Cargando…
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...
Autores principales: | , , , , , , , |
---|---|
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 |
_version_ | 1785099556272209920 |
---|---|
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. |
format | Online Article Text |
id | pubmed-10469923 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT zhaoxiaodong ananoikisrelatedgenesignatureforpredictionoftheprognosisinprostatecancer AT wangzuheng ananoikisrelatedgenesignatureforpredictionoftheprognosisinprostatecancer AT tangzilu ananoikisrelatedgenesignatureforpredictionoftheprognosisinprostatecancer AT hujun ananoikisrelatedgenesignatureforpredictionoftheprognosisinprostatecancer AT zhouyulin ananoikisrelatedgenesignatureforpredictionoftheprognosisinprostatecancer AT gejingping ananoikisrelatedgenesignatureforpredictionoftheprognosisinprostatecancer AT dongjie ananoikisrelatedgenesignatureforpredictionoftheprognosisinprostatecancer AT xusong ananoikisrelatedgenesignatureforpredictionoftheprognosisinprostatecancer AT zhaoxiaodong anoikisrelatedgenesignatureforpredictionoftheprognosisinprostatecancer AT wangzuheng anoikisrelatedgenesignatureforpredictionoftheprognosisinprostatecancer AT tangzilu anoikisrelatedgenesignatureforpredictionoftheprognosisinprostatecancer AT hujun anoikisrelatedgenesignatureforpredictionoftheprognosisinprostatecancer AT zhouyulin anoikisrelatedgenesignatureforpredictionoftheprognosisinprostatecancer AT gejingping anoikisrelatedgenesignatureforpredictionoftheprognosisinprostatecancer AT dongjie anoikisrelatedgenesignatureforpredictionoftheprognosisinprostatecancer AT xusong anoikisrelatedgenesignatureforpredictionoftheprognosisinprostatecancer |