Dataset Information

Mass spectrometry and the emerging field of glycomics.

ABSTRACT: The biological significance of protein and lipid glycosylation is well established. For example, cells respond to environmental stimuli by altering glycan structures on their surfaces, and cancer cells evade normal growth regulation in part by remodeling their surface glycans. In general, glycan chemical properties differ significantly from those of proteins, lipids, nucleic acids, and small molecule metabolites. Thus, advances in glycomics, a comprehensive study to identify all glycans in an organism, rely on the development of specialized analytical methods. Mass spectrometry (MS) is emerging as an enabling technology in the field of glycomics. This review summarizes recent developments in mass spectrometric analysis methods for protein-based glycomics and glycoproteomics workflows.

SUBMITTER: Zaia J

PROVIDER: S-EPMC2570164 | biostudies-literature | 2008 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Mass spectrometry and the emerging field of glycomics.

Zaia Joseph J

Chemistry & biology 20080901 9

The biological significance of protein and lipid glycosylation is well established. For example, cells respond to environmental stimuli by altering glycan structures on their surfaces, and cancer cells evade normal growth regulation in part by remodeling their surface glycans. In general, glycan chemical properties differ significantly from those of proteins, lipids, nucleic acids, and small molecule metabolites. Thus, advances in glycomics, a comprehensive study to identify all glycans in an or ...[more]

PMID: 18804025

Similar Datasets

Project description:BackgroundNovel molecular and statistical methods are in rising demand for disease diagnosis and prognosis with the help of recent advanced biotechnology. High-resolution mass spectrometry (MS) is one of those biotechnologies that are highly promising to improve health outcome. Previous literatures have identified some proteomics biomarkers that can distinguish healthy patients from cancer patients using MS data. In this paper, an MS study is demonstrated which uses glycomics to identify ovarian cancer. Glycomics is the study of glycans and glycoproteins. The glycans on the proteins may deviate between a cancer cell and a normal cell and may be visible in the blood. High-resolution MS has been applied to measure relative abundances of potential glycan biomarkers in human serum. Multiple potential glycan biomarkers are measured in MS spectra. With the objection of maximizing the empirical area under the ROC curve (AUC), an analysis method was considered which combines potential glycan biomarkers for the diagnosis of cancer.ResultsMaximizing the empirical AUC of glycomics MS data is a large-dimensional optimization problem. The technical difficulty is that the empirical AUC function is not continuous. Instead, it is in fact an empirical 0-1 loss function with a large number of linear predictors. An approach was investigated that regularizes the area under the ROC curve while replacing the 0-1 loss function with a smooth surrogate function. The constrained threshold gradient descent regularization algorithm was applied, where the regularization parameters were chosen by the cross-validation method, and the confidence intervals of the regression parameters were estimated by the bootstrap method. The method is called TGDR-AUC algorithm. The properties of the approach were studied through a numerical simulation study, which incorporates the positive values of mass spectrometry data with the correlations between measurements within person. The simulation proved asymptotic properties that estimated AUC approaches the true AUC. Finally, mass spectrometry data of serum glycan for ovarian cancer diagnosis was analyzed. The optimal combination based on TGDR-AUC algorithm yields plausible result and the detected biomarkers are confirmed based on biological evidence.ConclusionThe TGDR-AUC algorithm relaxes the normality and independence assumptions from previous literatures. In addition to its flexibility and easy interpretability, the algorithm yields good performance in combining potential biomarkers and is computationally feasible. Thus, the approach of TGDR-AUC is a plausible algorithm to classify disease status on the basis of multiple biomarkers.

Dataset Information

Mass spectrometry and the emerging field of glycomics.

Publications

Mass spectrometry and the emerging field of glycomics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets