Sulfation of O-glycans on Mucin-type Proteins From Serous Ovarian Epithelial Tumors

Glycosaminoglycans, Instructive Biomolecules That Regulate Cellular Activity and Synaptic Neuronal Control of Specific Tissue Functional Properties

Glycosaminoglycans (GAGs) are a diverse family of ancient biomolecules that evolved over millennia as key components in the glycocalyx that surrounds all cells. GAGs have molecular recognition and cell instructive properties when attached to cell surface and extracellular matrix (ECM) proteoglycans (PGs), which act as effector molecules that regulate cellular behavior. The perception of mechanical cues which arise from perturbations in the ECM microenvironment allow the cell to undertake appropriate biosynthetic responses to maintain ECM composition and tissue function. ECM PGs substituted with GAGs provide structural support to weight-bearing tissues and an ability to withstand shear forces in some tissue contexts. This review outlines the structural complexity of GAGs and the diverse functional properties they convey to cellular and ECM PGs. PGs have important roles in cartilaginous weight-bearing tissues and fibrocartilages subject to tension and high shear forces and also have important roles in vascular and neural tissues. Specific PGs have roles in synaptic stabilization and convey specificity and plasticity in the regulation of neurophysiological responses in the CNS/PNS that control tissue function. A better understanding of GAG instructional roles over cellular behavior may be insightful for the development of GAG-based biotherapeutics designed to treat tissue dysfunction in disease processes and in novel tissue repair strategies following trauma. GAGs have a significant level of sophistication over the control of cellular behavior in many tissue contexts, which needs to be fully deciphered in order to achieve a useful therapeutic product. GAG biotherapeutics offers exciting opportunities in the modern glycomics arena.

Minor acidic glycans: Review of focused glycomics methods

Glycans are modified by acidic molecules, including sialic acid, sulfates, phosphates, and glucuronic acid, forming acidic glycans. Among these, sialylated glycans form major components of deuterostome glycomes, and their structure and function have been widely studied. The other acidic glycans, comprising minor components of the glycome, are often overlooked by glycomics and glycoproteomics methods, although they are implicated in conditions such as inflammatory diseases, neurological diseases, and viral infections. Therefore, minor acidic glycans are high-priority targets for glycomics, and analytical techniques focused on minor acidic glycans are being developed. This review examines the methods of enriching the minor acidic glycans from biological glycomes and examines their structure, providing examples of the application of these techniques in biological and clinical samples.

Immune-related glycosylation genes based classification predicts prognosis and therapy options of osteosarcoma

Osteosarcoma is the most common primary bone malignancy, with a very poor prognosis. Aberrant glycosylation is close involvement in osteosarcoma. Accordingly, this study aimed at investigating the role of glycosylation genes in the prognosis and therapy options of osteosarcoma. The microenvironment of osteosarcoma was assessed using estimate algorithm. A total of 20 immune-related glycosylation genes (IRGGs) was identified using Pearson correlation analysis. Accordingly, osteosarcoma patients were divided into C1 and C2 type using consensus clustering. Multiple algorithms (Xcell, MCP-counter, ssGSEA, epic, quantiseq), cancer immune cycle analysis, and GSVA were applied to estimate the immune, molecule and metabolism characteristics of osteosarcoma, indicating that C1 type was featured with high immune infiltration, high glycosylation, enriched MEK signaling, and good prognosis, while C2 type was characterized by more metastasis, enriched immunotherapy-positive gene signatures, high tumor mutation burden, and poor prognosis. Results from TIDE algorithm and immunotherapy datasets suggested the C2 type's preference of immune checkpoint inhibitors (ICIs), while data of GDSC, CMap analysis and cell experiments indicated that C1 type was sensitivity to MEK inhibitor PD0325901. In addition, univariate Cox and Lasso analysis was combined to establish an IRGGs' risk score containing 6 genes (B3GNT8, FUT7, GAL3ST4, GALNT14, HS3ST2, and MFNG). The data of DCA and ROC indicated its well prediction of prognosis in osteosarcoma. Finally, cellular location analysis showed that the 6 genes not only distributed in tumor cells but also in immune cells. In summary, the classification and risk score based on IRGGs effectively predicted the prognosis and therapy options of osteosarcoma. Further studies on IRGGs may contribute to the understanding of cancer immunity in osteosarcoma.

Advancements in protein glycosylation biomarkers for ovarian cancer through mass spectrometry-based approaches

INTRODUCTION

Ovarian cancer, characterized by metastasis and reduced 5-year survival rates, stands as a substantial factor in the mortality of gynecological malignancies worldwide. The challenge of delayed diagnosis originates from vague early symptoms and the absence of efficient screening and diagnostic biomarkers for early cancer detection. Recent studies have explored the intricate interplay between ovarian cancer and protein glycosylation, unveiling the potential significance of glycosylation-oriented biomarkers.

AREAS COVERED

This review examines the progress in glycosylation biomarker research, with particular emphasis on advances driven by mass spectrometry-based technologies. We document milestones achieved, discuss encountered limitations, and also highlight potential areas for future research and development of protein glycosylation biomarkers for ovarian cancer.

EXPERT OPINION

The association of glycosylation in ovarian cancer is well known, but current research lacks desired sensitivity and specificity for early detection. Notably, investigations into protein-specific and site-specific glycoproteomics have the potential to significantly enhance our understanding of ovarian cancer and facilitate the identification of glycosylation-based biomarkers. Furthermore, the integration of advanced mass spectrometry techniques with AI-driven analysis and glycome databases holds the promise for revolutionizing biomarker discovery for ovarian cancer, ultimately transforming diagnosis and improving patient outcomes.