Evidence map›Paper›PMID 34944852›Full record

ArticleCancers2021

The Antiviral Drug Efavirenz in Breast Cancer Stem Cell Therapy.

Pey-Tsyr Chiou, Stephen Ohms, Philip G Board, Jane E Dahlstrom, Danny Rangasamy, Marco G Casarotto

Open access · goldAbstract read
In one paragraph

Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.3field-weighted citation impact, top 40% 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

4 citing papers in PubMed, 4 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. 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

6 authors at 1 institution in 1 country.

Pey-Tsyr ChiouThe John Curtin School of Medical Research, The Australian National University, Canberra, ACT 2600, Australia.
Stephen OhmsThe John Curtin School of Medical Research, The Australian National University, Canberra, ACT 2600, Australia.
Philip G BoardThe John Curtin School of Medical Research, The Australian National University, Canberra, ACT 2600, Australia.
Jane E DahlstromAnatomical Pathology, ACT Pathology, Canberra Hospital, Canberra, ACT 2600, Australia.ORCID 0000-0001-5608-693X
Danny RangasamyThe John Curtin School of Medical Research, The Australian National University, Canberra, ACT 2600, Australia.
Marco G CasarottoThe John Curtin School of Medical Research, The Australian National University, Canberra, ACT 2600, Australia.
Australian National University · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although many breast cancer therapies show initial success in the treatment of the primary tumour, they often fail to eliminate a sub-population of cells known as cancer stem cells (CSCs). These cells are recognised for their self-renewal properties and for their capacity for differentiation often leading to chemo/radio-resistance. The antiviral drug Efavirenz has been shown to be effective in eliminating triple-negative breast cancer cells, and here we examine its effect on breast CSCs. The effects of Efavirenz on CSCs for several breast cancer cell lines were investigated by examining cellular changes upon drug treatment, including CSC numbers, morphology, RNA/microRNA expression and levels of epithelial/mesenchymal CSC subtypes. Efavirenz treatment resulted in a decrease in the size and number of tumorspheres and a reduction in epithelial-type CSC levels, but an increase in mesenchymal-type CSCs. Efavirenz caused upregulation of several CSC-related genes as well as

Indexed as

breast cancercancer stem cellsEfavirenzgenestreatment

Identifiers

PMID34944852
PMCPMC8699628
OpenAlexW4200426227

What OpenQuestion 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 OpenQuestion graph.