Evidence map›Paper›PMID 20596302›Full record

ArticleJournal of proteomics & bioinformatics2010

Hydrophobic Fractionation Enhances Novel Protein Detection by Mass Spectrometry in Triple Negative Breast Cancer.

Ming Lu, Julian P Whitelegge, Stephen A Whelan, Jianbo He, Romaine E Saxton, Kym F Faull, Helena R Chang

Open access · greenAbstract read
In one paragraph

Article in Journal of proteomics & bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.0field-weighted citation impact, top 16% 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

4 citing papers in PubMed, 17 citations in OpenAlex.

  1. Review
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  4. 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

7 authors at 1 institution in 1 country.

Ming LuGonda/UCLA Breast Cancer Research Laboratory, David Geffen School of Medicine, Los Angeles, California.
Julian P Whitelegge
Stephen A Whelan
Jianbo He
Romaine E Saxton
Kym F Faull
Helena R Chang
Breast Cancer Research Foundation · US

Funding

Ethnicity-based proteomic biomarkers in breast cancerR01CA093736 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI CHANG, HELENA R · 2002 to 2004
$1.5M
NCI NIH HHS R01 CA093736
6 · The paper itself

Abstract

It is widely believed that discovery of specific, sensitive and reliable tumor biomarkers can improve the treatment of cancer. The goal of this study was to develop a novel fractionation protocol targeting hydrophobic proteins as possible cancer cell membrane biomarkers. Hydrophobic proteins of breast cancer tissues and cell lines were enriched by polymeric reverse phase columns. The retained proteins were eluted and digested for peptide identification by nano-liquid chromatography with tandem mass spectrometry using a hybrid linear ion-trap Orbitrap.Hundreds of proteins were identified from each of these three specimens: tumors, normal breast tissue, and breast cancer cell lines. Many of the identified proteins defined key cellular functions. Protein profiles of cancer and normal tissues from the same patient were systematically examined and compared. Stem cell markers were overexpressed in triple negative breast cancer (TNBC) compared with non-TNBC samples. Because breast cancer stem cells are known to be resistant to radiation and chemotherapy, and can be the source of metastasis frequently seen in patients with TNBC, our study may provide evidence of molecules promoting the aggressiveness of TNBC.The initial results obtained using a combination of hydrophobic fractionation and nano-LC mass spectrometry analysis of these proteins appear promising in the discovery of potential cancer biomarkers. When sufficiently refined, this approach may prove useful for early detection and better treatment of breast cancer.

Identifiers

PMID20596302
PMCPMC2894735
OpenAlexW1975102800

What OpenQuestion holds

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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.