Laboratory for Functional Glycomics, College of Life Sciences, Northwest University, Xi'an, China.
Department of Endocrine, Shanghai Gongli Hospital of Pudong New Area, Shanghai, China; Department of Endocrine, The Second Affiliated Hospital of Xi'an Jiao Tong University, Xi'an, China.
Int J Biol Macromol. 2022 Aug 31;215:280-289. doi: 10.1016/j.ijbiomac.2022.05.194. Epub 2022 Jun 2.
The diagnosis of thyroid cancer, especially papillary thyroid cancer (PTC), is increasing rapidly worldwide. In this study, we aimed to study the glycosylation of salivary proteins associated with PTC and assess the likelihood that salivary glycopatterns may be a potential biomarker of PTC diagnosis. Firstly, 22 benign thyroid nodule (BTN) samples, 27 PTC samples, and 30 healthy volunteers (HV) samples were collected to probe the difference of salivary glycopatterns associated with PTC using lectin microarrays. Then, five machine learning models including K-Nearest Neighbor (KNN), Multilayer Perceptron (MLP), Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) were established to distinguish HV, BTN and PTC based on the changes of salivary glycopatterns. As a result, SVM had the best diagnostic effect with an accuracy rate of 92 % in testing set. Besides, lectin microarrays were used to explore the differences in salivary glycopatterns of 26 paired salivary samples of PTC patients before and after operation in order to probe into salivary glycopatterns as potential biomarkers for prognosis of PTC patients. The results showed that the levels of salivary glycopatterns recognized by 6 different lectins in patients after the operation almost convergenced with HVs. This study could help to screen and assess patients with PTC and their prognosis based on precise changes of salivary glycopatterns.
甲状腺癌的诊断，特别是甲状腺乳头状癌（PTC），在全球范围内迅速增加。在这项研究中，我们旨在研究与 PTC 相关的唾液蛋白的糖基化，并评估唾液糖谱是否可能成为 PTC 诊断的潜在生物标志物。首先，收集了 22 例良性甲状腺结节（BTN）样本、27 例 PTC 样本和 30 例健康志愿者（HV）样本，使用凝集素微阵列研究与 PTC 相关的唾液糖谱的差异。然后，建立了包括 K-最近邻（KNN）、多层感知机（MLP）、逻辑回归（LR）、随机森林（RF）和支持向量机（SVM）在内的 5 种机器学习模型，基于唾液糖谱的变化来区分 HV、BTN 和 PTC。结果表明，SVM 在测试集中的准确率为 92%，具有最佳的诊断效果。此外，还使用凝集素微阵列来探索 26 对 PTC 患者手术前后唾液样本中唾液糖谱的差异，以探究唾液糖谱是否可作为 PTC 患者预后的潜在生物标志物。结果表明，术后患者唾液中被 6 种不同凝集素识别的糖谱水平几乎与 HV 一致。这项研究有助于基于唾液糖谱的精确变化筛选和评估 PTC 患者及其预后。
