癌症中的糖基化是生物标记物的来源。

IF 3.8 3区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Expert Review of Proteomics Pub Date : 2024-09-01 Epub Date: 2024-10-24 DOI:10.1080/14789450.2024.2409224
Sara Khorami-Sarvestani, Samir M Hanash, Johannes F Fahrmann, Ricardo A León-Letelier, Hiroyuki Katayama
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引用次数: 0

摘要

引言糖基化是指糖合成并附着在目标分子上的过程,是哺乳动物细胞中一种重要而常见的翻译后修饰(PTM)。它影响蛋白质的亲水性、电荷、溶解性、结构、定位、功能和免受蛋白水解的能力。蛋白质中异常的糖基化可揭示新的检测和治疗糖基生物标记,有助于提高早期诊断的准确性和个性化治疗。本综述强调了聚糖和糖蛋白作为人类疾病(尤其是癌症)生物标志物来源的关键作用:本综述深入探讨了糖基化的意义,揭示了糖基化在影响生物标志物的癌症相关细胞过程中的复杂作用。本综述对截至 2024 年 6 月在 PubMed、Scopus 和 Google Scholar 上发表的文献进行了深入研究,主要关注以下术语:(Glycosylation[Title/Abstract]) OR (Glycan[Title/Abstract]) OR (glycoproteomics[Title/Abstract]) OR (Proteoglycans[Title/Abstract]) OR (Glycomarkers[Title/Abstract]) AND (Cancer[Title/Abstract]) AND ((Diagno*[Title/Abstract]) OR (Progno*[Title/Abstract])).专家意见:糖生物标志物可提高癌症的早期发现率,实现早期干预并改善患者预后。然而,丰富而复杂的动态聚糖结构可能会给其科学和临床应用带来困难。对癌症生物标志物中糖基化特征的探索可以提供癌症病因学的详细视图,并为糖基化在癌症研究中的革命性潜力带来希望。
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Glycosylation in cancer as a source of biomarkers.

Introduction: Glycosylation, the process of glycan synthesis and attachment to target molecules, is a crucial and common post-translational modification (PTM) in mammalian cells. It affects the protein's hydrophilicity, charge, solubility, structure, localization, function, and protection from proteolysis. Aberrant glycosylation in proteins can reveal new detection and therapeutic Glyco-biomarkers, which help to improve accurate early diagnosis and personalized treatment. This review underscores the pivotal role of glycans and glycoproteins as a source of biomarkers in human diseases, particularly cancer.

Areas covered: This review delves into the implications of glycosylation, shedding light on its intricate roles in cancer-related cellular processes influencing biomarkers. It is underpinned by a thorough examination of literature up to June 2024 in PubMed, Scopus, and Google Scholar; concentrating on the terms: (Glycosylation[Title/Abstract]) OR (Glycan[Title/Abstract]) OR (glycoproteomics[Title/Abstract]) OR (Proteoglycans[Title/Abstract]) OR (Glycomarkers[Title/Abstract]) AND (Cancer[Title/Abstract]) AND ((Diagno*[Title/Abstract]) OR (Progno*[Title/Abstract])).

Expert opinion: Glyco-biomarkers enhance early cancer detection, allow early intervention, and improve patient prognoses. However, the abundance and complex dynamic glycan structure may make their scientific and clinical application difficult. This exploration of glycosylation signatures in cancer biomarkers can provide a detailed view of cancer etiology and instill hope in the potential of glycosylation to revolutionize cancer research.

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来源期刊
Expert Review of Proteomics
Expert Review of Proteomics 生物-生化研究方法
CiteScore
7.60
自引率
0.00%
发文量
20
审稿时长
6-12 weeks
期刊介绍: Expert Review of Proteomics (ISSN 1478-9450) seeks to collect together technologies, methods and discoveries from the field of proteomics to advance scientific understanding of the many varied roles protein expression plays in human health and disease. The journal coverage includes, but is not limited to, overviews of specific technological advances in the development of protein arrays, interaction maps, data archives and biological assays, performance of new technologies and prospects for future drug discovery. The journal adopts the unique Expert Review article format, offering a complete overview of current thinking in a key technology area, research or clinical practice, augmented by the following sections: Expert Opinion - a personal view on the most effective or promising strategies and a clear perspective of future prospects within a realistic timescale Article highlights - an executive summary cutting to the author''s most critical points.
期刊最新文献
Validating proteomic biomarkers in saliva: distinguishing between health and periodontal diseases. Optimization of glycopeptide enrichment techniques for the identification of clinical biomarkers. Cellular thermal shift assay: an approach to identify and assess protein target engagement. Digitalomics - digital transformation leading to omics insights. Formation of the Canadian Artificial Intelligence and Mass Spectrometry for systems biology (CAN-AIMS) consortium.
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