Evidence map›Paper›PMID 34281542›Full record

ArticleWorld journal of surgical oncology2021

A novel prognostic model based on epithelial-mesenchymal transition-related genes predicts patient survival in gastric cancer.

Wanting Song, Yi Bai, Jialin Zhu, Fanxin Zeng, Chunmeng Yang, Beibei Hu, Mingjun Sun, Chenyan Li, Shiqiao Peng, Moye Chen and 1 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in World journal of surgical oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.2field-weighted citation impact, top 11% 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

7 citing papers in PubMed, 25 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

11 authors at 1 institution in 1 country.

Wanting SongDepartment of Gastroenterology, First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China.
Yi BaiDepartment of Gastroenterology, First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China.
Jialin ZhuDepartment of Gastroenterology, First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China.
Fanxin ZengDepartment of Gastroenterology, First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China.
Chunmeng YangDepartment of Gastroenterology, First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China.
Beibei HuDepartment of Gastroenterology, First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China.
Mingjun SunDepartment of Gastroenterology, First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China.
Chenyan LiDepartment of Endocrinology and Metabolism, First Hospital of China Medical University, Shenyang, Liaoning, China.
Shiqiao PengDepartment of Endocrinology and Metabolism, First Hospital of China Medical University, Shenyang, Liaoning, China.
Moye ChenDepartment of Gastroenterology, First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China. rantong_722@163.com.
Xuren SunDepartment of Gastroenterology, First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China. sxr679@126.com.ORCID http://orcid.org/0000-0003-1435-6182
First Hospital of China Medical University · CN

Funding

Moye Chen 20180550049
6 · The paper itself

Abstract

backgroundGastric cancer (GC) represents a major malignancy and is the third deathliest cancer globally. Several lines of evidence indicate that the epithelial-mesenchymal transition (EMT) has a critical function in the development of gastric cancer. Although plentiful molecular biomarkers have been identified, a precise risk model is still necessary to help doctors determine patient prognosis in GC.

methodsGene expression data and clinical information for GC were acquired from The Cancer Genome Atlas (TCGA) database and 200 EMT-related genes (ERGs) from the Molecular Signatures Database (MSigDB). Then, ERGs correlated with patient prognosis in GC were assessed by univariable and multivariable Cox regression analyses. Next, a risk score formula was established for evaluating patient outcome in GC and validated by survival and ROC curves. In addition, Kaplan-Meier curves were generated to assess the associations of the clinicopathological data with prognosis. And a cohort from the Gene Expression Omnibus (GEO) database was used for validation.

resultsSix EMT-related genes, including CDH6, COL5A2, ITGAV, MATN3, PLOD2, and POSTN, were identified. Based on the risk model, GC patients were assigned to the high- and low-risk groups. The results revealed that the model had good performance in predicting patient prognosis in GC.

conclusionsWe constructed a prognosis risk model for GC. Then, we verified the performance of the model, which may help doctors predict patient prognosis.

Indexed as

Stomach NeoplasmsCohort StudiesEpithelial-Mesenchymal TransitionHumansPrognosisEpithelial-mesenchymal transitionGastric cancerGeneSurvival

Identifiers

PMID34281542
PMCPMC8290588
OpenAlexW3186089133

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.