Identification of novel cell glycolysis related gene signature predicting survival in patients with breast cancer

Abstract One of the most frequently identified tumors and a contributing cause of death in women is breast cancer (BC). Many biomarkers associated with survival and prognosis were identified in previous studies through database mining. Nevertheless, the predictive capabilities of single-gene biomark...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Feng Jiang, Chuyan Wu, Ming Wang, Ke Wei, Jimei Wang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0ad4fb48f4ec49b88bbf839dade30af6
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:0ad4fb48f4ec49b88bbf839dade30af6
record_format	dspace
spelling	oai:doaj.org-article:0ad4fb48f4ec49b88bbf839dade30af62021-12-02T14:21:43ZIdentification of novel cell glycolysis related gene signature predicting survival in patients with breast cancer10.1038/s41598-021-83628-92045-2322https://doaj.org/article/0ad4fb48f4ec49b88bbf839dade30af62021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83628-9https://doaj.org/toc/2045-2322Abstract One of the most frequently identified tumors and a contributing cause of death in women is breast cancer (BC). Many biomarkers associated with survival and prognosis were identified in previous studies through database mining. Nevertheless, the predictive capabilities of single-gene biomarkers are not accurate enough. Genetic signatures can be an enhanced prediction method. This research analyzed data from The Cancer Genome Atlas (TCGA) for the detection of a new genetic signature to predict BC prognosis. Profiling of mRNA expression was carried out in samples of patients with TCGA BC (n = 1222). Gene set enrichment research has been undertaken to classify gene sets that vary greatly between BC tissues and normal tissues. Cox models for additive hazards regression were used to classify genes that were strongly linked to overall survival. A subsequent Cox regression multivariate analysis was used to construct a predictive risk parameter model. Kaplan–Meier survival predictions and log-rank validation have been used to verify the value of risk prediction parameters. Seven genes (PGK1, CACNA1H, IL13RA1, SDC1, AK3, NUP43, SDC3) correlated with glycolysis were shown to be strongly linked to overall survival. Depending on the 7-gene-signature, 1222 BC patients were classified into subgroups of high/low-risk. Certain variables have not impaired the prognostic potential of the seven-gene signature. A seven-gene signature correlated with cellular glycolysis was developed to predict the survival of BC patients. The results include insight into cellular glycolysis mechanisms and the detection of patients with poor BC prognosis.Feng JiangChuyan WuMing WangKe WeiJimei WangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Feng Jiang Chuyan Wu Ming Wang Ke Wei Jimei Wang Identification of novel cell glycolysis related gene signature predicting survival in patients with breast cancer
description	Abstract One of the most frequently identified tumors and a contributing cause of death in women is breast cancer (BC). Many biomarkers associated with survival and prognosis were identified in previous studies through database mining. Nevertheless, the predictive capabilities of single-gene biomarkers are not accurate enough. Genetic signatures can be an enhanced prediction method. This research analyzed data from The Cancer Genome Atlas (TCGA) for the detection of a new genetic signature to predict BC prognosis. Profiling of mRNA expression was carried out in samples of patients with TCGA BC (n = 1222). Gene set enrichment research has been undertaken to classify gene sets that vary greatly between BC tissues and normal tissues. Cox models for additive hazards regression were used to classify genes that were strongly linked to overall survival. A subsequent Cox regression multivariate analysis was used to construct a predictive risk parameter model. Kaplan–Meier survival predictions and log-rank validation have been used to verify the value of risk prediction parameters. Seven genes (PGK1, CACNA1H, IL13RA1, SDC1, AK3, NUP43, SDC3) correlated with glycolysis were shown to be strongly linked to overall survival. Depending on the 7-gene-signature, 1222 BC patients were classified into subgroups of high/low-risk. Certain variables have not impaired the prognostic potential of the seven-gene signature. A seven-gene signature correlated with cellular glycolysis was developed to predict the survival of BC patients. The results include insight into cellular glycolysis mechanisms and the detection of patients with poor BC prognosis.
format	article
author	Feng Jiang Chuyan Wu Ming Wang Ke Wei Jimei Wang
author_facet	Feng Jiang Chuyan Wu Ming Wang Ke Wei Jimei Wang
author_sort	Feng Jiang
title	Identification of novel cell glycolysis related gene signature predicting survival in patients with breast cancer
title_short	Identification of novel cell glycolysis related gene signature predicting survival in patients with breast cancer
title_full	Identification of novel cell glycolysis related gene signature predicting survival in patients with breast cancer
title_fullStr	Identification of novel cell glycolysis related gene signature predicting survival in patients with breast cancer
title_full_unstemmed	Identification of novel cell glycolysis related gene signature predicting survival in patients with breast cancer
title_sort	identification of novel cell glycolysis related gene signature predicting survival in patients with breast cancer
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0ad4fb48f4ec49b88bbf839dade30af6
work_keys_str_mv	AT fengjiang identificationofnovelcellglycolysisrelatedgenesignaturepredictingsurvivalinpatientswithbreastcancer AT chuyanwu identificationofnovelcellglycolysisrelatedgenesignaturepredictingsurvivalinpatientswithbreastcancer AT mingwang identificationofnovelcellglycolysisrelatedgenesignaturepredictingsurvivalinpatientswithbreastcancer AT kewei identificationofnovelcellglycolysisrelatedgenesignaturepredictingsurvivalinpatientswithbreastcancer AT jimeiwang identificationofnovelcellglycolysisrelatedgenesignaturepredictingsurvivalinpatientswithbreastcancer
_version_	1718391468298600448

Identification of novel cell glycolysis related gene signature predicting survival in patients with breast cancer

Ejemplares similares