Evidence mapPaperPMID 39242964Full record

ReviewNature reviews. Urology2025

Non-coding transcriptome profiles in clear-cell renal cell carcinoma.

Tereza Tesarova, Ondrej Fiala, Milan Hora, Radka Vaclavikova

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Urology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
field-weighted citation impact
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

2 citing papers in PubMed.

  1. Review
  2. Review
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

4 authors.

Tereza TesarovaToxicogenomics Unit, National Institute of Public Health, Prague, Czech Republic. tereza.tesarova@szu.cz.ORCID 0000-0002-1035-7375
Ondrej FialaDepartment of Oncology and Radiotherapeutics, Faculty of Medicine in Pilsen and University Hospital, Charles University, Pilsen, Czech Republic.
Milan HoraDepartment of Urology, Faculty of Medicine in Pilsen and University Hospital, Charles University, Pilsen, Czech Republic.ORCID 0000-0002-5061-3687
Radka VaclavikovaToxicogenomics Unit, National Institute of Public Health, Prague, Czech Republic.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Clear-cell renal cell carcinoma (ccRCC) is a common urological malignancy with an increasing incidence. The development of molecular biomarkers that can predict the response to treatment and guide personalized therapy selection would substantially improve patient outcomes. Dysregulation of non-coding RNA (ncRNA) has been shown to have a role in the pathogenesis of ccRCC. Thus, an increasing number of studies are being carried out with a focus on the identification of ncRNA biomarkers in ccRCC tissue samples and the connection of these markers with patients' prognosis, pathological stage and grade (including metastatic potential), and therapy outcome. RNA sequencing analysis led to the identification of several ncRNA biomarkers that are dysregulated in ccRCC and might have a role in ccRCC development. These ncRNAs have the potential to be prognostic and predictive biomarkers for ccRCC, with prospective applications in personalized treatment selection. Research on ncRNA biomarkers in ccRCC is advancing, but clinical implementation remains preliminary owing to challenges in validation, standardization and reproducibility. Comprehensive studies and integration of ncRNAs into clinical trials are essential to accelerate the clinical use of these biomarkers.

Indexed as

Carcinoma, Renal CellKidney NeoplasmsRNA, UntranslatedTranscriptomeBiomarkers, TumorHumansPrognosisBiomarkers, TumorRNA, Untranslated

Identifiers

PMID39242964

What Socratic holds

Textmetadata
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 Socratic graph.