Comparative Glycoproteomic Analysis in Cancer

Unlock Cancer Mysteries with Cutting-edge Comparative Glycoproteomic Analysis

At CD BioGlyco, we are committed to advancing the development of life sciences through innovative technologies. As an important part of our diversified services, we provide Glycoinformatics-assisted Glycomics Analysis Services, which deeply combine advanced glycomics technology with bioinformatics. Within this broad service framework, we further refine the service direction, among which Glycoinformatics-assisted Disease Glycomics Analysis Service focuses on analyzing the changes in glycan chains in disease states.

In response to the current major challenge in the global health field of cancer, we specially offer Glycoinformatics-assisted Cancer Glycomics Analysis Services. At this level, comparative glycoproteomic analysis in cancer not only integrates high-resolution glycoproteomics analysis technology but also deeply integrates the powerful analytical capabilities of glycoinformatics. By comparing the differences in glycosylation patterns of glycoproteins between cancer and healthy samples, it accurately explores cancer-specific biomarkers and molecular mechanisms.

Diagrammatic representation of glycoproteomic analysis for comparison in cancer. (CD BioGlyco)

Sample preparation and enrichment

Sample preparation: We process a variety of biological samples, such as cancer cell lines and primary tissues.

Glycoprotein enrichment: Samples undergo our specialized glycoprotein enrichment protocols, which typically rely on lectin affinity chromatography or hydrazide chemistry to selectively isolate glycoproteins.

Glycoproteomic analysis

Protein digestion: We digest the enriched glycoproteins using specific proteases such as trypsin to generate glycopeptides.

Mass spectrometry (MS): We employ high-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS) to identify and quantify glycopeptides. Orbitrap or Q-TOF analysis provides the mass accuracy and resolution required for glycopeptide analysis.

Glycan analysis: We employ techniques such as matrix-assisted laser desorption/ionization (MALDI)-MS or LC-MS to elucidate the glycan structures attached to glycopeptides.

Data processing and glycoinformatics

Bioinformatics tools: We process the raw data from MS using specialized bioinformatics tools for glycoprotein identification and glycopeptide quantification.

Glycoinformatics tools: We utilize advanced software tools and databases to interpret glycan structures and their biological significance.

Comparative analysis: We analyze the differential expression and glycosylation patterns between cancer and normal samples.

Validation

Biochemical assays: We validate the results of MS analysis using traditional biochemical assays such as Western blot and ELISA.

Functional studies: We perform follow-up functional assays, including cell proliferation, migration, and invasion assays, which help validate the biological role of the identified glycoproteins in cancer.

Publication Data

Journal: Mol Cell Proteomics

IF: 7.381

Published: 2017

Results: The researchers introduced GlycoPAT, a detailed, open-source, modular software designed for analyzing glycoproteomics data. This tool included three significant innovations: (1) "SmallGlyPep," a minimalistic linear depiction of glycopeptides tailored for MSn data analysis; (2) an innovative scoring method that computes an "Ensemble Score (ES)," enabling cross-correlation and probability-based evaluations to score and rank MS/MS spectra for both N- and O-linked glycopeptides; and (3) a false discovery rate (FDR) assessment method that generated decoy glycopeptides by concurrently shuffling the amino acid sequence and inserting artificial monosaccharides through modifications of the original sugar mass. GlycoPAT was utilized to document site-specific glycosylation on straightforward glycoproteins, and standard protein mixtures. Moreover, it identified 960 distinct glycopeptides in cell lysates from prostate cancer cells.

Fig.1 The nomenclature and comprehensive data analysis framework of SmallGlyPep (SGP1.0).Fig.1 The naming convention for SmallGlyPep (SGP1.0) and the comprehensive data analysis approach. (Liu, et al., 2017)

Applications

  • Comparative glycoproteomics analysis can be used for cancer biomarker discovery.
  • Comparative glycoproteomics analysis is used to reveal how glycosylation modification of glycoproteins in cancer cells affects the activation or inhibition of signaling pathways.
  • Comparative glycoproteomics analysis can be used to study the post-translational modification mechanism of cancer proteins. This technology deeply analyzes the glycosylation modification mechanism of glycoproteins in cancer cells, including the synthesis, processing, and transport of glycan chains.

Advantages of Us

  • Our service uses advanced glycomic informatics technology to deeply analyze the glycosylation modifications of proteins in cancer samples, which often show significant differences in healthy and diseased states.
  • Our service helps to efficiently screen glycoproteins and their glycosylation characteristics that are closely related to the occurrence and development of cancer.
  • We have a team of experts in the fields of glycomics, bioinformatics, and cancer biology with rich experience and deep expertise. Throughout the service process, we provide clients with full technical support and consulting services.

Frequently Asked Questions

  • If I have questions about some of the results in the report, do you provide follow-up consulting support?
    • Of course, we attach great importance to communication and cooperation with our clients. If you have questions about any results in the report or need further explanation, our expert team will provide follow-up consulting support. We will patiently answer your questions and provide additional data analysis or experimental validation services as needed.
  • Are there any specific limitations or challenges in cancer glycoproteomics analysis?
    • Yes, there are several challenges and limitations to consider:
    • Complexity of glycosylation: Glycosylation is a highly complex and dynamic post-translational modification, and it is difficult to fully capture and interpret all glycosylation events.
    • Sample quality: Poor sample quality or improper handling can lead to glycoprotein degradation and affect the reliability of results.
    • Data interpretation: The amount and complexity of the data require complex bioinformatics tools and expertise for accurate analysis.
    • Standardization: The lack of standardized protocols and reference materials sometimes leads to differences in results between different laboratories.

At CD BioGlyco, our cancer comparative glycoproteomic analysis service deeply explores the complex changes of glycoprotein glycosylation in cancer through the combination of glycoproteomics analysis and glycoinformatics analysis, providing strong support for cancer-related research. Please feel free to contact us if you are interested in our comparative glycoproteomic analysis in cancer.

Reference

  1. Liu G; et al. A comprehensive, open-source platform for mass spectrometry-based glycoproteomics data analysis. Mol Cell Proteomics. 2017, 16(11): 2032-2047.
For research use only. Not intended for any diagnostic use.
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