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A Novel Risk Factor Model Based on Glycolysis-Associated Genes for Predicting the Prognosis of Patients With Prostate Cancer
BACKGROUND: Prostate cancer (PCa) is one of the most prevalent cancers among males, and its mortality rate is increasing due to biochemical recurrence (BCR). Glycolysis has been proven to play an important regulatory role in tumorigenesis. Although several key regulators or predictors involved in PC...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8476926/ https://www.ncbi.nlm.nih.gov/pubmed/34595101 http://dx.doi.org/10.3389/fonc.2021.605810 |
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author | Guo, Kaixuan Lai, Cong Shi, Juanyi Tang, Zhuang Liu, Cheng Li, Kuiqing Xu, Kewei |
author_facet | Guo, Kaixuan Lai, Cong Shi, Juanyi Tang, Zhuang Liu, Cheng Li, Kuiqing Xu, Kewei |
author_sort | Guo, Kaixuan |
collection | PubMed |
description | BACKGROUND: Prostate cancer (PCa) is one of the most prevalent cancers among males, and its mortality rate is increasing due to biochemical recurrence (BCR). Glycolysis has been proven to play an important regulatory role in tumorigenesis. Although several key regulators or predictors involved in PCa progression have been found, the relationship between glycolysis and PCa is unclear; we aimed to develop a novel glycolysis-associated multifactor prediction model for better predicting the prognosis of PCa. METHODS: Differential mRNA expression profiles derived from the Cancer Genome Atlas (TCGA) PCa cohort were generated through the “edgeR” package. Glycolysis-related genes were obtained from the GSEA database. Univariate Cox and LASSO regression analyses were used to identify genes significantly associated with disease-free survival. ROC curves were applied to evaluate the predictive value of the model. An external dataset derived from Gene Expression Omnibus (GEO) was used to verify the predictive ability. Glucose consumption and lactic production assays were used to assess changes in metabolic capacity, and Transwell assays were used to assess the invasion and migration of PC3 cells. RESULTS: Five glycolysis-related genes were applied to construct a risk score prediction model. Patients with PCa derived from TCGA and GEO (GSE70770) were divided into high-risk and low-risk groups according to the median. In the TCGA cohort, the high-risk group had a poorer prognosis than the low-risk group, and the results were further verified in the GSE70770 cohort. In vitro experiments demonstrated that knocking down HMMR, KIF20A, PGM2L1, and ANKZF1 separately led to less glucose consumption, less lactic production, and inhibition of cell migration and invasion, and the results were the opposite with GPR87 knockdown. CONCLUSION: The risk score based on five glycolysis-related genes may serve as an accurate prognostic marker for PCa patients with BCR. |
format | Online Article Text |
id | pubmed-8476926 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84769262021-09-29 A Novel Risk Factor Model Based on Glycolysis-Associated Genes for Predicting the Prognosis of Patients With Prostate Cancer Guo, Kaixuan Lai, Cong Shi, Juanyi Tang, Zhuang Liu, Cheng Li, Kuiqing Xu, Kewei Front Oncol Oncology BACKGROUND: Prostate cancer (PCa) is one of the most prevalent cancers among males, and its mortality rate is increasing due to biochemical recurrence (BCR). Glycolysis has been proven to play an important regulatory role in tumorigenesis. Although several key regulators or predictors involved in PCa progression have been found, the relationship between glycolysis and PCa is unclear; we aimed to develop a novel glycolysis-associated multifactor prediction model for better predicting the prognosis of PCa. METHODS: Differential mRNA expression profiles derived from the Cancer Genome Atlas (TCGA) PCa cohort were generated through the “edgeR” package. Glycolysis-related genes were obtained from the GSEA database. Univariate Cox and LASSO regression analyses were used to identify genes significantly associated with disease-free survival. ROC curves were applied to evaluate the predictive value of the model. An external dataset derived from Gene Expression Omnibus (GEO) was used to verify the predictive ability. Glucose consumption and lactic production assays were used to assess changes in metabolic capacity, and Transwell assays were used to assess the invasion and migration of PC3 cells. RESULTS: Five glycolysis-related genes were applied to construct a risk score prediction model. Patients with PCa derived from TCGA and GEO (GSE70770) were divided into high-risk and low-risk groups according to the median. In the TCGA cohort, the high-risk group had a poorer prognosis than the low-risk group, and the results were further verified in the GSE70770 cohort. In vitro experiments demonstrated that knocking down HMMR, KIF20A, PGM2L1, and ANKZF1 separately led to less glucose consumption, less lactic production, and inhibition of cell migration and invasion, and the results were the opposite with GPR87 knockdown. CONCLUSION: The risk score based on five glycolysis-related genes may serve as an accurate prognostic marker for PCa patients with BCR. Frontiers Media S.A. 2021-09-14 /pmc/articles/PMC8476926/ /pubmed/34595101 http://dx.doi.org/10.3389/fonc.2021.605810 Text en Copyright © 2021 Guo, Lai, Shi, Tang, Liu, Li and Xu https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Guo, Kaixuan Lai, Cong Shi, Juanyi Tang, Zhuang Liu, Cheng Li, Kuiqing Xu, Kewei A Novel Risk Factor Model Based on Glycolysis-Associated Genes for Predicting the Prognosis of Patients With Prostate Cancer |
title | A Novel Risk Factor Model Based on Glycolysis-Associated Genes for Predicting the Prognosis of Patients With Prostate Cancer |
title_full | A Novel Risk Factor Model Based on Glycolysis-Associated Genes for Predicting the Prognosis of Patients With Prostate Cancer |
title_fullStr | A Novel Risk Factor Model Based on Glycolysis-Associated Genes for Predicting the Prognosis of Patients With Prostate Cancer |
title_full_unstemmed | A Novel Risk Factor Model Based on Glycolysis-Associated Genes for Predicting the Prognosis of Patients With Prostate Cancer |
title_short | A Novel Risk Factor Model Based on Glycolysis-Associated Genes for Predicting the Prognosis of Patients With Prostate Cancer |
title_sort | novel risk factor model based on glycolysis-associated genes for predicting the prognosis of patients with prostate cancer |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8476926/ https://www.ncbi.nlm.nih.gov/pubmed/34595101 http://dx.doi.org/10.3389/fonc.2021.605810 |
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