4.6 Article

Altered Glycosylation of Human Alpha-1-Acid Glycoprotein as a Biomarker for Malignant Melanoma

期刊

MOLECULES
卷 26, 期 19, 页码 -

出版社

MDPI
DOI: 10.3390/molecules26196003

关键词

alpha-1-acid glycoprotein; biomarker; glycosylation; hydrophilic interaction chromatography; linear discriminant analysis; mass spectrometry; melanoma

资金

  1. Higher Education Institutional Excellence Program of the Ministry of Human Capacities in Hungary
  2. Lendulet (Momentum) Program of the Hungarian Academy of Sciences
  3. European Union [VEKOP-2.3.3-15-2017-00020]
  4. State of Hungary [VEKOP-2.3.3-15-2017-00020]
  5. New National Excellence Program of the Ministry of Human Capacities [UNKP-20-4-I-SE-30]
  6. European Regional Development Fund

向作者/读者索取更多资源

A high-resolution HILIC-MS/MS method was developed for analyzing N-glycan anthranilic acid derivatives, revealing differential glycan patterns in patients with high-risk malignant melanoma. The method successfully categorized samples based on their glycosylation pattern and showed superior diagnostic potential for malignant melanoma compared to traditional serological markers.
A high-resolution HILIC-MS/MS method was developed to analyze anthranilic acid derivatives of N-glycans released from human serum alpha-1-acid glycoprotein (AGP). The method was applied to samples obtained from 18 patients suffering from high-risk malignant melanoma as well as 19 healthy individuals. It enabled the identification of 102 glycan isomers separating isomers that differ only in sialic acid linkage (alpha-2,3, alpha-2,6) or in fucose positions (core, antenna). Comparative assessment of the samples revealed that upregulation of certain fucosylated glycans and downregulation of their nonfucosylated counterparts occurred in cancer patients. An increased ratio of isomers with more alpha-2,6-linked sialic acids was also observed. Linear discriminant analysis (LDA) combining 10 variables with the highest discriminatory power was employed to categorize the samples based on their glycosylation pattern. The performance of the method was tested by cross-validation, resulting in an overall classification success rate of 96.7%. The approach presented here is significantly superior to serological marker S100B protein in terms of sensitivity and negative predictive power in the population studied. Therefore, it may effectively support the diagnosis of malignant melanoma as a biomarker.

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