J Biol Macromol. 2023 Dec 1;252:126354. doi: 10.1016/j.ijbiomac.2023.126354. Epub 2023 Aug 15.
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.
唾液具有采集方便、无痛、非侵入性的优势，有望成为癌症检测、病理评估和治疗监测的有价值的生物标志物来源。近年来，唾液糖组图谱在癌症筛查方面显示出了巨大的潜力。然而，在癌症诊断中，对非癌性部位良性病变的认识却被忽视了。阐明良性病变对唾液糖组图谱和癌症筛查的影响，对于推进基于唾液糖组图谱的诊断学的发展至关重要。在这项研究中，使用凝集素微阵列分析了 2885 个样本，根据病变的数量、位置和类型来识别唾液糖组图谱的变化。利用我们之前发表的肿瘤相关唾液糖组图谱数据，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效果。结果表明，病变的位置和数量对唾液糖组图谱都有明显的影响。此外，将广泛的良性疾病纳入对照组可以提高分类器区分癌症病例和对照组的性能。这一发现对于增强基于唾液糖组图谱的癌症筛查具有指导意义，并有助于其在临床环境中的实际应用。
