Evidence map›Paper›PMID 36378434›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2023

DIGE-Based Biomarker Discovery in Blood Cancers.

Katie Dunphy, Paul Dowling

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
1.3field-weighted citation impact, top 21% of its field
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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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 at 1 institution in 1 country.

Katie DunphyDepartment of Biology, Maynooth University, National University of Ireland, Maynooth, Co. Kildare, Ireland. Katie.Dunphy@mu.ie.
Paul DowlingDepartment of Biology, Maynooth University, National University of Ireland, Maynooth, Co. Kildare, Ireland.
National University of Ireland, Maynooth · IE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer of blood or bone marrow-derived cells dysregulates normal hematopoiesis and accounts for over 6% of all cancer cases annually. Proteomic analyses of blood cancers have improved our understanding of disease mechanisms and identified numerous proteins of clinical relevance. For many years, gel-based proteomic studies have aided in the discovery of novel diagnostic, prognostic, and predictive biomarkers, as well as therapeutic targets, in various diseases, including blood cancer. Fluorescence two-dimensional difference gel electrophoresis (2D-DIGE) facilitates comparative proteomic research to identify differential protein expression in a simple and reproducible manner. The versatility of 2D-DIGE as a quantitative proteomic technique has provided insight into various aspects of blood cancer pathology, including disease development, prognostic subtypes, and drug resistance. The ability to couple 2D-DIGE with additional downstream mass spectrometry-based techniques yields comprehensive workflows capable of identifying proteins of biological and clinical significance. The application of 2D-DIGE in blood cancer research has significantly contributed to the increasingly important initiative of precision medicine. This chapter will focus on the influential role of 2D-DIGE as a tool in blood cancer research.

Indexed as

Hematologic NeoplasmsNeoplasmsElectrophoresis, Gel, Two-DimensionalHumansMass SpectrometryProteinsProteomicsTwo-Dimensional Difference Gel ElectrophoresisProteins2D-DIGEBiomarkersBlood cancerGel electrophoresisLeukemiaLymphomaMultiple myelomaProteomics

Identifiers

PMID36378434
OpenAlexW4309048538

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.