Evidence map›Paper›PMID 32782557›Full record

ArticleOncology letters2020

Identification of a five-gene signature for predicting the progression and prognosis of stage I endometrial carcinoma.

Jia Bian, Yuzi Xu, Fei Wu, Qiangwei Pan, Yunlong Liu

Open access · diamondAbstract read
In one paragraph

Article in Oncology letters, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.9field-weighted citation impact, top 20% 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

9 citing papers in PubMed, 11 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 4 institutions in 1 country.

Jia BianDepartment of Gynecology and Obstetrics, Yinzhou Hospital Affiliated to Medical School of Ningbo University, Ningbo, Zhejiang 315040, P.R. China.
Yuzi XuDepartment of Oral Implantology and Prosthodontics, The Affiliated Stomatology Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, P.R. China.
Fei WuSchool of Medicine, Anhui University of Science and Technology, Huainan, Anhui 232001, P.R. China.
Qiangwei PanDepartment of Gynecology and Obstetrics, Wenzhou People's Hospital, Wenzhou, Zhejiang 325000, P.R. China.
Yunlong LiuDepartment of Medical Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310016, P.R. China.
Zhejiang University · CNAnhui University of Science and Technology · CNNingbo University · CNWenzhou City People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Uterine corpus endometrial carcinoma (UCEC) is often diagnosed at an early clinical stage based on abnormal vaginal bleeding. However, the prognosis of UCEC is poor. The present study was conducted to identify novel tumor grade-related genes with the potential to predict the prognosis and progression of UCEC. A total of three gene expression microarray datasets were downloaded from the Gene Expression Omnibus database, and one RNA-sequencing dataset with corresponding clinical information of patients with UCEC was obtained from The Cancer Genome Atlas database. In summary, 1,447 differentially expressed genes (DEGs) were identified between endometrial cancerous tissues and normal endometrial tissues. Weighted gene co-expression network analysis was performed to assess the associations between DEGs and clinical traits. In total, five genes were found to be highly associated with the tumorigenesis and prognosis of UCEC. Among them, BUB1 mitotic checkpoint serine/threonine kinase B, cyclin B1, cell-division cycle protein 20 and non-SMC condensing I complex subunit G were involved in cell cycle regulation pathways, and DLG-associated protein 5 was involved in the Notch receptor 3 signaling pathway based on functional enrichment analyses. Of the five genes, four were highly expressed in endometrial cancerous tissues compared with normal endometrial tissues at the protein level. In addition, the higher expression of these genes predicted a higher tumor grade and worse overall survival. In conclusion, the present study revealed a 5-gene signature that can be used to predict the progression of UCEC.

Indexed as

prognosissurvival analysistumor gradeuterine corpus endometrial carcinomaweighted gene co-expression network analysis

Identifiers

PMID32782557
PMCPMC7400971
OpenAlexW3040686725

What OpenQuestion holds

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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.