Evidence map›Paper›PMID 25522218›Full record

ReviewProteomics. Clinical applications2015

Targeted methods for quantitative analysis of protein glycosylation.

Radoslav Goldman, Miloslav Sanda

Abstract readReview
In one paragraph

Review in Proteomics. Clinical applications, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed.

  1. Article
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  3. Article
  4. Article
  5. Overview of protein posttranslational modifications in Arthropoda venoms.The journal of venomous animals and toxins including tropical diseases · 2022
    Review
  6. Article
  7. Article
  8. Review
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  10. Review
  11. Article
  12. Article
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  15. Review
  16. Article
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  18. Article
  19. Chemical Glycoproteomics.Chemical reviews · 2016
    Review
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Radoslav GoldmanDepartment of Oncology, Lombardi Comprehensive Cancer Center, Washington, DC, USA; Department of Biochemistry and Molecular and Cellular Biology, Georgetown University, Washington, DC, USA.
Miloslav Sanda

Funding

Glycans in Hepatocellular CarcinomaR01CA135069 · NCI · GEORGETOWN UNIVERSITY · PI GOLDMAN, RADOSLAV · 2009 to 2019
$4.1M
Alliance of Glycobiologists for Detection of CancerU01CA168926 · NCI · GEORGETOWN UNIVERSITY · PI GOLDMAN, RADOSLAV · 2012 to 2016
$2.0M
Basic Cancer Research in Cancer Health DisparitiesU01CA171146 · NCI · GEORGETOWN UNIVERSITY · PI GOLDMAN, RADOSLAV · 2012 to 2016
$1.5M
NCI NIH HHS R01 CA135069NCI NIH HHS U01 CA168926NCI NIH HHS U01 CA171146
6 · The paper itself

Abstract

Quantification of proteins by LC-MS/MS-MRM has become a standard method with broad projected clinical applicability. MRM quantification of protein modifications is, however, far less utilized, especially in the case of glycoproteins. This review summarizes current methods for quantitative analysis of protein glycosylation with a focus on MRM methods. We describe advantages of this quantitative approach, analytical parameters that need to be optimized to achieve reliable measurements, and point out the limitations. Differences between major classes of N- and O-glycopeptides are described and class-specific glycopeptide assays are demonstrated.

Indexed as

Protein Processing, Post-TranslationalAnimalsChromatography, LiquidGlycopeptidesGlycoproteinsGlycosylationHumansMass SpectrometryGlycopeptidesGlycoproteinsGlycosylationMRMMSQuantificationSite-specific glycoforms

Identifiers

PMID25522218
PMCPMC5780646

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.