Evidence map›Paper›PMID 34367239›Full record

ArticleFrontiers in genetics2021

Four Prognosis-Associated lncRNAs Serve as Biomarkers in Ovarian Cancer.

Jianfeng Zheng, Jialu Guo, Huizhi Zhang, Benben Cao, Guomin Xu, Zhifen Zhang, Jinyi Tong

Open access · goldAbstract read
In one paragraph

Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
1.7field-weighted citation impact, top 16% 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

12 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.

  1. Pooled it
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  12. Long Intergenic Noncoding RNAInternational journal of molecular sciences · 2021
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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 3 institutions in 1 country.

Jianfeng ZhengDepartment of Obstetrics and Gynecology, Affiliated Hangzhou Hospital, Nanjing Medical University, Hangzhou, China.
Jialu GuoDepartment of Fourth Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China.
Huizhi ZhangDepartment of Fourth Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China.
Benben CaoDepartment of Fourth Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China.
Guomin XuDepartment of Obstetrics and Gynecology, Haining Second People's Hospital, Haining, China.
Zhifen ZhangDepartment of Obstetrics and Gynecology, Hangzhou Women's Hospital, Hangzhou, China.
Jinyi TongDepartment of Obstetrics and Gynecology, Affiliated Hangzhou Hospital, Nanjing Medical University, Hangzhou, China.
Hangzhou Women’s Hospital · CNZhejiang Chinese Medical University · CNXian Yang Central Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Long non-coding RNAs (lncRNAs) play crucial roles in ovarian cancer (OC) development. However, prognosis-associated lncRNAs (PALs) for OC have not been completely elucidated. Our study aimed to identify the PAL signature of OC. A total of 663 differentially expressed lncRNAs were identified in the databases. According to the weighted gene coexpression analysis, the highly correlated genes were clustered into seven modules related to the clinical phenotype of OC. A total of 25 lncRNAs that were significantly related to overall survival were screened based on univariate Cox regression analysis. The prognostic risk model constructed contained seven PALs based on the parameter λ

Indexed as

cell function assayslncRNAovarian cancerrisk score modelweighted gene coexpression analysis

Identifiers

PMID34367239
PMCPMC8336869
OpenAlexW3185672310

What OpenQuestion holds

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LicenceCC BY
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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.