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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9737657 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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