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Identification of a glycolysis-related gene signature for predicting prognosis in patients with hepatocellular carcinoma

BACKGROUND: Hepatocellular carcinoma (HCC) is the most common primary liver cancer in the world. Although great advances in HCC diagnosis and treatment have been achieved, due to the complicated mechanisms in tumor development and progression, the prognosis of HCC is still dismal. Recent studies hav...

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Autores principales: Kong, Junjie, Yu, Guangsheng, Si, Wei, Li, Guangbing, Chai, Jiawei, Liu, Yong, Liu, Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8817563/
https://www.ncbi.nlm.nih.gov/pubmed/35123420
http://dx.doi.org/10.1186/s12885-022-09209-9
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author Kong, Junjie
Yu, Guangsheng
Si, Wei
Li, Guangbing
Chai, Jiawei
Liu, Yong
Liu, Jun
author_facet Kong, Junjie
Yu, Guangsheng
Si, Wei
Li, Guangbing
Chai, Jiawei
Liu, Yong
Liu, Jun
author_sort Kong, Junjie
collection PubMed
description BACKGROUND: Hepatocellular carcinoma (HCC) is the most common primary liver cancer in the world. Although great advances in HCC diagnosis and treatment have been achieved, due to the complicated mechanisms in tumor development and progression, the prognosis of HCC is still dismal. Recent studies have revealed that the Warburg effect is related to the development, progression and treatment of various cancers; however, there have been a few explorations of the relationship between glycolysis and HCC prognosis. METHODS: mRNA expression profiling was downloaded from public databases. Gene set enrichment analysis (GSEA) was used to explore glycolysis-related genes (GRGs), and the LASSO method and Cox regression analysis were used to identify GRGs related to HCC prognosis and to construct predictive models associated with overall survival (OS) and disease-free survival (DFS). The relationship between the predictive model and the tumor mutation burden (TMB) and tumor immune microenvironment (TIME) was explored. Finally, real-time PCR was used to validate the expression levels of the GRGs in clinical samples and different cell lines. RESULTS: Five GRGs (ABCB6, ANKZF1, B3GAT3, KIF20A and STC2) were identified and used to construct gene signatures to predict HCC OS and DFS. Using the median value, HCC patients were divided into low- and high-risk groups. Patients in the high-risk group had worse OS/DFS than those in the low-risk group, were related to higher TMB and were associated with a higher rate of CD4+ memory T cells resting and CD4+ memory T cells activated. Finally, real-time PCR suggested that the five GRGs were all dysregulated in HCC samples compared to adjacent normal samples. CONCLUSIONS: We identified five GRGs associated with HCC prognosis and constructed two GRGs-related gene signatures to predict HCC OS and DFS. The findings in this study may contribute to the prediction of prognosis and promote HCC treatment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-022-09209-9.
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spelling pubmed-88175632022-02-07 Identification of a glycolysis-related gene signature for predicting prognosis in patients with hepatocellular carcinoma Kong, Junjie Yu, Guangsheng Si, Wei Li, Guangbing Chai, Jiawei Liu, Yong Liu, Jun BMC Cancer Research BACKGROUND: Hepatocellular carcinoma (HCC) is the most common primary liver cancer in the world. Although great advances in HCC diagnosis and treatment have been achieved, due to the complicated mechanisms in tumor development and progression, the prognosis of HCC is still dismal. Recent studies have revealed that the Warburg effect is related to the development, progression and treatment of various cancers; however, there have been a few explorations of the relationship between glycolysis and HCC prognosis. METHODS: mRNA expression profiling was downloaded from public databases. Gene set enrichment analysis (GSEA) was used to explore glycolysis-related genes (GRGs), and the LASSO method and Cox regression analysis were used to identify GRGs related to HCC prognosis and to construct predictive models associated with overall survival (OS) and disease-free survival (DFS). The relationship between the predictive model and the tumor mutation burden (TMB) and tumor immune microenvironment (TIME) was explored. Finally, real-time PCR was used to validate the expression levels of the GRGs in clinical samples and different cell lines. RESULTS: Five GRGs (ABCB6, ANKZF1, B3GAT3, KIF20A and STC2) were identified and used to construct gene signatures to predict HCC OS and DFS. Using the median value, HCC patients were divided into low- and high-risk groups. Patients in the high-risk group had worse OS/DFS than those in the low-risk group, were related to higher TMB and were associated with a higher rate of CD4+ memory T cells resting and CD4+ memory T cells activated. Finally, real-time PCR suggested that the five GRGs were all dysregulated in HCC samples compared to adjacent normal samples. CONCLUSIONS: We identified five GRGs associated with HCC prognosis and constructed two GRGs-related gene signatures to predict HCC OS and DFS. The findings in this study may contribute to the prediction of prognosis and promote HCC treatment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-022-09209-9. BioMed Central 2022-02-05 /pmc/articles/PMC8817563/ /pubmed/35123420 http://dx.doi.org/10.1186/s12885-022-09209-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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
Kong, Junjie
Yu, Guangsheng
Si, Wei
Li, Guangbing
Chai, Jiawei
Liu, Yong
Liu, Jun
Identification of a glycolysis-related gene signature for predicting prognosis in patients with hepatocellular carcinoma
title Identification of a glycolysis-related gene signature for predicting prognosis in patients with hepatocellular carcinoma
title_full Identification of a glycolysis-related gene signature for predicting prognosis in patients with hepatocellular carcinoma
title_fullStr Identification of a glycolysis-related gene signature for predicting prognosis in patients with hepatocellular carcinoma
title_full_unstemmed Identification of a glycolysis-related gene signature for predicting prognosis in patients with hepatocellular carcinoma
title_short Identification of a glycolysis-related gene signature for predicting prognosis in patients with hepatocellular carcinoma
title_sort identification of a glycolysis-related gene signature for predicting prognosis in patients with hepatocellular carcinoma
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8817563/
https://www.ncbi.nlm.nih.gov/pubmed/35123420
http://dx.doi.org/10.1186/s12885-022-09209-9
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