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The Development of a Gleason Score-Related Gene Signature for Predicting the Prognosis of Prostate Cancer

The recurrence of prostate cancer (PCa) is intrinsically linked to increased mortality. The goal of this study was to develop an efficient and reliable prognosis prediction signature for PCa patients. The training cohort was acquired from The Cancer Genome Atlas (TCGA) dataset, while the validation...

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Autores principales: Yimamu, Yiliyasi, Yang, Xu, Chen, Junxin, Luo, Cheng, Xiao, Wenyang, Guan, Hongyu, Wang, Daohu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9737657/
https://www.ncbi.nlm.nih.gov/pubmed/36498737
http://dx.doi.org/10.3390/jcm11237164
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author Yimamu, Yiliyasi
Yang, Xu
Chen, Junxin
Luo, Cheng
Xiao, Wenyang
Guan, Hongyu
Wang, Daohu
author_facet Yimamu, Yiliyasi
Yang, Xu
Chen, Junxin
Luo, Cheng
Xiao, Wenyang
Guan, Hongyu
Wang, Daohu
author_sort Yimamu, Yiliyasi
collection PubMed
description The recurrence of prostate cancer (PCa) is intrinsically linked to increased mortality. The goal of this study was to develop an efficient and reliable prognosis prediction signature for PCa patients. The training cohort was acquired from The Cancer Genome Atlas (TCGA) dataset, while the validation cohort was obtained from the Gene Expression Omnibus (GEO) dataset (GSE70769). To explore the Gleason score (GS)-based prediction signature, we screened the differentially expressed genes (DEGs) between low- and high-GS groups, and then univariate Cox regression survival analysis and multiple Cox analyses were performed sequentially using the training cohort. The testing cohort was used to evaluate and validate the prognostic model’s effectiveness, accuracy, and clinical practicability. In addition, the correlation analyses between the risk score and clinical features, as well as immune infiltration, were performed. We constructed and optimized a valid and credible model for predicting the prognosis of PCa recurrence using four GS-associated genes (SFRP4, FEV, COL1A1, SULF1). Furthermore, ROC and Kaplan–Meier analysis revealed a higher predictive efficiency for biochemical recurrence (BCR). The results showed that the risk model was an independent prognostic factor. Moreover, the risk score was associated with clinical features and immune infiltration. Finally, the risk model was validated in a testing cohort. Our data support that the GS-based four-gene signature acts as a novel signature for predicting BCR in PCa patients.
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spelling pubmed-97376572022-12-11 The Development of a Gleason Score-Related Gene Signature for Predicting the Prognosis of Prostate Cancer Yimamu, Yiliyasi Yang, Xu Chen, Junxin Luo, Cheng Xiao, Wenyang Guan, Hongyu Wang, Daohu J Clin Med Article The recurrence of prostate cancer (PCa) is intrinsically linked to increased mortality. The goal of this study was to develop an efficient and reliable prognosis prediction signature for PCa patients. The training cohort was acquired from The Cancer Genome Atlas (TCGA) dataset, while the validation cohort was obtained from the Gene Expression Omnibus (GEO) dataset (GSE70769). To explore the Gleason score (GS)-based prediction signature, we screened the differentially expressed genes (DEGs) between low- and high-GS groups, and then univariate Cox regression survival analysis and multiple Cox analyses were performed sequentially using the training cohort. The testing cohort was used to evaluate and validate the prognostic model’s effectiveness, accuracy, and clinical practicability. In addition, the correlation analyses between the risk score and clinical features, as well as immune infiltration, were performed. We constructed and optimized a valid and credible model for predicting the prognosis of PCa recurrence using four GS-associated genes (SFRP4, FEV, COL1A1, SULF1). Furthermore, ROC and Kaplan–Meier analysis revealed a higher predictive efficiency for biochemical recurrence (BCR). The results showed that the risk model was an independent prognostic factor. Moreover, the risk score was associated with clinical features and immune infiltration. Finally, the risk model was validated in a testing cohort. Our data support that the GS-based four-gene signature acts as a novel signature for predicting BCR in PCa patients. MDPI 2022-12-01 /pmc/articles/PMC9737657/ /pubmed/36498737 http://dx.doi.org/10.3390/jcm11237164 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yimamu, Yiliyasi
Yang, Xu
Chen, Junxin
Luo, Cheng
Xiao, Wenyang
Guan, Hongyu
Wang, Daohu
The Development of a Gleason Score-Related Gene Signature for Predicting the Prognosis of Prostate Cancer
title The Development of a Gleason Score-Related Gene Signature for Predicting the Prognosis of Prostate Cancer
title_full The Development of a Gleason Score-Related Gene Signature for Predicting the Prognosis of Prostate Cancer
title_fullStr The Development of a Gleason Score-Related Gene Signature for Predicting the Prognosis of Prostate Cancer
title_full_unstemmed The Development of a Gleason Score-Related Gene Signature for Predicting the Prognosis of Prostate Cancer
title_short The Development of a Gleason Score-Related Gene Signature for Predicting the Prognosis of Prostate Cancer
title_sort development of a gleason score-related gene signature for predicting the prognosis of prostate cancer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9737657/
https://www.ncbi.nlm.nih.gov/pubmed/36498737
http://dx.doi.org/10.3390/jcm11237164
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