Evidence map›Paper›PMID 32512891›Full record

ReviewCells2020

Targeting Cancer Stem Cells to Overcome Therapy Resistance in Ovarian Cancer.

Sandra Muñoz-Galván, Amancio Carnero

Abstract readReview
In one paragraph

Review in Cells, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.

0numbers the graph read from it
0cells of the map it votes in
53citing 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

53 citing papers in PubMed.

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  15. Loss of XIST lncRNA unlocks stemness and cellular plasticity in ovarian cancer.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  16. Personalized Versus Precision Nanomedicine for Treatment of Ovarian Cancer.Small (Weinheim an der Bergstrasse, Germany) · 2024
    Article
  17. Article
  18. Article
  19. Article
  20. 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

2 authors.

Sandra Muñoz-GalvánInstituto de Biomedicina de Sevilla, IBIS, Hospital Universitario Virgen del Rocío, Universidad de Sevilla, Consejo Superior de Investigaciones Científicas, Avda. Manuel Siurot s/n, 41013 Seville, Spain.
Amancio CarneroInstituto de Biomedicina de Sevilla, IBIS, Hospital Universitario Virgen del Rocío, Universidad de Sevilla, Consejo Superior de Investigaciones Científicas, Avda. Manuel Siurot s/n, 41013 Seville, Spain.ORCID 0000-0003-4357-3979

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ovarian cancer is the most lethal gynecological malignancy due to its late detection and high recurrence rate. Resistance to conventional platinum-based therapies and metastasis are attributed to a population of cells within tumors called cancer stem cells, which possess stem-like features and are able to recapitulate new tumors. Recent studies have deepened the understanding of the biology of ovarian cancer stem cells and their special properties and have identified multiple markers and signaling pathways responsible for their self-renewal abilities. Targeting cancer stem cells represents the most promising strategy for overcoming therapy resistance and reducing mortality in ovarian cancer, but further efforts must be made to improve our understanding of the mechanisms involved in therapy resistance. In this review, we summarize our current knowledge about ovarian cancer stem cells, their involvement in metastasis and their interactions with the tumor microenvironment; we also discuss the therapeutic approaches that are being developed to target them to prevent tumor relapse.

Indexed as

Drug Resistance, NeoplasmAnimalsExosomesFemaleHumansNeoplasm MetastasisNeoplastic Stem CellsOvarian NeoplasmsTumor Microenvironmentcancer stem cellsovarian cancertherapy resistance

Identifiers

PMID32512891
PMCPMC7349391

What OpenQuestion holds

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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.