ArticleJournal of proteome research2012
Ion mobility-mass spectrometry analysis of serum N-linked glycans from esophageal adenocarcinoma phenotypes.
Article in Journal of proteome research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed.
- Advanced Mass Spectrometry Techniques for the Characterization of Carbohydrates.Handbook of experimental pharmacology · 2025Review
- High-Throughput Glycomic Methods.Chemical reviews · 2022Review
- Mass Spectrometry-Based Techniques to Elucidate the Sugar Code.Chemical reviews · 2022Review
- MS-based glycomics: An analytical tool to assess nervous system diseases.Frontiers in neuroscience · 2022Review
- Characterization of glycan isomers using magnetic carbon nanoparticles as a MALDI co-matrix.RSC advances · 2019Article
- Glycoproteomic markers of hepatocellular carcinoma-mass spectrometry based approaches.Mass spectrometry reviews · 2019Review
- Clinical application of quantitative glycomics.Expert review of proteomics · 2018Review
- Evaluation of Serum Glycoprotein Biomarker Candidates for Detection of Esophageal Adenocarcinoma and Surveillance of Barrett's Esophagus.Molecular & cellular proteomics : MCP · 2018Article
- Mass Spectrometry Approaches to Glycomic and Glycoproteomic Analyses.Chemical reviews · 2018Review
- Differential Fragmentation of Mobility-Selected Glycans via Ultraviolet Photodissociation and Ion Mobility-Mass Spectrometry.Journal of the American Society for Mass Spectrometry · 2017Article
- Delineation of disease phenotypes associated with esophageal adenocarcinoma by MALDI-IMS-MS analysis of serum N-linked glycans.The Analyst · 2017Article
- Quantitative Glycomics: A Combined Analytical and Bioinformatics Approach.Methods in enzymology · 2017Article
- Travelling-wave ion mobility and negative ion fragmentation of high-mannose N-glycans.Journal of mass spectrometry : JMS · 2016Article
- Serum glycoprotein-derived N- and O-linked glycans as cancer biomarkers.American journal of cancer research · 2016Review
- Serum Glycoprotein Biomarker Discovery and Qualification Pipeline Reveals Novel Diagnostic Biomarker Candidates for Esophageal Adenocarcinoma.Molecular & cellular proteomics : MCP · 2015Article
- Ion Mobility Mass Spectrometry for Ion Recovery and Clean-Up of MS and MS/MS Spectra Obtained from Low Abundance Viral Samples.Journal of the American Society for Mass Spectrometry · 2015Article
- Rectangular ion funnel: a new ion funnel interface for structures for lossless ion manipulations.Analytical chemistry · 2015Article
- Polysialylated N-glycans identified in human serum through combined developments in sample preparation, separations, and electrospray ionization-mass spectrometry.Analytical chemistry · 2014Article
- Mass spectrometry-based analysis of glycoproteins and its clinical applications in cancer biomarker discovery.Clinical proteomics · 2014Article
- Evidence for two new solution states of ubiquitin by IMS-MS analysis.The journal of physical chemistry. B · 2014Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Three disease phenotypes, Barrett's esophagus (BE), high-grade dysplasia (HGD), esophageal adenocarcinoma (EAC), and a set of normal control (NC) serum samples are examined using a combination of ion mobility spectrometry (IMS), mass spectrometry (MS), and principal component analysis (PCA) techniques. Samples from a total of 136 individuals were examined, including 7 characterized as BE, 12 as HGD, 56 as EAC, and 61 as NC. In typical data sets, it was possible to assign ∼20 to 30 glycan ions based on MS measurements. Ion mobility distributions for these ions show multiple features. In some cases, such as the [S1H5N4+3Na]3+ and [S1F1H5N4+3Na]3+ glycan ions, the ratio of intensities of high-mobility features to low-mobility features vary significantly for different groups. The degree to which such variations in mobility profiles can be used to distinguish phenotypes is evaluated for 11 N-linked glycan ions. An outlier analysis on each sample class followed by an unsupervised PCA using a genetic algorithm for pattern recognition reveals that EAC samples are separated from NC samples based on 46 features originating from the 11-glycan composite IMS distribution.
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Registered trials
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