Evidence map›Paper›PMID 32894095›Full record

ArticleBMC cancer2020

Identification of a metabolism-related gene expression prognostic model in endometrial carcinoma patients.

Pinping Jiang, Wei Sun, Ningmei Shen, Xiaohao Huang, Shilong Fu

Abstract read
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Article in BMC cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

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20citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

20 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.

Pinping JiangDepartment of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu Province, China.
Wei SunDepartment of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu Province, China.
Ningmei ShenDepartment of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu Province, China.
Xiaohao HuangDepartment of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu Province, China. xiaohao512006@126.com.
Shilong FuDepartment of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu Province, China. docfusl@163.com.ORCID http://orcid.org/0000-0001-7416-3070

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMetabolic abnormalities have recently been widely studied in various cancer types. This study aims to explore the expression profiles of metabolism-related genes (MRGs) in endometrial cancer (EC).

methodsWe analyzed the expression of MRGs using The Cancer Genome Atlas (TCGA) data to screen differentially expressed MRGs (DE-MRGs) significantly correlated with EC patient prognosis. Functional pathway enrichment analysis of the DE-MRGs was performed. LASSO and Cox regression analyses were performed to select MRGs closely related to EC patient outcomes. A prognostic signature was developed, and the efficacy was validated in part of and the entire TCGA EC cohort. Moreover, we developed a comprehensive nomogram including the risk model and clinical features to predict EC patients' survival probability.

resultsForty-seven DE-MRGs were significantly correlated with EC patient prognosis. Functional enrichment analysis showed that these MRGs were highly enriched in amino acid, glycolysis, and glycerophospholipid metabolism. Nine MRGs were found to be closely related to EC patient outcomes: CYP4F3, CEL, GPAT3, LYPLA2, HNMT, PHGDH, CKM, UCK2 and ACACB. Based on these nine DE-MRGs, we developed a prognostic signature, and its efficacy in part of and the entire TCGA EC cohort was validated. The nine-MRG signature was independent of other clinical features, and could effectively distinguish high- and low-risk EC patients and predict patient OS. The nomogram showed excellent consistency between the predictions and actual survival observations.

conclusionsThe MRG prognostic model and the comprehensive nomogram could guide precise outcome prediction and rational therapy selection in clinical practice.

Indexed as

PrognosisBiomarkers, TumorDatabases, GeneticEndometrial NeoplasmsFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticGlycolysisHumansNomogramsProtein Interaction MapsTranscriptomeBiomarkers, TumorEndometrial carcinomaMetabolismNomogramPrognostic modelTCGA

Identifiers

PMID32894095
PMCPMC7487491

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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.