Glycoconjugates are complex carbohydrate structures that serve as ligands for signaling receptors, often as multi-valent structures. Understanding the metabolism of carbohydrates and glycoconjugates in cancer is critical for clinical diagnosis and management. For example, tumor cells are known to favor monosaccharide metabolism by glycolysis through the expression of pyruvate kinase isoenzyme type M2. It remains controversial whether cancer cells favor the metabolism of specific types of glycoconjugates, or express abnormal glycoconjugate structures in a stochastic manner. Nevertheless, certain glycoconjugate structures have been successfully identified as targets for antibody therapy, such as ganglioside GD2 for pediatric neuroblastoma.
Significant amounts of data have been generated, and databases are being created for cancer glycoconjugates due to the invention of MALDI and ESI ionization technology, and the application of ion-trap mass spectrometers in the analysis of glycoconjugates. Strategies to interpret these data, mostly generated by chemists, in a biological setting present challenge. The aim of this article collection is to identify and overcome the barriers to communication. Firstly, scientists of all fields must find generally applicable ways to communicate on glyco-data, i.e., what are and where are the structures identified by mass spectrometry or other methods. Secondly, how to interpret the glyco-data in the context of specific biological settings including cancer cell lines, cancer biopsy tissues, and disease models? Lastly, how to combine the glyco-data with other big data including genomics, proteomics, and epigenetics?
In this Research Topic, we welcome Original Research and Review papers that focus on:
1) New insights, new technologies and software programs to connect mass spectrometry data to public databases such as GenBank, GeneCards, UniProt, Mouse Genome Informatics, and TCGA.
2) Big data on N-glycopeptidome, O-glycopeptididome, Glycolipidome, and Glycan-binding proteins in cancer
3) Big data of glycosyltransferases and related genes, and functional units of glycoconjugates in cancer.
Glycoconjugates are complex carbohydrate structures that serve as ligands for signaling receptors, often as multi-valent structures. Understanding the metabolism of carbohydrates and glycoconjugates in cancer is critical for clinical diagnosis and management. For example, tumor cells are known to favor monosaccharide metabolism by glycolysis through the expression of pyruvate kinase isoenzyme type M2. It remains controversial whether cancer cells favor the metabolism of specific types of glycoconjugates, or express abnormal glycoconjugate structures in a stochastic manner. Nevertheless, certain glycoconjugate structures have been successfully identified as targets for antibody therapy, such as ganglioside GD2 for pediatric neuroblastoma.
Significant amounts of data have been generated, and databases are being created for cancer glycoconjugates due to the invention of MALDI and ESI ionization technology, and the application of ion-trap mass spectrometers in the analysis of glycoconjugates. Strategies to interpret these data, mostly generated by chemists, in a biological setting present challenge. The aim of this article collection is to identify and overcome the barriers to communication. Firstly, scientists of all fields must find generally applicable ways to communicate on glyco-data, i.e., what are and where are the structures identified by mass spectrometry or other methods. Secondly, how to interpret the glyco-data in the context of specific biological settings including cancer cell lines, cancer biopsy tissues, and disease models? Lastly, how to combine the glyco-data with other big data including genomics, proteomics, and epigenetics?
In this Research Topic, we welcome Original Research and Review papers that focus on:
1) New insights, new technologies and software programs to connect mass spectrometry data to public databases such as GenBank, GeneCards, UniProt, Mouse Genome Informatics, and TCGA.
2) Big data on N-glycopeptidome, O-glycopeptididome, Glycolipidome, and Glycan-binding proteins in cancer
3) Big data of glycosyltransferases and related genes, and functional units of glycoconjugates in cancer.
