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Identification of a metabolism-related gene expression prognostic model in endometrial carcinoma patients
BACKGROUND: Metabolic 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). METHODS: We analyzed the expression of MRGs using The Cancer Genome Atlas (TCGA) data to sc...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7487491/ https://www.ncbi.nlm.nih.gov/pubmed/32894095 http://dx.doi.org/10.1186/s12885-020-07345-8 |
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author | Jiang, Pinping Sun, Wei Shen, Ningmei Huang, Xiaohao Fu, Shilong |
author_facet | Jiang, Pinping Sun, Wei Shen, Ningmei Huang, Xiaohao Fu, Shilong |
author_sort | Jiang, Pinping |
collection | PubMed |
description | BACKGROUND: Metabolic 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). METHODS: We 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. RESULTS: Forty-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. CONCLUSIONS: The MRG prognostic model and the comprehensive nomogram could guide precise outcome prediction and rational therapy selection in clinical practice. |
format | Online Article Text |
id | pubmed-7487491 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-74874912020-09-15 Identification of a metabolism-related gene expression prognostic model in endometrial carcinoma patients Jiang, Pinping Sun, Wei Shen, Ningmei Huang, Xiaohao Fu, Shilong BMC Cancer Research Article BACKGROUND: Metabolic 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). METHODS: We 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. RESULTS: Forty-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. CONCLUSIONS: The MRG prognostic model and the comprehensive nomogram could guide precise outcome prediction and rational therapy selection in clinical practice. BioMed Central 2020-09-07 /pmc/articles/PMC7487491/ /pubmed/32894095 http://dx.doi.org/10.1186/s12885-020-07345-8 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Jiang, Pinping Sun, Wei Shen, Ningmei Huang, Xiaohao Fu, Shilong Identification of a metabolism-related gene expression prognostic model in endometrial carcinoma patients |
title | Identification of a metabolism-related gene expression prognostic model in endometrial carcinoma patients |
title_full | Identification of a metabolism-related gene expression prognostic model in endometrial carcinoma patients |
title_fullStr | Identification of a metabolism-related gene expression prognostic model in endometrial carcinoma patients |
title_full_unstemmed | Identification of a metabolism-related gene expression prognostic model in endometrial carcinoma patients |
title_short | Identification of a metabolism-related gene expression prognostic model in endometrial carcinoma patients |
title_sort | identification of a metabolism-related gene expression prognostic model in endometrial carcinoma patients |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7487491/ https://www.ncbi.nlm.nih.gov/pubmed/32894095 http://dx.doi.org/10.1186/s12885-020-07345-8 |
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