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Identification and validation of a prognostic index based on a metabolic-genomic landscape analysis of ovarian cancer

Purpose: Tumour metabolism has become a novel factor targeted by personalised cancer drugs. This research evaluated the prognostic significance of metabolism-related genes (MRGs) in ovarian serous cystadenocarcinoma (OSC). Methods: MRGs in 379 women surviving OSC were obtained using The Cancer Genom...

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Autores principales: Zhang, Qun-feng, Li, Yu-kun, Chen, Chang-ye, Zhang, Xiao-di, Cao, Lu, Quan, Fei-fei, Zeng, Xin, Wang, Juan, Liu, Jue
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Portland Press Ltd. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7527429/
https://www.ncbi.nlm.nih.gov/pubmed/32880385
http://dx.doi.org/10.1042/BSR20201937
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author Zhang, Qun-feng
Li, Yu-kun
Chen, Chang-ye
Zhang, Xiao-di
Cao, Lu
Quan, Fei-fei
Zeng, Xin
Wang, Juan
Liu, Jue
author_facet Zhang, Qun-feng
Li, Yu-kun
Chen, Chang-ye
Zhang, Xiao-di
Cao, Lu
Quan, Fei-fei
Zeng, Xin
Wang, Juan
Liu, Jue
author_sort Zhang, Qun-feng
collection PubMed
description Purpose: Tumour metabolism has become a novel factor targeted by personalised cancer drugs. This research evaluated the prognostic significance of metabolism-related genes (MRGs) in ovarian serous cystadenocarcinoma (OSC). Methods: MRGs in 379 women surviving OSC were obtained using The Cancer Genome Atlas (TCGA) database. Then, several biomedical computational algorithms were employed to identify eight hub prognostic MRGs that were significantly relevant to OSC survival. These eight genes have important clinical significance and prognostic value in OSC. Subsequently, a prognostic index was constructed. Drug sensitivity analysis was used to screen the key genes in eight MRGs. Immunohistochemistry (IHC) staining confirmed the expression levels of key genes and their correlations with clinical parameters and prognosis for patients. Results: A total of 701 differentially expressed MRGs were confirmed in women with OSC by the TCGA database. The random walking with restart (RWR) algorithm and the univariate Cox and lasso regression analyses indicated a prognostic signature based on eight MRGs (i.e., ENPP1, FH, CYP2E1, HPGDS, ADCY9, NDUFA5, ADH1B and PYGB), which performed moderately well in prognostic predictions. Drug sensitivity analysis indicated that PYGB played a key role in the progression of OSC. Also, IHC staining confirmed that PYGB has a close correlation with clinical parameters and poor prognosis in OSC. Conclusion: The results of the present study may help to establish a foundation for future research attempting to predict the prognosis of OSC patients and to characterise OSC metabolism.
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spelling pubmed-75274292020-10-06 Identification and validation of a prognostic index based on a metabolic-genomic landscape analysis of ovarian cancer Zhang, Qun-feng Li, Yu-kun Chen, Chang-ye Zhang, Xiao-di Cao, Lu Quan, Fei-fei Zeng, Xin Wang, Juan Liu, Jue Biosci Rep Cancer Purpose: Tumour metabolism has become a novel factor targeted by personalised cancer drugs. This research evaluated the prognostic significance of metabolism-related genes (MRGs) in ovarian serous cystadenocarcinoma (OSC). Methods: MRGs in 379 women surviving OSC were obtained using The Cancer Genome Atlas (TCGA) database. Then, several biomedical computational algorithms were employed to identify eight hub prognostic MRGs that were significantly relevant to OSC survival. These eight genes have important clinical significance and prognostic value in OSC. Subsequently, a prognostic index was constructed. Drug sensitivity analysis was used to screen the key genes in eight MRGs. Immunohistochemistry (IHC) staining confirmed the expression levels of key genes and their correlations with clinical parameters and prognosis for patients. Results: A total of 701 differentially expressed MRGs were confirmed in women with OSC by the TCGA database. The random walking with restart (RWR) algorithm and the univariate Cox and lasso regression analyses indicated a prognostic signature based on eight MRGs (i.e., ENPP1, FH, CYP2E1, HPGDS, ADCY9, NDUFA5, ADH1B and PYGB), which performed moderately well in prognostic predictions. Drug sensitivity analysis indicated that PYGB played a key role in the progression of OSC. Also, IHC staining confirmed that PYGB has a close correlation with clinical parameters and poor prognosis in OSC. Conclusion: The results of the present study may help to establish a foundation for future research attempting to predict the prognosis of OSC patients and to characterise OSC metabolism. Portland Press Ltd. 2020-09-28 /pmc/articles/PMC7527429/ /pubmed/32880385 http://dx.doi.org/10.1042/BSR20201937 Text en © 2020 The Author(s). https://creativecommons.org/licenses/by/4.0/ This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY).
spellingShingle Cancer
Zhang, Qun-feng
Li, Yu-kun
Chen, Chang-ye
Zhang, Xiao-di
Cao, Lu
Quan, Fei-fei
Zeng, Xin
Wang, Juan
Liu, Jue
Identification and validation of a prognostic index based on a metabolic-genomic landscape analysis of ovarian cancer
title Identification and validation of a prognostic index based on a metabolic-genomic landscape analysis of ovarian cancer
title_full Identification and validation of a prognostic index based on a metabolic-genomic landscape analysis of ovarian cancer
title_fullStr Identification and validation of a prognostic index based on a metabolic-genomic landscape analysis of ovarian cancer
title_full_unstemmed Identification and validation of a prognostic index based on a metabolic-genomic landscape analysis of ovarian cancer
title_short Identification and validation of a prognostic index based on a metabolic-genomic landscape analysis of ovarian cancer
title_sort identification and validation of a prognostic index based on a metabolic-genomic landscape analysis of ovarian cancer
topic Cancer
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7527429/
https://www.ncbi.nlm.nih.gov/pubmed/32880385
http://dx.doi.org/10.1042/BSR20201937
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