Evidence map›Paper›PMID 24548857›Full record

ArticleBritish journal of cancer2014

Quantitative proteomics in resected renal cancer tissue for biomarker discovery and profiling.

A Atrih, M A V Mudaliar, P Zakikhani, D J Lamont, J T-J Huang, S E Bray, G Barton, S Fleming, G Nabi

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in British journal of cancer, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed
4.8field-weighted citation impact, top 4% 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

41 citing papers in PubMed, 74 citations in OpenAlex.

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  17. Integrated Analysis of Three Publicly Available Gene Expression Profiles Identified Genes and Pathways Associated with Clear Cell Renal Cell Carcinoma.Medical science monitor : international medical journal of experimental and clinical research · 2020
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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 3 institutions in 1 country.

A AtrihFingerprints Proteomics Facility, College of Life Sciences, University of Dundee, Dundee, DD1 5EH, UK.
M A V Mudaliar1] Data Analysis Group, Division of Computational Biology, School of Research, College of Life Sciences, University of Dundee, Dundee, DD1 5EH, UK [2] Glasgow Polyomics, Wolfson Wohl Cancer Research Centre, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, G61 1QH, UK.
P ZakikhaniMedical Research Institute, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, UK.
D J LamontFingerprints Proteomics Facility, College of Life Sciences, University of Dundee, Dundee, DD1 5EH, UK.
J T-J HuangMedical Research Institute, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, UK.
S E BrayMedical Research Institute, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, UK.
G BartonData Analysis Group, Division of Computational Biology, School of Research, College of Life Sciences, University of Dundee, Dundee, DD1 5EH, UK.
S FlemingMedical Research Institute, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, UK.
G NabiMedical Research Institute, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, UK.
University of Dundee · GBNinewells Hospital · GBUniversity of Glasgow · GB

Funding

Wellcome Trust 097945
6 · The paper itself

Abstract

backgroundProteomics-based approaches for biomarker discovery are promising strategies used in cancer research. We present state-of-art label-free quantitative proteomics method to assess proteome of renal cell carcinoma (RCC) compared with noncancer renal tissues.

methodsFresh frozen tissue samples from eight primary RCC lesions and autologous adjacent normal renal tissues were obtained from surgically resected tumour-bearing kidneys. Proteins were extracted by complete solubilisation of tissues using filter-aided sample preparation (FASP) method. Trypsin digested proteins were analysed using quantitative label-free proteomics approach followed by data interpretation and pathways analysis.

resultsA total of 1761 proteins were identified and quantified with high confidence (MASCOT ion score threshold of 35 and P-value <0.05). Of these, 596 proteins were identified as differentially expressed between cancer and noncancer tissues. Two upregulated proteins in tumour samples (adipose differentiation-related protein and Coronin 1A) were further validated by immunohistochemistry. Pathway analysis using IPA, KOBAS 2.0, DAVID functional annotation and FLink tools showed enrichment of many cancer-related biological processes and pathways such as oxidative phosphorylation, glycolysis and amino acid synthetic pathways.

conclusionsOur study identified a number of differentially expressed proteins and pathways using label-free proteomics approach in RCC compared with normal tissue samples. Two proteins validated in this study are the focus of on-going research in a large cohort of patients.

Indexed as

AgedAged, 80 and overBiomarkers, TumorCarcinoma, Renal CellFemaleHumansKidney NeoplasmsMaleMass SpectrometryMiddle AgedNeoplasm ProteinsProteomicsSignal TransductionBiomarkers, TumorNeoplasm Proteins

Identifiers

PMID24548857
PMCPMC3960606
OpenAlexW2082238745

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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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.