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A Novel Apoptosis-Related Gene Signature Predicts Biochemical Recurrence of Localized Prostate Cancer After Radical Prostatectomy
Background: Nowadays, predictions of biochemical recurrence (BCR) in localized prostate cancer (PCa) patients after radical prostatectomy (RP) are mainly based on clinical parameters with a low predictive accuracy. Given the critical role of apoptosis in PCa occurrence and progression, we aimed to e...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7734189/ https://www.ncbi.nlm.nih.gov/pubmed/33329725 http://dx.doi.org/10.3389/fgene.2020.586376 |
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author | Zhang, Qijie Zhao, Kai Song, Lebin Ji, Chengjian Cong, Rong Luan, Jiaochen Zhou, Xiang Xia, Jiadong Song, Ninghong |
author_facet | Zhang, Qijie Zhao, Kai Song, Lebin Ji, Chengjian Cong, Rong Luan, Jiaochen Zhou, Xiang Xia, Jiadong Song, Ninghong |
author_sort | Zhang, Qijie |
collection | PubMed |
description | Background: Nowadays, predictions of biochemical recurrence (BCR) in localized prostate cancer (PCa) patients after radical prostatectomy (RP) are mainly based on clinical parameters with a low predictive accuracy. Given the critical role of apoptosis in PCa occurrence and progression, we aimed to establish a novel predictive model based on apoptosis-related gene signature and clinicopathological parameters that can improve risk stratification for BCR and assist in clinical decision-making. Methods: Expression data and corresponding clinical information were obtained from four public cohorts, one from The Cancer Genome Atlas (TCGA) dataset and three from the Gene Expression Omnibus (GEO) dataset. Weighted gene co-expression network analysis (WGCNA) was performed to identify candidate modules closely correlated to BCR, and univariate and multivariate Cox regression analyses were utilized to build the gene signature. Time-dependent receiver operating curve (ROC) and Kaplan–Meier (KM) survival analysis were used to assess the prognostic value. Finally, we analyzed the expression of genes in the signature and validated the results using quantitative real-time PCR (qRT-PCR). Results: The novel gene signature we established exhibited a high prognostic value and was able to act as an independent risk factor for BCR [Training set: P < 0.001, hazard ratio (HR) = 7.826; Validation set I: P = 0.006, HR = 2.655; Validation set II: P = 0.003, HR = 4.175; Validation set III: P < 0.001, HR = 3.008]. Nomogram based on the gene signature and clinical parameters was capable of distinguishing high-risk BCR patients. Additionally, functional enrichment analysis showed several enriched pathways and biological processes, which might help reveal the underlying mechanism. The expression results of qRT-PCR were consistent with TCGA results. Conclusion: The apoptosis-related gene signature could serve as a powerful predictor and risk factor for BCR in localized PCa patients after RP. |
format | Online Article Text |
id | pubmed-7734189 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-77341892020-12-15 A Novel Apoptosis-Related Gene Signature Predicts Biochemical Recurrence of Localized Prostate Cancer After Radical Prostatectomy Zhang, Qijie Zhao, Kai Song, Lebin Ji, Chengjian Cong, Rong Luan, Jiaochen Zhou, Xiang Xia, Jiadong Song, Ninghong Front Genet Genetics Background: Nowadays, predictions of biochemical recurrence (BCR) in localized prostate cancer (PCa) patients after radical prostatectomy (RP) are mainly based on clinical parameters with a low predictive accuracy. Given the critical role of apoptosis in PCa occurrence and progression, we aimed to establish a novel predictive model based on apoptosis-related gene signature and clinicopathological parameters that can improve risk stratification for BCR and assist in clinical decision-making. Methods: Expression data and corresponding clinical information were obtained from four public cohorts, one from The Cancer Genome Atlas (TCGA) dataset and three from the Gene Expression Omnibus (GEO) dataset. Weighted gene co-expression network analysis (WGCNA) was performed to identify candidate modules closely correlated to BCR, and univariate and multivariate Cox regression analyses were utilized to build the gene signature. Time-dependent receiver operating curve (ROC) and Kaplan–Meier (KM) survival analysis were used to assess the prognostic value. Finally, we analyzed the expression of genes in the signature and validated the results using quantitative real-time PCR (qRT-PCR). Results: The novel gene signature we established exhibited a high prognostic value and was able to act as an independent risk factor for BCR [Training set: P < 0.001, hazard ratio (HR) = 7.826; Validation set I: P = 0.006, HR = 2.655; Validation set II: P = 0.003, HR = 4.175; Validation set III: P < 0.001, HR = 3.008]. Nomogram based on the gene signature and clinical parameters was capable of distinguishing high-risk BCR patients. Additionally, functional enrichment analysis showed several enriched pathways and biological processes, which might help reveal the underlying mechanism. The expression results of qRT-PCR were consistent with TCGA results. Conclusion: The apoptosis-related gene signature could serve as a powerful predictor and risk factor for BCR in localized PCa patients after RP. Frontiers Media S.A. 2020-11-30 /pmc/articles/PMC7734189/ /pubmed/33329725 http://dx.doi.org/10.3389/fgene.2020.586376 Text en Copyright © 2020 Zhang, Zhao, Song, Ji, Cong, Luan, Zhou, Xia and Song. http://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 | Genetics Zhang, Qijie Zhao, Kai Song, Lebin Ji, Chengjian Cong, Rong Luan, Jiaochen Zhou, Xiang Xia, Jiadong Song, Ninghong A Novel Apoptosis-Related Gene Signature Predicts Biochemical Recurrence of Localized Prostate Cancer After Radical Prostatectomy |
title | A Novel Apoptosis-Related Gene Signature Predicts Biochemical Recurrence of Localized Prostate Cancer After Radical Prostatectomy |
title_full | A Novel Apoptosis-Related Gene Signature Predicts Biochemical Recurrence of Localized Prostate Cancer After Radical Prostatectomy |
title_fullStr | A Novel Apoptosis-Related Gene Signature Predicts Biochemical Recurrence of Localized Prostate Cancer After Radical Prostatectomy |
title_full_unstemmed | A Novel Apoptosis-Related Gene Signature Predicts Biochemical Recurrence of Localized Prostate Cancer After Radical Prostatectomy |
title_short | A Novel Apoptosis-Related Gene Signature Predicts Biochemical Recurrence of Localized Prostate Cancer After Radical Prostatectomy |
title_sort | novel apoptosis-related gene signature predicts biochemical recurrence of localized prostate cancer after radical prostatectomy |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7734189/ https://www.ncbi.nlm.nih.gov/pubmed/33329725 http://dx.doi.org/10.3389/fgene.2020.586376 |
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