Evidence map›Paper›PMID 33534780›Full record

ArticleAging2021

Prognostic value of an autophagy-related long-noncoding-RNA signature for endometrial cancer.

Xiufang Wang, Chenyang Dai, Minqing Ye, Jingyun Wang, Weizhao Lin, Ruiman Li

Open access · greenAbstract read
In one paragraph

Article in Aging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
3.8field-weighted citation impact, top 5% 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

20 citing papers in PubMed, 40 citations in OpenAlex.

  1. Article
  2. SCENE: Signature Collection for Endometrial Cancer Prognosis.Journal of cellular and molecular medicine · 2025
    Review
  3. Article
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  6. Copper homeostasis and cuproptosis in gynecological cancers.Frontiers in cell and developmental biology · 2024
    Review
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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

6 authors at 2 institutions in 1 country.

Xiufang WangDepartment of Obstetrics and Gynecology, The First Affiliated Hospital of Jinan University, Guangzhou 510632, China.
Chenyang DaiDepartment of Obstetrics and Gynecology, The First Affiliated Hospital of Jinan University, Guangzhou 510632, China.
Minqing YeDepartment of Obstetrics and Gynecology, Foshan Women and Children Hospital, Foshan 528000, China.
Jingyun WangDepartment of Obstetrics and Gynecology, The First Affiliated Hospital of Jinan University, Guangzhou 510632, China.
Weizhao LinDepartment of Obstetrics and Gynecology, The First Affiliated Hospital of Jinan University, Guangzhou 510632, China.
Ruiman LiDepartment of Obstetrics and Gynecology, The First Affiliated Hospital of Jinan University, Guangzhou 510632, China.
First Affiliated Hospital of Jinan University · CNFoshan Maternity and Child Health Care Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study retrieved the transcriptome profiling data of 552 endometrial cancer (EC) patients from the TCGA (The Cancer Genome Atlas) database, and identified 1297 lncRNAs (long noncoding RNAs) related to autophagy genes using Pearson correlation analysis. Univariate Cox regression analysis of the training data set revealed that 14 autophagy-related lncRNAs had significantly prognostic value for endometrial cancer (

Indexed as

AutophagyCarcinoma, EndometrioidDatabases, GeneticEndometrial NeoplasmsFemaleHumansKaplan-Meier EstimateMiddle AgedMultivariate AnalysisNeoplasm GradingNeoplasms, Cystic, Mucinous, and SerousNeoplasm StagingPrognosisProportional Hazards ModelsRNA, Long NoncodingSurvival RateRNA, Long Noncodingautophagyendometrial cancerlong-noncoding-RNAprognostic signatureTCGA

Identifiers

PMID33534780
PMCPMC7950257
OpenAlexW3126168746

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.