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An anoikis-related gene signature for prediction of the prognosis in prostate cancer

PURPOSE: This study presents a novel approach to predict postoperative biochemical recurrence (BCR) in prostate cancer (PCa) patients which involves constructing a signature based on anoikis-related genes (ARGs). METHODS: In this study, we utilised data from TCGA-PARD and GEO databases to identify s...

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Autores principales: Zhao, Xiaodong, Wang, Zuheng, Tang, Zilu, Hu, Jun, Zhou, Yulin, Ge, Jingping, Dong, Jie, Xu, Song
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10469923/
https://www.ncbi.nlm.nih.gov/pubmed/37664042
http://dx.doi.org/10.3389/fonc.2023.1169425
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author Zhao, Xiaodong
Wang, Zuheng
Tang, Zilu
Hu, Jun
Zhou, Yulin
Ge, Jingping
Dong, Jie
Xu, Song
author_facet Zhao, Xiaodong
Wang, Zuheng
Tang, Zilu
Hu, Jun
Zhou, Yulin
Ge, Jingping
Dong, Jie
Xu, Song
author_sort Zhao, Xiaodong
collection PubMed
description PURPOSE: This study presents a novel approach to predict postoperative biochemical recurrence (BCR) in prostate cancer (PCa) patients which involves constructing a signature based on anoikis-related genes (ARGs). METHODS: In this study, we utilised data from TCGA-PARD and GEO databases to identify specific ARGs in prostate cancer. We established a signature of these ARGs using Cox regression analysis and evaluated their clinical predictive efficacy and immune-related status through various methods such as Kaplan-Meier survival analysis, subject work characteristics analysis, and CIBERSORT method. Our findings suggest that these ARGs may have potential as biomarkers for prostate cancer prognosis and treatment. To investigate the biological pathways of genes associated with anoikis, we utilised GSVA, GO, and KEGG. The expression of ARGs was confirmed by the HPA database. Furthermore, we conducted PPI analysis to identify the core network of ARGs in PCa. RESULTS: Based on analysis of the TCGA database, a set of eight ARGs were identified as prognostic signature genes for prostate cancer. The reliability and validity of this signature were well verified in both the TCGA and GEO codifications. Using this signature, patients were classified into two groups based on their risk for developing BCR. There was a significant difference in BCR-free time between the high and low risk groups (P < 0.05).This signature serves as a dependable and unbiased prognostic factor for predicting biochemical recurrence (BCR) in prostate cancer (PCa) patients. It outperforms clinicopathological characteristics in terms of accuracy and reliability. PLK1 may play a potential regulatory role as a core gene in the development of prostate cancer. CONCLUSION: This signature suggests the potential role of ARGs in the development and progression of PCa and can effectively predict the risk of BCR in PCa patients after surgery. It also provides a basis for further research into the mechanism of ARGs in PCa and for the clinical management of patients with PCa.
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spelling pubmed-104699232023-09-01 An anoikis-related gene signature for prediction of the prognosis in prostate cancer Zhao, Xiaodong Wang, Zuheng Tang, Zilu Hu, Jun Zhou, Yulin Ge, Jingping Dong, Jie Xu, Song Front Oncol Oncology PURPOSE: This study presents a novel approach to predict postoperative biochemical recurrence (BCR) in prostate cancer (PCa) patients which involves constructing a signature based on anoikis-related genes (ARGs). METHODS: In this study, we utilised data from TCGA-PARD and GEO databases to identify specific ARGs in prostate cancer. We established a signature of these ARGs using Cox regression analysis and evaluated their clinical predictive efficacy and immune-related status through various methods such as Kaplan-Meier survival analysis, subject work characteristics analysis, and CIBERSORT method. Our findings suggest that these ARGs may have potential as biomarkers for prostate cancer prognosis and treatment. To investigate the biological pathways of genes associated with anoikis, we utilised GSVA, GO, and KEGG. The expression of ARGs was confirmed by the HPA database. Furthermore, we conducted PPI analysis to identify the core network of ARGs in PCa. RESULTS: Based on analysis of the TCGA database, a set of eight ARGs were identified as prognostic signature genes for prostate cancer. The reliability and validity of this signature were well verified in both the TCGA and GEO codifications. Using this signature, patients were classified into two groups based on their risk for developing BCR. There was a significant difference in BCR-free time between the high and low risk groups (P < 0.05).This signature serves as a dependable and unbiased prognostic factor for predicting biochemical recurrence (BCR) in prostate cancer (PCa) patients. It outperforms clinicopathological characteristics in terms of accuracy and reliability. PLK1 may play a potential regulatory role as a core gene in the development of prostate cancer. CONCLUSION: This signature suggests the potential role of ARGs in the development and progression of PCa and can effectively predict the risk of BCR in PCa patients after surgery. It also provides a basis for further research into the mechanism of ARGs in PCa and for the clinical management of patients with PCa. Frontiers Media S.A. 2023-08-17 /pmc/articles/PMC10469923/ /pubmed/37664042 http://dx.doi.org/10.3389/fonc.2023.1169425 Text en Copyright © 2023 Zhao, Wang, Tang, Hu, Zhou, Ge, Dong 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
Zhao, Xiaodong
Wang, Zuheng
Tang, Zilu
Hu, Jun
Zhou, Yulin
Ge, Jingping
Dong, Jie
Xu, Song
An anoikis-related gene signature for prediction of the prognosis in prostate cancer
title An anoikis-related gene signature for prediction of the prognosis in prostate cancer
title_full An anoikis-related gene signature for prediction of the prognosis in prostate cancer
title_fullStr An anoikis-related gene signature for prediction of the prognosis in prostate cancer
title_full_unstemmed An anoikis-related gene signature for prediction of the prognosis in prostate cancer
title_short An anoikis-related gene signature for prediction of the prognosis in prostate cancer
title_sort anoikis-related gene signature for prediction of the prognosis in prostate cancer
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10469923/
https://www.ncbi.nlm.nih.gov/pubmed/37664042
http://dx.doi.org/10.3389/fonc.2023.1169425
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