Glycosylation Signatures of Thyroglobulin in Papillary Thyroid Carcinoma

IF 2.6 3区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

ELECTROPHORESIS Pub Date : 2026-09-14 Epub Date: 2026-07-10 DOI:10.1002/elps.70130

Ali Hayatu, Esra Nur Akyildiz, Betul Karabudak, Sena Aksoy, Harun Erol, Bekir Salih, Hacı Mehmet Kayili

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Abstract

Thyroglobulin (Tg) is a clinically established biomarker for thyroid cancer; however, its diagnostic specificity remains limited due to confounding benign conditions. This study investigated changes in the site-specific N-glycosylation profiles of thyroglobulin protein derived from papillary thyroid cancer and adjacent healthy tissues. Using a bottom-up mass spectrometry-based glycoproteomic approach, we analyzed site-specific glycan compositions in retrospective patient tissue samples. Nine distinct N-glycosylation sites were identified and quantified in this study. Statistical analysis revealed significant differences in the site-specific glycosylation profiles. The glycan compositions HexNAc(3)Hex(5) and HexNAc(3)Hex(6) at the Asn1365 site exhibited a discriminatory potential. Dimensionality reduction and hierarchical clustering based on the top four glycopeptide variants showed observable grouping of tumor versus normal samples. Furthermore, supervised machine learning models were used to evaluate these features, with a neural network achieving the highest diagnostic performance (area under the curve [AUC] = 0.83). Feature importance analysis indicated that Asn1365-HexNAc(3)Hex(6) was the strongest contributor to the predictive power of the model. The observed alterations in specific glycan compositions, combined with machine learning evaluations, may offer improved diagnostic specificity and provide promising avenues for personalized thyroid cancer management.

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甲状腺乳头状癌甲状腺球蛋白的糖基化特征。

甲状腺球蛋白（Tg）是临床上确定的甲状腺癌生物标志物；然而，其诊断特异性仍然有限，由于混杂良性条件。本研究调查了甲状腺乳头状癌和邻近健康组织中甲状腺球蛋白蛋白的位点特异性n -糖基化谱的变化。使用自下而上的质谱为基础的糖蛋白组学方法，我们分析了回顾性患者组织样本中特定位点的聚糖组成。本研究鉴定并量化了9个不同的n -糖基化位点。统计分析显示位点特异性糖基化谱存在显著差异。在Asn1365位点上的糖基组合HexNAc(3)Hex(5)和HexNAc(3)Hex(6)表现出区别性。基于前四种糖肽变异的降维和分层聚类显示了肿瘤与正常样本的可观察分组。此外，使用监督机器学习模型来评估这些特征，其中神经网络获得了最高的诊断性能（曲线下面积[AUC] = 0.83）。特征重要性分析表明，Asn1365-HexNAc(3)Hex(6)对模型的预测能力贡献最大。观察到的特定聚糖组成的改变，结合机器学习评估，可能提供更好的诊断特异性，并为个性化甲状腺癌管理提供有希望的途径。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ELECTROPHORESIS 生物-分析化学

CiteScore

6.30

自引率

13.80%

发文量

244

审稿时长

1.9 months

期刊介绍： ELECTROPHORESIS is an international journal that publishes original manuscripts on all aspects of electrophoresis, and liquid phase separations (e.g., HPLC, micro- and nano-LC, UHPLC, micro- and nano-fluidics, liquid-phase micro-extractions, etc.). Topics include new or improved analytical and preparative methods, sample preparation, development of theory, and innovative applications of electrophoretic and liquid phase separations methods in the study of nucleic acids, proteins, carbohydrates natural products, pharmaceuticals, food analysis, environmental species and other compounds of importance to the life sciences. Papers in the areas of microfluidics and proteomics, which are not limited to electrophoresis-based methods, will also be accepted for publication. Contributions focused on hyphenated and omics techniques are also of interest. Proteomics is within the scope, if related to its fundamentals and new technical approaches. Proteomics applications are only considered in particular cases. Papers describing the application of standard electrophoretic methods will not be considered. Papers on nanoanalysis intended for publication in ELECTROPHORESIS should focus on one or more of the following topics: • Nanoscale electrokinetics and phenomena related to electric double layer and/or confinement in nano-sized geometry • Single cell and subcellular analysis • Nanosensors and ultrasensitive detection aspects (e.g., involving quantum dots, "nanoelectrodes" or nanospray MS) • Nanoscale/nanopore DNA sequencing (next generation sequencing) • Micro- and nanoscale sample preparation • Nanoparticles and cells analyses by dielectrophoresis • Separation-based analysis using nanoparticles, nanotubes and nanowires.