Evidence map›Paper›PMID 32249490›Full record

ReviewCell proliferation2020

Systematic characterization of non-coding RNAs in triple-negative breast cancer.

Jie Mei, Leiyu Hao, Huiyu Wang, Rui Xu, Yan Liu, Yichao Zhu, Chaoying Liu

Open access · goldAbstract readReview
In one paragraph

Review in Cell proliferation, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed
4.5field-weighted citation impact, top 3% 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

38 citing papers in PubMed, 61 citations in OpenAlex.

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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

7 authors at 2 institutions in 1 country.

Jie MeiDepartment of Oncology, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China.ORCID https://orcid.org/0000-0001-7799-8129
Leiyu HaoDepartment of Physiology, Nanjing Medical University, Nanjing, China.
Huiyu WangDepartment of Oncology, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China.
Rui XuDepartment of Physiology, Nanjing Medical University, Nanjing, China.
Yan LiuDepartment of Physiology, Nanjing Medical University, Nanjing, China.
Yichao ZhuDepartment of Physiology, Nanjing Medical University, Nanjing, China.
Chaoying LiuDepartment of Oncology, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China.ORCID https://orcid.org/0000-0003-0484-9522
Nanjing Medical University · CNWuxi People's Hospital · CN

Funding

Foundation of Wuxi Health Commission QNRC003Natural Science Foundation of Jiangsu Province of China BE2017626Natural Science Foundation of Jiangsu Province of China BK20181367
6 · The paper itself

Abstract

Triple-negative breast cancer (TNBC) is one of the most aggressive subtypes of breast cancer with negativity for oestrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor (HER2). Non-coding RNAs (ncRNAs) make up most of the transcriptome and are widely present in eukaryotic cells. In recent years, emerging evidence suggests that ncRNAs, mainly microRNAs (miRNAs), long ncRNAs (lncRNAs) and circular RNAs (circRNAs), play prominent roles in the tumorigenesis and development of TNBC, but the functions of most ncRNAs have not been fully described. In this review, we systematically elucidate the general characteristics and biogenesis of miRNAs, lncRNAs and circRNAs, discuss the emerging functions of these ncRNAs in TNBC and present future perspectives in clinical practice.

Indexed as

AnimalsCarcinogenesisFemaleHumansMicroRNAsRNA, CircularRNA, Long NoncodingTriple Negative Breast NeoplasmsMicroRNAsRNA, CircularRNA, Long Noncoding

Identifiers

PMID32249490
PMCPMC7260065
OpenAlexW3014367814

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.