Evidence map›Paper›PMID 34295810›Full record

ReviewFrontiers in oncology2021

Circular RNAs in Lung Cancer: Recent Advances and Future Perspectives.

Huan-Huan Chen, Tie-Ning Zhang, Qi-Jun Wu, Xin-Mei Huang, Yu-Hong Zhao

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
4.5field-weighted citation impact, top 4% 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

23 citing papers in PubMed, 54 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

5 authors at 2 institutions in 1 country.

Huan-Huan ChenDepartment of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China.
Tie-Ning ZhangDepartment of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China.
Qi-Jun WuDepartment of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China.
Xin-Mei HuangDepartment of Endocrinology, Shanghai Fifth People's Hospital, Fudan University, Shanghai, China.
Yu-Hong ZhaoDepartment of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China.
China Medical University · CNFudan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Globally, lung cancer is the most commonly diagnosed cancer and carries with it the greatest mortality rate, with 5-year survival rates varying from 4-17% depending on stage and geographical differences. For decades, researchers have studied disease mechanisms, occurrence rates and disease development, however, the mechanisms underlying disease progression are not yet fully elucidated, thus an increased understanding of disease pathogenesis is key to developing new strategies towards specific disease diagnoses and targeted treatments. Circular RNAs (circRNAs) are a class of non-coding RNA widely expressed in eukaryotic cells, and participate in various biological processes implicated in human disease. Recent studies have indicated that circRNAs both positively and negatively regulate lung cancer cell proliferation, migration, invasion and apoptosis. Additionally, circRNAs could be promising biomarkers and targets for lung cancer therapies. This review systematically highlights recent advances in circRNA regulatory roles in lung cancer, and sheds light on their use as potential biomarkers and treatment targets for this disease.

Indexed as

biomarkercircular RNAdrug-resistancelung cancermechanismsradioresistance

Identifiers

PMID34295810
PMCPMC8290158
OpenAlexW3178050496

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.