Evidence map›Paper›PMID 33886004›Full record

ReviewCurrent urology reports2021

Biomarkers for Renal Cell Carcinoma Recurrence: State of the Art.

Michele Marchioni, Juan Gomez Rivas, Anamaria Autran, Moises Socarras, Simone Albisinni, Matteo Ferro, Luigi Schips, Roberto Mario Scarpa, Rocco Papalia, Francesco Esperto

Abstract readReview
In one paragraph

Review in Current urology reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

22 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Review
  6. Review
  7. Article
  8. Management of Renal Cell Carcinoma: Promising Biomarkers and the Challenges to Reach the Clinic.Clinical cancer research : an official journal of the American Association for Cancer Research · 2024
    Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Review
  15. Review
  16. Biomarkers in renal cell carcinoma and their targeted therapies: a review.Exploration of targeted anti-tumor therapy · 2023
    Review
  17. Review
  18. Article
  19. Review
  20. Article
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

10 authors.

Michele MarchioniUnit of Urology, Department of Medical, Oral and Biotechnological Sciences, SS. Annunziata Hospital, "G. d'Annunzio University", Chieti, Italy. mic.marchioni@gmail.com.ORCID http://orcid.org/0000-0002-1702-4127
Juan Gomez RivasHospital Clinico San Carlos, Madrid, Spain.
Anamaria AutranDepartment of Urology, Fundacion Jimemez Diaz, Madrid, Spain.
Moises SocarrasInstituto de Cirugia Urologica Avanzada (ICUA), Madrid, Spain.
Simone AlbisinniUrology Department, Université Libre de Bruxelles, Erasme Hospital, Brussels, Belgium.
Matteo FerroDepartment of Urology, European Institute of Oncology (IEO), IRCCS, Milan, Italy.
Luigi SchipsUnit of Urology, Department of Medical, Oral and Biotechnological Sciences, SS. Annunziata Hospital, "G. d'Annunzio University", Chieti, Italy.
Roberto Mario ScarpaDepartment of Urology, Campus Bio-Medico University, Rome, Italy.
Rocco PapaliaDepartment of Urology, Campus Bio-Medico University, Rome, Italy.
Francesco EspertoDepartment of Urology, Campus Bio-Medico University, Rome, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewWe aim to summarize the current state of art about the possible use of biomarkers for predicting renal cell carcinoma (RCC) recurrence after curative treatment. In addition, we aim to provide a snapshot about the clinical implication of biomarkers use for follow-up planification. RECENT

findingsA wide variety of biomarkers have been proposed. RCC biomarkers have been individuated in tumoral tissue, blood, and urine. A variety of molecules, including proteins, DNA, and RNA, warrant a good accuracy for RCC recurrence and progression prediction. Their use in prediction models might warrant a better patients' risk stratification. Future prognostic models will probably include a combination of classical features (tumor grade, stage, etc.) and novel biomarkers. Such models might allow a more accurate treatment and follow-up planification.

Indexed as

Biomarkers, TumorCarcinoma, Renal CellHumansKidney NeoplasmsLiquid BiopsyNeoplasm Recurrence, LocalPrognosisBiomarkers, TumorBiomarkersLiquid biopsymiRNARenal cell carcinoma

Identifiers

PMID33886004
PMCPMC8062344

What Socratic holds

Textmetadata
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 Socratic graph.