Evidence map›Paper›PMID 25265318›Full record

ArticlePloS one2014

Label-free LC-MSe in tissue and serum reveals protein networks underlying differences between benign and malignant serous ovarian tumors.

Wouter Wegdam, Carmen A Argmann, Gertjan Kramer, Johannes P Vissers, Marrije R Buist, Gemma G Kenter, Johannes M F G Aerts, Danielle Meijer, Perry D Moerland

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
0.6field-weighted citation impact, top 34% 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

15 citing papers in PubMed, 19 citations in OpenAlex.

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  15. Systems medicine: evolution of systems biology from bench to bedside.Wiley interdisciplinary reviews. Systems biology and medicine
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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

9 authors at 4 institutions in 2 countries.

Wouter WegdamDepartment of Gynecology, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands.
Carmen A ArgmannDepartment of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.
Gertjan KramerClinical Proteomics Group, Department of Medical Biochemistry, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands.
Johannes P VissersWaters Corporation MS Technologies Center, Manchester, United Kingdom.
Marrije R BuistDepartment of Gynecology, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands.
Gemma G KenterDepartment of Gynecology, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands.
Johannes M F G AertsClinical Proteomics Group, Department of Medical Biochemistry, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands.
Danielle MeijerDepartment of Gynecology, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands.
Perry D MoerlandBioinformatics Laboratory, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands.
University of Amsterdam · NLAcademic Medical Center · NLIcahn School of Medicine at Mount Sinai · USWaters (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo identify proteins and (molecular/biological) pathways associated with differences between benign and malignant epithelial ovarian tumors. EXPERIMENTAL PROCEDURES: Serum of six patients with a serous adenocarcinoma of the ovary was collected before treatment, with a control group consisting of six matched patients with a serous cystadenoma. In addition to the serum, homogeneous regions of cells exhibiting uniform histology were isolated from benign and cancerous tissue by laser microdissection. We subsequently employed label-free liquid chromatography tandem mass spectrometry (LC-MSe) to identify proteins in these serum and tissues samples. Analyses of differential expression between samples were performed using Bioconductor packages and in-house scripts in the statistical software package R. Hierarchical clustering and pathway enrichment analyses were performed, as well as network enrichment and interactome analysis using MetaCore.

resultsIn total, we identified 20 and 71 proteins that were significantly differentially expressed between benign and malignant serum and tissue samples, respectively. The differentially expressed protein sets in serum and tissue largely differed with only 2 proteins in common. MetaCore network analysis, however inferred GCR-alpha and Sp1 as common transcriptional regulators. Interactome analysis highlighted 14-3-3 zeta/delta, 14-3-3 beta/alpha, Alpha-actinin 4, HSP60, and PCBP1 as critical proteins in the tumor proteome signature based on their relative overconnectivity. The data have been deposited to the ProteomeXchange with identifier PXD001084. DISCUSSION: Our analysis identified proteins with both novel and previously known associations to ovarian cancer biology. Despite the small overlap between differentially expressed protein sets in serum and tissue, APOA1 and Serotransferrin were significantly lower expressed in both serum and cancer tissue samples, suggesting a tissue-derived effect in serum. Pathway and subsequent interactome analysis also highlighted common regulators in serum and tissue samples, suggesting a yet unknown role for PCBP1 in ovarian cancer pathophysiology.

Indexed as

AdultAgedChromatography, LiquidCluster AnalysisFemaleGene Regulatory NetworksHumansMass SpectrometryMiddle AgedNeoplasm ProteinsOvarian NeoplasmsProteomeNeoplasm ProteinsProteome

Identifiers

PMID25265318
PMCPMC4180266
OpenAlexW1970379597

What OpenQuestion holds

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LicenceCC BY
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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.