Systematic review: Glycomics as diagnostic markers for hepatocellular carcinoma

糖组学 岩藻糖基化 医学 肝细胞癌 内科学 肿瘤科 生物信息学 糖蛋白 生物 聚糖 分子生物学
作者
Emma Butaye,Nicky Somers,Lorenz Grossar,N. Pauwels,Sander Lefere,Lindsey Devisscher,Sarah Raevens,Anja Geerts,Leander Meuris,Nico Callewaert,Hans Van Vlierberghe,Xavier Verhelst
出处
期刊:Alimentary Pharmacology & Therapeutics [Wiley]
卷期号:59 (1): 23-38 被引量:4
标识
DOI:10.1111/apt.17748
摘要

Summary Background Hepatocellular carcinoma (HCC) is the most prevalent primary liver cancer with one of the highest cancer‐related mortality rates worldwide. Early diagnosis is crucial for improving the therapeutic options and reducing the disease‐related mortality. Aim To investigate serum N‐glycomics as diagnostic markers for HCC. Methods We performed a comprehensive search in PubMed, EMBASE, Web of Science and Scopus through August 17, 2023. Eligible studies assessed the potential use of serum N‐glycomics as diagnostic biomarkers for HCC. Study selection, data extraction and quality assessment were performed by two independent reviewers. Results Of the 48 articles included, 11 evaluated the utility of N‐glycomics for the diagnosis of HCC in whole serum while the remaining articles focused on specific protein glycoforms or protein levels. Of these specific proteins, haptoglobin, alpha‐fetoprotein (AFP), kininogen (Kin), α‐1‐antitrypsin and Golgi protein 73 (GP73) were the most frequently studied. Increased levels of fucosylation and branching presented as the most prevalent post‐translational modifications of glycoproteins in patients with HCC compared to controls. Notably, glycomics‐based biomarkers may provide a clinical benefit for the diagnosis of early HCC, as several algorithms achieved AUCs between 0.92–0.97. However, these were based on single studies with limited sample sizes and should therefore be validated. Conclusions Alterations in serum N‐glycomics, characterised by increased levels of fucosylation and branching, have potential as diagnostic biomarkers for HCC. Optimisation of study design, patient selection and analysing techniques are needed before clinical implementation will be possible.
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