47
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Colorectal cancer cell lines show striking diversity of their O-glycome reflecting the cellular differentiation phenotype

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Alterations in protein glycosylation in colorectal cancer (CRC) have been extensively studied using cell lines as models. However, little is known about their O-glycome and the differences in glycan biosynthesis in different cell types. To provide a better understanding of the variation in O-glycosylation phenotypes and their association with other molecular features, an in-depth O-glycosylation analysis of 26 different CRC cell lines was performed. The released O-glycans were analysed on porous graphitized carbon nano-liquid chromatography system coupled to a mass spectrometer via electrospray ionization (PGC-nano-LC–ESI-MS/MS) allowing isomeric separation as well as in-depth structural characterization. Associations between the observed glycan phenotypes with previously reported cell line transcriptome signatures were examined by canonical correlation analysis. Striking differences are observed between the O-glycomes of 26 CRC cell lines. Unsupervized principal component analysis reveals a separation between well-differentiated colon-like and undifferentiated cell lines. Colon-like cell lines are characterized by a prevalence of I-branched and sialyl Lewis x/a epitope carrying glycans, while most undifferentiated cell lines show absence of Lewis epitope expression resulting in dominance of truncated α2,6-core sialylated glycans. Moreover, the expression of glycan signatures associates with the expression of glycosyltransferases that are involved in their biosynthesis, providing a deeper insight into the regulation of glycan biosynthesis in different cell types. This untargeted in-depth screening of cell line O-glycomes paves the way for future studies exploring the role of glycosylation in CRC development and drug response leading to discovery of novel targets for the development of anti-cancer antibodies.

          Electronic supplementary material

          The online version of this article (10.1007/s00018-020-03504-z) contains supplementary material, which is available to authorized users.

          Related collections

          Most cited references53

          • Record: found
          • Abstract: found
          • Article: not found

          Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012.

          Estimates of the worldwide incidence and mortality from 27 major cancers and for all cancers combined for 2012 are now available in the GLOBOCAN series of the International Agency for Research on Cancer. We review the sources and methods used in compiling the national cancer incidence and mortality estimates, and briefly describe the key results by cancer site and in 20 large "areas" of the world. Overall, there were 14.1 million new cases and 8.2 million deaths in 2012. The most commonly diagnosed cancers were lung (1.82 million), breast (1.67 million), and colorectal (1.36 million); the most common causes of cancer death were lung cancer (1.6 million deaths), liver cancer (745,000 deaths), and stomach cancer (723,000 deaths). © 2014 UICC.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            The Consensus Molecular Subtypes of Colorectal Cancer

            Colorectal cancer (CRC) is a frequently lethal disease with heterogeneous outcomes and drug responses. To resolve inconsistencies among the reported gene expression–based CRC classifications and facilitate clinical translation, we formed an international consortium dedicated to large-scale data sharing and analytics across expert groups. We show marked interconnectivity between six independent classification systems coalescing into four consensus molecular subtypes (CMS) with distinguishing features: CMS1 (MSI Immune, 14%), hypermutated, microsatellite unstable, strong immune activation; CMS2 (Canonical, 37%), epithelial, chromosomally unstable, marked WNT and MYC signaling activation; CMS3 (Metabolic, 13%), epithelial, evident metabolic dysregulation; and CMS4 (Mesenchymal, 23%), prominent transforming growth factor β activation, stromal invasion, and angiogenesis. Samples with mixed features (13%) possibly represent a transition phenotype or intra-tumoral heterogeneity. We consider the CMS groups the most robust classification system currently available for CRC – with clear biological interpretability – and the basis for future clinical stratification and subtype–based targeted interventions.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              mixOmics: An R package for ‘omics feature selection and multiple data integration

              The advent of high throughput technologies has led to a wealth of publicly available ‘omics data coming from different sources, such as transcriptomics, proteomics, metabolomics. Combining such large-scale biological data sets can lead to the discovery of important biological insights, provided that relevant information can be extracted in a holistic manner. Current statistical approaches have been focusing on identifying small subsets of molecules (a ‘molecular signature’) to explain or predict biological conditions, but mainly for a single type of ‘omics. In addition, commonly used methods are univariate and consider each biological feature independently. We introduce mixOmics, an R package dedicated to the multivariate analysis of biological data sets with a specific focus on data exploration, dimension reduction and visualisation. By adopting a systems biology approach, the toolkit provides a wide range of methods that statistically integrate several data sets at once to probe relationships between heterogeneous ‘omics data sets. Our recent methods extend Projection to Latent Structure (PLS) models for discriminant analysis, for data integration across multiple ‘omics data or across independent studies, and for the identification of molecular signatures. We illustrate our latest mixOmics integrative frameworks for the multivariate analyses of ‘omics data available from the package.
                Bookmark

                Author and article information

                Contributors
                m.wuhrer@lumc.nl
                Journal
                Cell Mol Life Sci
                Cell Mol Life Sci
                Cellular and Molecular Life Sciences
                Springer International Publishing (Cham )
                1420-682X
                1420-9071
                31 March 2020
                31 March 2020
                2021
                : 78
                : 1
                : 337-350
                Affiliations
                [1 ]GRID grid.10419.3d, ISNI 0000000089452978, Center for Proteomics and Metabolomics, , Leiden University Medical Center, ; Postbus 9600, 2300 RC Leiden, The Netherlands
                [2 ]GRID grid.8761.8, ISNI 0000 0000 9919 9582, Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, , University of Gothenburg, ; Gothenburg, Sweden
                Author information
                http://orcid.org/0000-0002-9310-4110
                Article
                3504
                10.1007/s00018-020-03504-z
                7867528
                32236654
                549e05e4-8b28-4e68-b8f6-c5deb3a97149
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 3 December 2019
                : 5 March 2020
                : 9 March 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100010661, Horizon 2020 Framework Programme;
                Award ID: 676421
                Award Recipient :
                Categories
                Original Article
                Custom metadata
                © Springer Nature Switzerland AG 2021

                Molecular biology
                o-glycosylation,glycomics,cell lines,colorectal cancer,mass spectrometry,porous graphitized carbon liquid chromatography

                Comments

                Comment on this article

                scite_
                0
                0
                0
                0
                Smart Citations
                0
                0
                0
                0
                Citing PublicationsSupportingMentioningContrasting
                View Citations

                See how this article has been cited at scite.ai

                scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

                Similar content650

                Cited by31

                Most referenced authors1,520