The glycosylation landscape of prostate cancer tissues and biofluids

聚糖 糖组学 糖基化 糖组 岩藻糖基化 前列腺癌 流浪汉 生物 计算生物学 生物化学 糖蛋白 化学 癌症 遗传学
作者
Jordan P. Hartig,Lyndsay E.A. Young,Grace Grimsley,Anand S. Mehta,Joseph E. Ippolito,Robin J. Leach,Peggi M. Angel,Richard R. Drake
出处
期刊:Advances in Cancer Research [Elsevier BV]
卷期号:: 1-30 被引量:3
标识
DOI:10.1016/bs.acr.2024.04.005
摘要

An overview of the role of glycosylation in prostate cancer (PCa) development and progression is presented, focusing on recent advancements in defining the N-glycome through glycomic profiling and glycoproteomic methodologies. Glycosylation is a common post-translational modification typified by oligosaccharides attached N-linked to asparagine or O-linked to serine or threonine on carrier proteins. These attached sugars have crucial roles in protein folding and cellular recognition processes, such that altered glycosylation is a hallmark of cancer pathogenesis and progression. In the past decade, advancements in N-glycan profiling workflows using Matrix Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI-MSI) technology have been applied to define the spatial distribution of glycans in PCa tissues. Multiple studies applying N-glycan MALDI-MSI to pathology-defined PCa tissues have identified significant alterations in N-glycan profiles associated with PCa progression. N-glycan compositions progressively increase in number, and structural complexity due to increased fucosylation and sialylation. Additionally, significant progress has been made in defining the glycan and glycopeptide compositions of prostatic-derived glycoproteins like prostate-specific antigen in tissues and biofluids. The glycosyltransferases involved in these changes are potential drug targets for PCa, and new approaches in this area are summarized. These advancements will be discussed in the context of the further development of clinical diagnostics and therapeutics targeting glycans and glycoproteins associated with PCa progression. Integration of large scale spatial glycomic data for PCa with other spatial-omic methodologies is now feasible at the tissue and single-cell levels.

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