Evidence map›Paper›PMID 39337390›Full record

ArticleInternational journal of molecular sciences2024

Exploring Protein Post-Translational Modifications of Breast Cancer Cells Using a Novel Combinatorial Search Algorithm.

Mariela Vasileva-Slaveva, Angel Yordanov, Gergana Metodieva, Metodi V Metodiev

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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

4 authors.

Mariela Vasileva-SlavevaResearch Institute, Medical University of Pleven, 5800 Pleven, Bulgaria.ORCID 0000-0002-6180-3742
Angel YordanovResearch Institute, Medical University of Pleven, 5800 Pleven, Bulgaria.ORCID 0000-0002-7719-382X
Gergana MetodievaSchool of Life Sciences, University of Essex, Colchester CO4 3SQ, UK.
Metodi V MetodievResearch Institute, Medical University of Pleven, 5800 Pleven, Bulgaria.ORCID 0000-0001-6810-0719

Funding

Scientific Investigations, Bulgaria BG-175467353-2022-04-0186
6 · The paper itself

Abstract

Post-translational modification of proteins plays an important role in cancer cell biology. Proteins encoded by oncogenes may be activated by phosphorylation, products of tumour suppressors might be inactivated by phosphorylation or ubiquitinylation, which marks them for degradation; chromatin-binding proteins are often methylated and/or acetylated. These are just a few of the many hundreds of post-translational modifications discovered by years of painstaking experimentation and the chemical analysis of purified proteins. In recent years, mass spectrometry-based proteomics emerged as the principal technique for identifying such modifications in samples from cultured cells and tumour tissue. Here, we used a recently developed combinatorial search algorithm implemented in the MGVB toolset to identify novel modifications in samples from breast cancer cell lines. Our results provide a rich resource of coupled protein abundance and post-translational modification data seen in the context of an important biological function-the response of cells to interferon gamma treatment-which can serve as a starting point for future investigations to validate promising modifications and explore the utility of the underlying molecular mechanisms as potential diagnostic or therapeutic targets.

Indexed as

AlgorithmsBreast NeoplasmsProtein Processing, Post-TranslationalProteomicsCell Line, TumorFemaleHumansPhosphorylationcancer proteomicscombinatorial searchmass spectrometrypost-translational modification of proteins

Identifiers

PMID39337390
PMCPMC11432537

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Registered trials

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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.