Evidence map›Paper›PMID 31105744›Full record

ArticleFrontiers in genetics2019

Identification of Potential Crucial Genes Associated With the Pathogenesis and Prognosis of Endometrial Cancer.

Li Liu, Jiajing Lin, Hongying He

Open access · goldAbstract read
In one paragraph

Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
54citing papers in PubMed, 2 pooled it
4.6field-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

54 citing papers in PubMed, 2 syntheses or guidelines pooled it, 99 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
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  5. Review
  6. PHLDA2 in cancer: From molecular mechanisms to therapeutic opportunities.The Journal of pharmacology and experimental therapeutics · 2025
    Review
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  11. The oncogenic role of EIF4A3/CDC20 axis in the endometrial cancer.Journal of molecular medicine (Berlin, Germany) · 2024
    Article
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  20. Ion Channels and Personalized Medicine in Gynecological Cancers.Pharmaceuticals (Basel, Switzerland) · 2023
    Review
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.

Li LiuDepartment of Obstetrics and Gynecology, Liuzhou Worker's Hospital, Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, China.
Jiajing LinDepartment of Obstetrics and Gynecology, Liuzhou Worker's Hospital, Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, China.
Hongying HeDepartment of Obstetrics and Gynecology, Liuzhou Worker's Hospital, Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, China.
Fourth Affiliated Hospital of Guangxi Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectiveEndometrial cancer (EC) is a common gynecological malignancy worldwide. Despite advances in the development of strategies for treating EC, prognosis of the disease remains unsatisfactory, especially for advanced EC. The aim of this study was to identify novel genes that can be used as potential biomarkers for identifying the prognosis of EC and to construct a novel risk stratification using these genes. METHODS AND

resultsAn mRNA sequencing dataset, corresponding survival data and expression profiling of an array of EC patients were obtained from The Cancer Genome Atlas and Gene Expression Omnibus, respectively. Common differentially expressed genes (DEGs) were identified based on sequencing and expression as given in the profiling dataset. Pathway enrichment analysis of the DEGs was performed using the Database for Annotation, Visualization, and Integrated Discovery. The protein-protein interaction network was established using the string online database in order to identify hub genes. Univariate and multivariable Cox regression analyses were used to screen prognostic DEGs and to construct a prognostic signature. Survival analysis based on the prognostic signature was performed on TCGA EC dataset. A total of 255 common DEGs were found and 11 hub genes (TOP2A, CDK1, CCNB1, CCNB2, AURKA, PCNA, CCNA2, BIRC5, NDC80, CDC20, and BUB1BA) that may be closely related to the pathogenesis of EC were identified. A panel of 7 DEG signatures consisting of PHLDA2, GGH, ESPL1, FAM184A, KIAA1644, ESPL1, and TRPM4 were constructed. The signature performed well for prognosis prediction (

conclusionThis study identified potential genes that may be involved in the pathophysiology of EC and constructed a novel gene expression signature for EC risk stratification and prognosis prediction.

Indexed as

bioinformaticsbiomarkerendometrial cancerGEOprognosisTCGA

Identifiers

PMID31105744
PMCPMC6499025
OpenAlexW2942181999

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.