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Identification of Pyroptosis-Related Gene Signatures and Construction of the Risk Model to Predict BCR in Prostate Cancer
Prostate cancer is one of the most common malignant tumors in men. Pyroptosis is related to tumor immune infiltration and tumor microenvironment (TME) and has been confirmed to be related to the progression of a variety of tumors. However, the relationship between prostate cancer and pyroptosis, as...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9259891/ https://www.ncbi.nlm.nih.gov/pubmed/35813821 http://dx.doi.org/10.3389/fmolb.2022.850758 |
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author | Wang, Miaomiao Xia, Haoran Yan, Qiuxia Liu, Wen Liu, Ming Wang, Xuan |
author_facet | Wang, Miaomiao Xia, Haoran Yan, Qiuxia Liu, Wen Liu, Ming Wang, Xuan |
author_sort | Wang, Miaomiao |
collection | PubMed |
description | Prostate cancer is one of the most common malignant tumors in men. Pyroptosis is related to tumor immune infiltration and tumor microenvironment (TME) and has been confirmed to be related to the progression of a variety of tumors. However, the relationship between prostate cancer and pyroptosis, as well as TME and tumor immune infiltration, has not been discussed yet. We obtained and combined the RNA-seq data of prostate cancer from TCGA and GEO databases, analyzed the differential expression of pyroptosis-related genes (PRGs), and divided them into two groups according to the PRG expression level. The relationship between pyroptosis subtypes and the TME of prostate cancer was further verified, and the differential expression genes (DEGs) in the two subtypes were identified. The relationship between the DEGs and clinicopathology was explored and KEGG and GO enrichment analysis was conducted; it was found that most DEGs were enriched in immune-related pathways. Then, we randomly divided datasets into training and testing sets, performed the LASSO and multicox progression analysis, selected eight genes as prognostic signatures and used the eight genes, calculated the risk score, and then separated the entire cohort into high- and low-risk groups. The prognosis between two groups and the 1-, 3-, and 5-year ROC curves of biochemical relapse (BCR) were verified in training, testing, and the entire cohort, respectively. The TME, CSC index, mutation, and drug susceptibility were also discussed. |
format | Online Article Text |
id | pubmed-9259891 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92598912022-07-08 Identification of Pyroptosis-Related Gene Signatures and Construction of the Risk Model to Predict BCR in Prostate Cancer Wang, Miaomiao Xia, Haoran Yan, Qiuxia Liu, Wen Liu, Ming Wang, Xuan Front Mol Biosci Molecular Biosciences Prostate cancer is one of the most common malignant tumors in men. Pyroptosis is related to tumor immune infiltration and tumor microenvironment (TME) and has been confirmed to be related to the progression of a variety of tumors. However, the relationship between prostate cancer and pyroptosis, as well as TME and tumor immune infiltration, has not been discussed yet. We obtained and combined the RNA-seq data of prostate cancer from TCGA and GEO databases, analyzed the differential expression of pyroptosis-related genes (PRGs), and divided them into two groups according to the PRG expression level. The relationship between pyroptosis subtypes and the TME of prostate cancer was further verified, and the differential expression genes (DEGs) in the two subtypes were identified. The relationship between the DEGs and clinicopathology was explored and KEGG and GO enrichment analysis was conducted; it was found that most DEGs were enriched in immune-related pathways. Then, we randomly divided datasets into training and testing sets, performed the LASSO and multicox progression analysis, selected eight genes as prognostic signatures and used the eight genes, calculated the risk score, and then separated the entire cohort into high- and low-risk groups. The prognosis between two groups and the 1-, 3-, and 5-year ROC curves of biochemical relapse (BCR) were verified in training, testing, and the entire cohort, respectively. The TME, CSC index, mutation, and drug susceptibility were also discussed. Frontiers Media S.A. 2022-06-23 /pmc/articles/PMC9259891/ /pubmed/35813821 http://dx.doi.org/10.3389/fmolb.2022.850758 Text en Copyright © 2022 Wang, Xia, Yan, Liu, Liu and Wang. 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 | Molecular Biosciences Wang, Miaomiao Xia, Haoran Yan, Qiuxia Liu, Wen Liu, Ming Wang, Xuan Identification of Pyroptosis-Related Gene Signatures and Construction of the Risk Model to Predict BCR in Prostate Cancer |
title | Identification of Pyroptosis-Related Gene Signatures and Construction of the Risk Model to Predict BCR in Prostate Cancer |
title_full | Identification of Pyroptosis-Related Gene Signatures and Construction of the Risk Model to Predict BCR in Prostate Cancer |
title_fullStr | Identification of Pyroptosis-Related Gene Signatures and Construction of the Risk Model to Predict BCR in Prostate Cancer |
title_full_unstemmed | Identification of Pyroptosis-Related Gene Signatures and Construction of the Risk Model to Predict BCR in Prostate Cancer |
title_short | Identification of Pyroptosis-Related Gene Signatures and Construction of the Risk Model to Predict BCR in Prostate Cancer |
title_sort | identification of pyroptosis-related gene signatures and construction of the risk model to predict bcr in prostate cancer |
topic | Molecular Biosciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9259891/ https://www.ncbi.nlm.nih.gov/pubmed/35813821 http://dx.doi.org/10.3389/fmolb.2022.850758 |
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