Evidence map›Paper›PMID 37218992›Full record

ArticleNon-coding RNA2023

Inverse Impact of Cancer Drugs on Circular and Linear RNAs in Breast Cancer Cell Lines.

Anna Terrazzan, Francesca Crudele, Fabio Corrà, Pietro Ancona, Jeffrey Palatini, Nicoletta Bianchi, Stefano Volinia

Open access · goldAbstract read
In one paragraph

Article in Non-coding RNA, 2023. 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
0.3field-weighted citation impact, top 37% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Review
  2. 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

7 authors at 3 institutions in 2 countries.

Anna TerrazzanDepartment of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy.ORCID 0000-0003-2658-3382
Francesca CrudeleGenetics Unit, Institute for Maternal and Child Health, Scientific Institute for Research, Hospitalization and Healthcare (IRCCS) Burlo Garofolo, 34137 Trieste, Italy.ORCID 0000-0003-4638-9122
Fabio CorràDepartment of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy.
Pietro AnconaDepartment of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy.ORCID 0009-0001-8881-1128
Jeffrey PalatiniGenomics Core Facility, Centre of New Technologies, University of Warsaw, 02-097 Warsaw, Poland.
Nicoletta BianchiDepartment of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy.ORCID 0000-0001-9280-6017
Stefano VoliniaDepartment of Translational Medicine, University of Ferrara, 44121 Ferrara, Italy.
University of Ferrara · ITIRCCS Materno Infantile Burlo Garofolo · ITUniversity of Warsaw · PL

Funding

FAR2021 FAR2120348FIR2021 FIR2120538
6 · The paper itself

Abstract

Altered expression of circular RNAs (circRNAs) has previously been investigated in breast cancer. However, little is known about the effects of drugs on their regulation and relationship with the cognate linear transcript (linRNA). We analyzed the dysregulation of both 12 cancer-related circRNAs and their linRNAs in two breast cancer cell lines undergoing various treatments. We selected 14 well-known anticancer agents affecting different cellular pathways and examined their impact. Upon drug exposure circRNA/linRNA expression ratios increased, as a result of the downregulation of linRNA and upregulation of circRNA within the same gene. In this study, we highlighted the relevance of identifying the drug-regulated circ/linRNAs according to their oncogenic or anticancer role. Interestingly,

Indexed as

breast cancercircular RNAdrugs

Identifiers

PMID37218992
PMCPMC10204552
OpenAlexW4377115317

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.