Evidence map›Paper›PMID 28673085›Full record

ArticleJournal of proteome research2017

Large-Scale Analysis of Breast Cancer-Related Conformational Changes in Proteins Using SILAC-SPROX.

Fang Liu, He Meng, Michael C Fitzgerald

Abstract read
In one paragraph

Article in Journal of proteome research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

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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

3 authors.

Fang LiuDepartment of Chemistry, Duke University , Durham, North Carolina 27708, United States.
He MengDepartment of Chemistry, Duke University , Durham, North Carolina 27708, United States.
Michael C FitzgeraldDepartment of Chemistry, Duke University , Durham, North Carolina 27708, United States.ORCID 0000-0002-6719-4722

Funding

Analysis of Protein-Ligand Binding on the Proteomic ScaleR01GM084174 · NIGMS · DUKE UNIVERSITY · PI FITZGERALD, MICHAEL C · 2009 to 2017
$2.6M
NIGMS NIH HHS R01 GM084174
6 · The paper itself

Abstract

Proteomic methods for disease state characterization and biomarker discovery have traditionally utilized quantitative mass spectrometry methods to identify proteins with altered expression levels in disease states. Here we report on the large-scale use of protein folding stability measurements to characterize different subtypes of breast cancer using the stable isotope labeling with amino acids in cell culture and stability of proteins from rates of oxidation (SILAC-SPROX) technique. Protein folding stability differences were studied in a comparison of two luminal breast cancer subtypes, luminal-A and -B (i.e., MCF-7 and BT-474 cells, respectively), and in a comparison of a luminal-A and basal subtype of the disease (i.e., MCF-7 and MDA-MB-468 cells, respectively). The 242 and 445 protein hits identified with altered stabilities in these comparative analyses included a large fraction with no significant expression level changes. This suggests thermodynamic stability measurements create a new avenue for protein biomarker discovery. A number of the identified protein hits are known from other biochemical studies to play a role in tumorigenesis and cancer progression. This not only substantiates the biological significance of the protein hits identified using the SILAC-SPROX approach, but it also helps elucidate the molecular basis for their disregulation and/or disfunction in cancer.

Indexed as

Amino Acid SequenceBiomarkers, TumorBreast NeoplasmsCarcinogenesisCell Line, TumorDisease ProgressionFemaleHumansHydrogen PeroxideIsotope LabelingMCF-7 CellsNeoplasm ProteinsOxidation-ReductionProtein FoldingProtein StabilityProteomeBiomarkers, TumorHydrogen PeroxideNeoplasm ProteinsProteomeBT-474chemical denaturationMCF-7MDA-MB-468protein folding

Identifiers

PMID28673085
PMCPMC5605765

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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.