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.

Characterization of glucose-binding proteins isolated from health volunteers and human type 2 diabetes mellitus patients.

Glucose is one of the most important monosaccharides. Although hyperglycemia in type 2 diabetes mellitus (T2DM) lead to a series of changes; however, little is known about the alterations of serum proteins in T2DM, especially those proteins with glucose affinity. In this study, the glucose-binding proteins (GlcBPs) of serum were isolated from 30 health volunteer (HV) and 30 T2DM patients by glucose-magnetic particle conjugates (GMPC) and identified by mass spectrum analysis. Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) indicated the main gene annotations and pathways of this GlcBPs, while Motif-X webtool provided the potential glucose-binding domains. Further docking analysis and glycan microarray were used to understand the interaction between the glucose and glucose-binding domains. A total of 149 and 119 GlcBPs were identified from HV and T2DM cases. Four hundred and sixty-eight GO annotations in 165 identified GlcBPs were available, while the majority involved in cellular processes and binding function. A short peptide, EGDEEITCLNGFWLE, which was derived from the Motif-X analysis, presented a high-binding ability to the glucose from both docking analysis and glycan analysis. GMPC provides a powerful tool for GlcBPs isolation and indicates the alteration of GlcBPs in T2DM.