Beneficial or detrimental: Recruiting more types of benign cases for cancer diagnosis based on salivary glycopatterns.

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.

A novel strategy for quantification of α2,3- and α2,6-linked sialic acids in sialylated glycoproteins.

Sialic acid, a monosaccharide containing nine carbon atoms, is widely distributed in eukaryotic cells. The bound sialic acids are mainly present at the glycan ends of glycoconjugates via α2-3 or α2-6 glycosidic bonds, and alterations in their expression levels and linkage types are associated with the progress of many diseases and tumors. The present study provides a new strategy for quantification of α2,3- and α2,6-linked sialic acids in sialylated glycoproteins. In fact, quantification of α2,3-linked sialic acids were based on the difference of the bound sialic acids in the sample before and after treatment with α2-3 neuraminidase, whereas the α2,6-linked sialic acids were equal to the bound sialic acids in the α2-3 neuraminidase-treated sample. Subsequently, α2,3/6-linked sialic acids in salivary glycoproteins from healthy volunteers and diabetic patients were quantified in accordance with this method. This work provides an accurate method for the quantification of α2,3- and α2,6-linked sialic acids in the sialoglycoproteins, which is more instructive for understanding the biological roles of α2,3/6-linked sialic acid in sialoglycoproteins.

Integrated glycomics strategy for galactosylated-N-glycans recognized by Bandeiraea Simplicifolia Lectin I in salivary proteins associated with lung cancer.

PURPOSE: Lung cancer (LC) is the leading cause of cancer-related deaths worldwide, mainly due to late diagnosis and poor prognosis. Saliva is an important source for discovering biomarkers and contains an abundance of biological information. The purpose of this study was to determine whether galactosylation levels of salivary proteins are associated with LC. EXPERIMENTAL DESIGN: First, we analyzed the alterations of the glycopatterns recognized by Bandeiraea Simplicifolia Lectin I (BS-I) in five groups (healthy volunteers [HV]: 28, benign pulmonary disease [BPD]: 27, lung adenocarcinoma [ADC]: 39, squamous cell carcinoma [SCC]: 28, small-cell lung cancer [SCLC]: 22) of 144 saliva samples using lectin microarrays. Pooled samples from each group were subsequently validated by the lectin blotting technique. Finally, the N-glycan profiles of their salivary glycoproteins isolated by the BS-I-magnetic particle conjugates from pooled samples for each group were analyzed by MALDI-TOF/TOF-MS. RESULTS: The results showed that the expression level of galactosylated glycans recognized by BS-I was significantly increased in patients with LC compared with BPD and HV. Receiver operating characteristic (ROC) analysis indicated that the levels of salivary glycopattern recognized by BS-I could discriminate lung disease (BPD, ADC, SCC, and SCLC) and HV with an AUC of 0.700 (95% CI: 0.589-0.812), and discriminate LC and BPD with an AUC of 0.860 (95% CI: 0.763-0.956). Also, the proportion of galactosylated N-glycans in ADC (38.4%), SCC (43.1%), and SCLC (39.5%) increased compared to HV (30.1%) and BPD (33.7%), and two galactosylated N-glycan peaks (m/z 1828.683, 2418.853) could be identified only in the LC groups (ADC, SCC, and SCLC). CONCLUSIONS AND CLINICAL RELEVANCE: These findings could provide crucial information on galactosylated N-linked glycans associated with LC and facilitate the study of LC biomarkers based on precise alterations of galactosylated N-glycans in saliva.

Machine learning reveals salivary glycopatterns as potential biomarkers for the diagnosis and prognosis of papillary thyroid cancer.

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.

Role of sialylated glycans on bovine lactoferrin against influenza virus.

Influenza is a worldwide plague caused by the influenza virus (IAV) infection, which is initiated by specific recognition with sialic acids on host cell surface. Bovine lactoferrin (bLf) is a sialoglycoprotein belonging to the transferrin family, and it plays an important role in immune regulation. It also shows toxicity against cancer cells and pathogenic microorganisms including bacteria, fungi, and virus. The purpose of this study is to assess the roles of the sialylated glycans on bLf against IAV. To this end, bLf were first treated with sodium periodate to destroy its sialylated glycans. Then, the binding activity of native or desialylated bLf with various IAV was assessed by blotting assay. Finally, their ability to inhibit IAV attachment to host cells was analyzed in vitro. Our result showed that the sialylated glycans on bLf were almost completely destroyed by sodium periodate treatment. Furthermore, the binding activity of desialylated bLf to IAV and the ability to inhibit IAV mimics binding to MDCK cells were significantly reduced compared to that of native bLf. These results demonstrated that the sialylated glycans on bLf could serve as competitive substrates to block IAV attachment to host cells during the early stages of viral infection. Our findings make an important contribute for the fully understanding of the mechanism of bLf in the prevention of IAV infections and their possible applications in antiviral infection.

[Research on gingival healing situation after stage II surgery of dental implantation for periodontitis patients].

OBJECTIVE: This study aimed to investigate the duration of gingival healing after the stage II surgery of dental implantation for periodontitis patients and to provide clinical guidelines for implant restoration. METHODS: Twenty-nine periodontitis patients who had implantation surgery and achieved osseointegration were operated with stage II surgery (a total of 60 pieces of implants). The height of buccal gingival of each implant was measured twice after the stage II surgery. All implants were measured at the lowest point ofbuccal gingival after one week. The implants were randomly divided into four groups according to the schedule of the next test time: group one at one week from the initial test point, group two at two weeks, group three at three weeks, and group four at four weeks. Each group includes 15 pieces of implants. The amount of the buccal gingival change in each group between the second and first tests was determined, and the data were analyzed statistically. RESULTS: The amount of gingival change of groups one, two, three, and four was (-0.25 +/- 0.66), (-0.04 +/- 0.52), (-0.70 +/- 0.77), and (-0.74 +/- 1.09) mm, respectively. No significant difference was observed between groups one and two in terms of the amount of gingival changes (P > 0.05). However, a significant difference was found between groups two and three (P < 0.05), and the amount of gingival recession was 0.66 mm. No significant difference was found between groups three and four (P > 0.05), and the gingival achieved stability. CONCLUSION: The gingival recession achieves stability at the fourth week (after 28 d) after stage II surgery. At this time, the implant can be restored, and the abutment can be selected according to the amount of gingival change of the periodontitis patient.