Evidence map›Paper›PMID 33929972›Full record

ArticleAging2021

Development and validation of epithelial mesenchymal transition-related prognostic model for hepatocellular carcinoma.

Xuequan Wang, Ziming Xing, Huihui Xu, Haihua Yang, Tongjing Xing

Abstract readValidation Study
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 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
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

10 citing papers in PubMed.

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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.

Xuequan WangPublic Research Platform, Department of Radiation Oncology, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Linhai 317000, Zhejiang Province, China.
Ziming XingSchool of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China.
Huihui XuCentral Laboratory, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Linhai 317000, Zhejiang Province, China.
Haihua YangDepartment of Radiation Oncology, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Linhai 317000, Zhejiang Province, China.
Tongjing XingDepartment of Infectious Disease, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Linhai 317000, Zhejiang Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Epithelial cell transformation (EMT) plays an important role in the pathogenesis and metastasis of hepatocellular carcinoma (HCC). We aimed to establish a genetic risk model to evaluate HCC prognosis based on the expression levels of EMT-related genes. The data of HCC patients were collected from TCGA and ICGC databases. Gene expression differential analysis, univariate analysis, and lasso combined with stepwise Cox regression were used to construct the prognostic model. Kaplan-Meier curve, receiver operating characteristic (ROC) curve, calibration analysis, Harrell's concordance index (C-index), and decision curve analysis (DCA) were used to evaluate the predictive ability of the risk model or nomogram. GO and KEGG were used to analyze differently expressed EMT genes, or genes that directly or indirectly interact with the risk-associated genes. A 10-gene signature, including

Indexed as

Epithelial-Mesenchymal TransitionCarcinoma, HepatocellularCell Line, TumorCohort StudiesDatabases, GeneticDNA MethylationGene Expression ProfilingGene Expression Regulation, NeoplasticGenetic VariationHumansKaplan-Meier EstimateLiver NeoplasmsLymphocytes, Tumor-InfiltratingMultivariate AnalysisNomogramsPrognosisepithelial cell transformationhepatocellular carcinomaprognosis

Identifiers

PMID33929972
PMCPMC8202896

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.