Evidence map›Paper›PMID 37569629›Full record

ReviewInternational journal of molecular sciences2023

Long Noncoding RNAs in Taxane Resistance of Breast Cancer.

Hailong Chen, Mengwen Zhang, Yongchuan Deng

Open access · goldAbstract readReview
In one paragraph

Review in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.6field-weighted citation impact, top 10% 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

7 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
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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

3 authors at 1 institution in 1 country.

Hailong ChenDepartment of Breast Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China.
Mengwen ZhangDepartment of Plastic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China.
Yongchuan DengDepartment of Breast Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China.
Second Affiliated Hospital of Zhejiang University · CN

Funding

Zhejiang Provincial Natural Science Foundation LQ20H160031Zhejiang Provincial Natural Science Foundation LQ20H160051
6 · The paper itself

Abstract

Breast cancer is a common cancer in women and a leading cause of mortality. With the early diagnosis and development of therapeutic drugs, the prognosis of breast cancer has markedly improved. Chemotherapy is one of the predominant strategies for the treatment of breast cancer. Taxanes, including paclitaxel and docetaxel, are widely used in the treatment of breast cancer and remarkably decrease the risk of death and recurrence. However, taxane resistance caused by multiple factors significantly impacts the effect of the drug and leads to poor prognosis. Long noncoding RNAs (lncRNAs) have been shown to play a significant role in critical cellular processes, and a number of studies have illustrated that lncRNAs play vital roles in taxane resistance. In this review, we systematically summarize the mechanisms of taxane resistance in breast cancer and the functions of lncRNAs in taxane resistance in breast cancer. The findings provide insight into the role of lncRNAs in taxane resistance and suggest that lncRNAs may be used to develop therapeutic targets to prevent or reverse taxane resistance in patients with breast cancer.

Indexed as

Breast NeoplasmsRNA, Long NoncodingBridged-Ring CompoundsFemaleHumansPaclitaxelTaxoidsBridged-Ring CompoundsPaclitaxelRNA, Long NoncodingtaxaneTaxoidsbreast cancerlong noncoding RNAstaxane resistance

Identifiers

PMID37569629
PMCPMC10418730
OpenAlexW4385428967

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.