Evidence map›Paper›PMID 24621579›Full record

ArticlePloS one2014

New miRNA profiles accurately distinguish renal cell carcinomas and upper tract urothelial carcinomas from the normal kidney.

Apostolos Zaravinos, George I Lambrou, Nikos Mourmouras, Patroklos Katafygiotis, Gregory Papagregoriou, Krinio Giannikou, Dimitris Delakas, Constantinos Deltas

Erratum issuedOpen 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. An erratum has been issued. Cited by 28 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 2 pooled it
4.8field-weighted citation impact, top 5% 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

28 citing papers in PubMed, 2 syntheses or guidelines pooled it, 50 citations in OpenAlex.

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  13. LncRNA SNHG5 regulates SOX4 expression through competitive binding to miR-489-3p in acute myeloid leukemia.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2020
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4 · The record

Corrections and comments

  • Erratum issued
5 · Who and what money

Authors and funding

8 authors at 3 institutions in 2 countries.

Apostolos ZaravinosMolecular Medicine Research Center and Laboratory of Molecular and Medical Genetics, Department of Biological Sciences, University of Cyprus, Nicosia, Cyprus. zaravinos.apostolos@ucy.ac.cy
George I LambrouChoremeio Research Laboratory, First Department of Pediatrics, University of Athens, Athens, Greece.
Nikos MourmourasDepartment of Urology, Asklipieio General Hospital, Athens, Greece.
Patroklos KatafygiotisDepartment of Urology, Asklipieio General Hospital, Athens, Greece.
Gregory PapagregoriouMolecular Medicine Research Center and Laboratory of Molecular and Medical Genetics, Department of Biological Sciences, University of Cyprus, Nicosia, Cyprus.
Krinio GiannikouDepartment of Medical Genetics, Medical School, University of Athens, Athens, Greece.
Dimitris DelakasDepartment of Urology, Asklipieio General Hospital, Athens, Greece.
Constantinos DeltasMolecular Medicine Research Center and Laboratory of Molecular and Medical Genetics, Department of Biological Sciences, University of Cyprus, Nicosia, Cyprus.
General Hospital Asklepieio Voulas · GRUniversity of Cyprus · CYNational and Kapodistrian University of Athens · GR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUpper tract urothelial carcinomas (UT-UC) can invade the pelvicalyceal system making differential diagnosis of the various histologically distinct renal cell carcinoma (RCC) subtypes and UT-UC, difficult. Correct diagnosis is critical for determining appropriate surgery and post-surgical treatments. We aimed to identify microRNA (miRNA) signatures that can accurately distinguish the most prevalent RCC subtypes and UT-UC form the normal kidney. METHODS AND

findingsmiRNA profiling was performed on FFPE tissue sections from RCC and UT-UC and normal kidney and 434 miRNAs were significantly deregulated in cancerous vs. the normal tissue. Hierarchical clustering distinguished UT-UCs from RCCs and classified the various RCC subtypes among them. qRT-PCR validated the deregulated expression profile for the majority of the miRNAs and ROC analysis revealed their capability to discriminate between tumour and normal kidney. An independent cohort of freshly frozen RCC and UT-UC samples was used to validate the deregulated miRNAs with the best discriminatory ability (AUC>0.8, p<0.001). Many of them were located within cytogenetic regions that were previously reported to be significantly aberrated. miRNA targets were predicted using the miRWalk algorithm and ingenuity pathway analysis identified the canonical pathways and curated networks of the deregulated miRNAs. Using the miRWalk algorithm, we further identified the top anti-correlated mRNA/miRNA pairs, between the deregulated miRNAs from our study and the top co-deregulated mRNAs among 5 independent ccRCC GEO datasets. The AB8/13 undifferentiated podocyte cells were used for functional assays using luciferase reporter constructs and the developmental transcription factor TFCP2L1 was proved to be a true target of miR-489, which was the second most upregulated miRNA in ccRCC.

conclusionsWe identified novel miRNAs specific for each RCC subtype and UT-UC, we investigated their putative targets, the networks and pathways in which they participate and we functionally verified the true targets of the top deregulated miRNAs.

Indexed as

AdultAgedAged, 80 and overCarcinoma, Renal CellChromosome AberrationsCohort StudiesFemaleGene Expression ProfilingHumansKidneyKidney NeoplasmsMaleMicroRNAsMiddle AgedUrotheliumMicroRNAs

Identifiers

PMID24621579
PMCPMC3951427
OpenAlexW1993427825

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.