Evidence map›Paper›PMID 26482227›Full record

ArticleJournal of translational medicine2015

Tumor size, stage and grade alterations of urinary peptidome in RCC.

Clizia Chinello, Marta Cazzaniga, Gabriele De Sio, Andrew James Smith, Angelica Grasso, Bernardo Rocco, Stefano Signorini, Marco Grasso, Silvano Bosari, Italo Zoppis and 2 more

Open access · goldAbstract read
In one paragraph

Article in Journal of translational medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
1.7field-weighted citation impact, top 14% 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

17 citing papers in PubMed, 44 citations in OpenAlex.

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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

12 authors at 4 institutions in 1 country.

Clizia ChinelloDepartment of Health Science, School of Medicine, University of Milano-Bicocca (UNIMIB), Via Cadore, 48, 20900, Monza, Italy. clizia.chinello@unimib.it.
Marta CazzanigaDepartment of Health Science, School of Medicine, University of Milano-Bicocca (UNIMIB), Via Cadore, 48, 20900, Monza, Italy. m.cazzaniga15@campus.unimib.it.
Gabriele De SioDepartment of Health Science, School of Medicine, University of Milano-Bicocca (UNIMIB), Via Cadore, 48, 20900, Monza, Italy. g.desio@campus.unimib.it.
Andrew James SmithDepartment of Health Science, School of Medicine, University of Milano-Bicocca (UNIMIB), Via Cadore, 48, 20900, Monza, Italy. andyjamessmith6@hotmail.com.
Angelica GrassoUrology Unit, Department of Specialistic Surgical Sciences, Ospedale Maggiore Policlinico Foundation, Milan, Italy. angelica_grasso@yahoo.it.
Bernardo RoccoUrology Unit, Department of Specialistic Surgical Sciences, Ospedale Maggiore Policlinico Foundation, Milan, Italy. bernardo.rocco@gmail.com.
Stefano SignoriniDepartment of Laboratory Medicine, Hospital of Desio, Desio, Italy. stefano.signorini@unimib.it.
Marco GrassoDepartment of Surgical Pathology, Cytology, Medical Genetics and Nephropathology, Azienda Ospedaliera San Gerardo, Monza, Italy. m.grasso@hsgerardo.org.
Silvano BosariDepartment of Medicine, Surgery and Dental Sciences, Pathology Unit, IRCCS-Policlinico Foundation, Mangiagalli and Regina Elena, University of Milan, Milan, Italy. silvano.bosari@unimi.it.
Italo ZoppisDepartment of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy. zoppis@disco.unimib.it.
Giancarlo MauriDepartment of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy. giancarlo.mauri@unimib.it.
Fulvio MagniDepartment of Health Science, School of Medicine, University of Milano-Bicocca (UNIMIB), Via Cadore, 48, 20900, Monza, Italy. fulvio.magni@unimib.it.
University of Milano-Bicocca · ITOspedale Maggiore · ITAzienda Ospedaliera San Gerardo · ITUniversity of Milan · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSeveral promising biomarkers have been found for RCC, but none of them has been used in clinical practice for predicting tumour progression. The most widely used features for predicting tumour aggressiveness still remain the cancer stage, size and grade. Therefore, the aim of our study is to investigate the urinary peptidome to search and identify peptides whose concentrations in urine are linked to tumour growth measure and clinical data.

methodsA proteomic approach applied to ccRCC urinary peptidome (n = 117) based on prefractionation with activated magnetic beads followed by MALDI-TOF profiling was used. A systematic correlation study was performed on urinary peptide profiles obtained from MS analysis. Peptide identity was obtained by LC-ESI-MS/MS.

resultsFifteen, twenty-six and five peptides showed a statistically significant alteration of their urinary concentration according to tumour size, pT and grade, respectively. Furthermore, 15 and 9 signals were observed to have urinary levels statistically modified in patients at different pT or grade values, even at very early stages. Among them, C1RL, A1AGx, ZAG2G, PGBM, MMP23, GP162, ADA19, G3P, RSPH3, DREB, NOTC2 SAFB2 and CC168 were identified.

conclusionsWe identified several peptides whose urinary abundance varied according to tumour size, stage and grade. Among them, several play a possible role in tumorigenesis, progression and aggressiveness. These results could be a useful starting point for future studies aimed at verifying their possible use in the managements of RCC patients.

Indexed as

AdultAgedAged, 80 and overBiomarkers, TumorCarcinoma, Renal CellDisease ProgressionFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansKidney NeoplasmsMaleMiddle AgedNeoplasm StagingPeptidesProteomicsBiomarkers, TumorPeptides

Identifiers

PMID26482227
PMCPMC4617827
OpenAlexW2099973456

What OpenQuestion holds

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LicenceCC BY
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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.