Novel cuproptosis-related long non-coding RNA signature to predict prognosis in prostate carcinoma
Background Cuproptosis, an emerging form of programmed cell death, has recently been identified. However, the association between cuproptosis-related long non-coding RNA (lncRNA) signature and the prognosis in prostate carcinoma remains elusive. This study aims to develop the novel cuproptosis-relat...
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| Published in | BMC cancer Vol. 23; no. 1; pp. 105 - 17 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
30.01.2023
BioMed Central Ltd Springer Nature B.V BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1471-2407 1471-2407 |
| DOI | 10.1186/s12885-023-10584-0 |
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| Abstract | Background
Cuproptosis, an emerging form of programmed cell death, has recently been identified. However, the association between cuproptosis-related long non-coding RNA (lncRNA) signature and the prognosis in prostate carcinoma remains elusive. This study aims to develop the novel cuproptosis-related lncRNA signature in prostate cancer and explore its latent molecular function.
Methods
RNA-seq data and clinical information were downloaded from the TCGA datasets. Then, cuproptosis-related gene was identified from the previous literature and further applied to screen the cuproptosis-related differentially expressed lncRNAs. Patients were randomly assigned to the training cohort or the validation cohort with a 1:1 ratio. Subsequently, the machine learning algorithms (Lasso and stepwise Cox (direction = both)) were used to construct a novel prognostic signature in the training cohorts, which was validated by the validation and the entire TCGA cohorts. The nomogram base on the lncRNA signature and several clinicopathological traits were constructed to predict the prognosis. Functional enrichment and immune analysis were performed to evaluate its potential mechanism. Furthermore, differences in the landscape of gene mutation, tumour mutational burden (TMB), microsatellite instability (MSI), drug sensitivity between both risk groups were also assessed to explicit their relationships.
Results
The cuproptosis-related lncRNA signature was constructed based on the differentially expressed cuproptosis-related lncRNAs, including AC005790.1, AC011472.4, AC099791.2, AC144450.1, LIPE-AS1, and STPG3-AS1. Kaplan–Meier survival and ROC curves demonstrate that the prognosis signature as an independent risk indicator had excellent potential to predict the prognosis in prostate cancer. The signature was closely associated with age, T stage, N stage, and the Gleason score. Immune analysis shows that the high-risk group was in an immunosuppressive microenvironment. Additionally, the significant difference in landscape of gene mutation, tumour mutational burden, microsatellite instability, and drug sensitivity between both risk groups was observed.
Conclusions
A novel cuproptosis-related lncRNA signature was constructed using machine learning algorithms to predict the prognosis of prostate cancer. It was closely with associated with several common clinical traits, immune cell infiltration, immune-related functions, immune checkpoints, gene mutation, TMB, MSI, and the drug sensitivity, which may be useful to improve the clinical outcome. |
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| AbstractList | Background Cuproptosis, an emerging form of programmed cell death, has recently been identified. However, the association between cuproptosis-related long non-coding RNA (lncRNA) signature and the prognosis in prostate carcinoma remains elusive. This study aims to develop the novel cuproptosis-related lncRNA signature in prostate cancer and explore its latent molecular function. Methods RNA-seq data and clinical information were downloaded from the TCGA datasets. Then, cuproptosis-related gene was identified from the previous literature and further applied to screen the cuproptosis-related differentially expressed lncRNAs. Patients were randomly assigned to the training cohort or the validation cohort with a 1:1 ratio. Subsequently, the machine learning algorithms (Lasso and stepwise Cox (direction = both)) were used to construct a novel prognostic signature in the training cohorts, which was validated by the validation and the entire TCGA cohorts. The nomogram base on the lncRNA signature and several clinicopathological traits were constructed to predict the prognosis. Functional enrichment and immune analysis were performed to evaluate its potential mechanism. Furthermore, differences in the landscape of gene mutation, tumour mutational burden (TMB), microsatellite instability (MSI), drug sensitivity between both risk groups were also assessed to explicit their relationships. Results The cuproptosis-related lncRNA signature was constructed based on the differentially expressed cuproptosis-related lncRNAs, including AC005790.1, AC011472.4, AC099791.2, AC144450.1, LIPE-AS1, and STPG3-AS1. Kaplan-Meier survival and ROC curves demonstrate that the prognosis signature as an independent risk indicator had excellent potential to predict the prognosis in prostate cancer. The signature was closely associated with age, T stage, N stage, and the Gleason score. Immune analysis shows that the high-risk group was in an immunosuppressive microenvironment. Additionally, the significant difference in landscape of gene mutation, tumour mutational burden, microsatellite instability, and drug sensitivity between both risk groups was observed. Conclusions A novel cuproptosis-related lncRNA signature was constructed using machine learning algorithms to predict the prognosis of prostate cancer. It was closely with associated with several common clinical traits, immune cell infiltration, immune-related functions, immune checkpoints, gene mutation, TMB, MSI, and the drug sensitivity, which may be useful to improve the clinical outcome. Keywords: Cuproptosis, LncRNA, Prostate carcinoma, Prognostic signature, Machine learning Cuproptosis, an emerging form of programmed cell death, has recently been identified. However, the association between cuproptosis-related long non-coding RNA (lncRNA) signature and the prognosis in prostate carcinoma remains elusive. This study aims to develop the novel cuproptosis-related lncRNA signature in prostate cancer and explore its latent molecular function. RNA-seq data and clinical information were downloaded from the TCGA datasets. Then, cuproptosis-related gene was identified from the previous literature and further applied to screen the cuproptosis-related differentially expressed lncRNAs. Patients were randomly assigned to the training cohort or the validation cohort with a 1:1 ratio. Subsequently, the machine learning algorithms (Lasso and stepwise Cox (direction = both)) were used to construct a novel prognostic signature in the training cohorts, which was validated by the validation and the entire TCGA cohorts. The nomogram base on the lncRNA signature and several clinicopathological traits were constructed to predict the prognosis. Functional enrichment and immune analysis were performed to evaluate its potential mechanism. Furthermore, differences in the landscape of gene mutation, tumour mutational burden (TMB), microsatellite instability (MSI), drug sensitivity between both risk groups were also assessed to explicit their relationships. The cuproptosis-related lncRNA signature was constructed based on the differentially expressed cuproptosis-related lncRNAs, including AC005790.1, AC011472.4, AC099791.2, AC144450.1, LIPE-AS1, and STPG3-AS1. Kaplan-Meier survival and ROC curves demonstrate that the prognosis signature as an independent risk indicator had excellent potential to predict the prognosis in prostate cancer. The signature was closely associated with age, T stage, N stage, and the Gleason score. Immune analysis shows that the high-risk group was in an immunosuppressive microenvironment. Additionally, the significant difference in landscape of gene mutation, tumour mutational burden, microsatellite instability, and drug sensitivity between both risk groups was observed. A novel cuproptosis-related lncRNA signature was constructed using machine learning algorithms to predict the prognosis of prostate cancer. It was closely with associated with several common clinical traits, immune cell infiltration, immune-related functions, immune checkpoints, gene mutation, TMB, MSI, and the drug sensitivity, which may be useful to improve the clinical outcome. Cuproptosis, an emerging form of programmed cell death, has recently been identified. However, the association between cuproptosis-related long non-coding RNA (lncRNA) signature and the prognosis in prostate carcinoma remains elusive. This study aims to develop the novel cuproptosis-related lncRNA signature in prostate cancer and explore its latent molecular function.BACKGROUNDCuproptosis, an emerging form of programmed cell death, has recently been identified. However, the association between cuproptosis-related long non-coding RNA (lncRNA) signature and the prognosis in prostate carcinoma remains elusive. This study aims to develop the novel cuproptosis-related lncRNA signature in prostate cancer and explore its latent molecular function.RNA-seq data and clinical information were downloaded from the TCGA datasets. Then, cuproptosis-related gene was identified from the previous literature and further applied to screen the cuproptosis-related differentially expressed lncRNAs. Patients were randomly assigned to the training cohort or the validation cohort with a 1:1 ratio. Subsequently, the machine learning algorithms (Lasso and stepwise Cox (direction = both)) were used to construct a novel prognostic signature in the training cohorts, which was validated by the validation and the entire TCGA cohorts. The nomogram base on the lncRNA signature and several clinicopathological traits were constructed to predict the prognosis. Functional enrichment and immune analysis were performed to evaluate its potential mechanism. Furthermore, differences in the landscape of gene mutation, tumour mutational burden (TMB), microsatellite instability (MSI), drug sensitivity between both risk groups were also assessed to explicit their relationships.METHODSRNA-seq data and clinical information were downloaded from the TCGA datasets. Then, cuproptosis-related gene was identified from the previous literature and further applied to screen the cuproptosis-related differentially expressed lncRNAs. Patients were randomly assigned to the training cohort or the validation cohort with a 1:1 ratio. Subsequently, the machine learning algorithms (Lasso and stepwise Cox (direction = both)) were used to construct a novel prognostic signature in the training cohorts, which was validated by the validation and the entire TCGA cohorts. The nomogram base on the lncRNA signature and several clinicopathological traits were constructed to predict the prognosis. Functional enrichment and immune analysis were performed to evaluate its potential mechanism. Furthermore, differences in the landscape of gene mutation, tumour mutational burden (TMB), microsatellite instability (MSI), drug sensitivity between both risk groups were also assessed to explicit their relationships.The cuproptosis-related lncRNA signature was constructed based on the differentially expressed cuproptosis-related lncRNAs, including AC005790.1, AC011472.4, AC099791.2, AC144450.1, LIPE-AS1, and STPG3-AS1. Kaplan-Meier survival and ROC curves demonstrate that the prognosis signature as an independent risk indicator had excellent potential to predict the prognosis in prostate cancer. The signature was closely associated with age, T stage, N stage, and the Gleason score. Immune analysis shows that the high-risk group was in an immunosuppressive microenvironment. Additionally, the significant difference in landscape of gene mutation, tumour mutational burden, microsatellite instability, and drug sensitivity between both risk groups was observed.RESULTSThe cuproptosis-related lncRNA signature was constructed based on the differentially expressed cuproptosis-related lncRNAs, including AC005790.1, AC011472.4, AC099791.2, AC144450.1, LIPE-AS1, and STPG3-AS1. Kaplan-Meier survival and ROC curves demonstrate that the prognosis signature as an independent risk indicator had excellent potential to predict the prognosis in prostate cancer. The signature was closely associated with age, T stage, N stage, and the Gleason score. Immune analysis shows that the high-risk group was in an immunosuppressive microenvironment. Additionally, the significant difference in landscape of gene mutation, tumour mutational burden, microsatellite instability, and drug sensitivity between both risk groups was observed.A novel cuproptosis-related lncRNA signature was constructed using machine learning algorithms to predict the prognosis of prostate cancer. It was closely with associated with several common clinical traits, immune cell infiltration, immune-related functions, immune checkpoints, gene mutation, TMB, MSI, and the drug sensitivity, which may be useful to improve the clinical outcome.CONCLUSIONSA novel cuproptosis-related lncRNA signature was constructed using machine learning algorithms to predict the prognosis of prostate cancer. It was closely with associated with several common clinical traits, immune cell infiltration, immune-related functions, immune checkpoints, gene mutation, TMB, MSI, and the drug sensitivity, which may be useful to improve the clinical outcome. Background Cuproptosis, an emerging form of programmed cell death, has recently been identified. However, the association between cuproptosis-related long non-coding RNA (lncRNA) signature and the prognosis in prostate carcinoma remains elusive. This study aims to develop the novel cuproptosis-related lncRNA signature in prostate cancer and explore its latent molecular function. Methods RNA-seq data and clinical information were downloaded from the TCGA datasets. Then, cuproptosis-related gene was identified from the previous literature and further applied to screen the cuproptosis-related differentially expressed lncRNAs. Patients were randomly assigned to the training cohort or the validation cohort with a 1:1 ratio. Subsequently, the machine learning algorithms (Lasso and stepwise Cox (direction = both)) were used to construct a novel prognostic signature in the training cohorts, which was validated by the validation and the entire TCGA cohorts. The nomogram base on the lncRNA signature and several clinicopathological traits were constructed to predict the prognosis. Functional enrichment and immune analysis were performed to evaluate its potential mechanism. Furthermore, differences in the landscape of gene mutation, tumour mutational burden (TMB), microsatellite instability (MSI), drug sensitivity between both risk groups were also assessed to explicit their relationships. Results The cuproptosis-related lncRNA signature was constructed based on the differentially expressed cuproptosis-related lncRNAs, including AC005790.1, AC011472.4, AC099791.2, AC144450.1, LIPE-AS1, and STPG3-AS1. Kaplan–Meier survival and ROC curves demonstrate that the prognosis signature as an independent risk indicator had excellent potential to predict the prognosis in prostate cancer. The signature was closely associated with age, T stage, N stage, and the Gleason score. Immune analysis shows that the high-risk group was in an immunosuppressive microenvironment. Additionally, the significant difference in landscape of gene mutation, tumour mutational burden, microsatellite instability, and drug sensitivity between both risk groups was observed. Conclusions A novel cuproptosis-related lncRNA signature was constructed using machine learning algorithms to predict the prognosis of prostate cancer. It was closely with associated with several common clinical traits, immune cell infiltration, immune-related functions, immune checkpoints, gene mutation, TMB, MSI, and the drug sensitivity, which may be useful to improve the clinical outcome. Abstract Background Cuproptosis, an emerging form of programmed cell death, has recently been identified. However, the association between cuproptosis-related long non-coding RNA (lncRNA) signature and the prognosis in prostate carcinoma remains elusive. This study aims to develop the novel cuproptosis-related lncRNA signature in prostate cancer and explore its latent molecular function. Methods RNA-seq data and clinical information were downloaded from the TCGA datasets. Then, cuproptosis-related gene was identified from the previous literature and further applied to screen the cuproptosis-related differentially expressed lncRNAs. Patients were randomly assigned to the training cohort or the validation cohort with a 1:1 ratio. Subsequently, the machine learning algorithms (Lasso and stepwise Cox (direction = both)) were used to construct a novel prognostic signature in the training cohorts, which was validated by the validation and the entire TCGA cohorts. The nomogram base on the lncRNA signature and several clinicopathological traits were constructed to predict the prognosis. Functional enrichment and immune analysis were performed to evaluate its potential mechanism. Furthermore, differences in the landscape of gene mutation, tumour mutational burden (TMB), microsatellite instability (MSI), drug sensitivity between both risk groups were also assessed to explicit their relationships. Results The cuproptosis-related lncRNA signature was constructed based on the differentially expressed cuproptosis-related lncRNAs, including AC005790.1, AC011472.4, AC099791.2, AC144450.1, LIPE-AS1, and STPG3-AS1. Kaplan–Meier survival and ROC curves demonstrate that the prognosis signature as an independent risk indicator had excellent potential to predict the prognosis in prostate cancer. The signature was closely associated with age, T stage, N stage, and the Gleason score. Immune analysis shows that the high-risk group was in an immunosuppressive microenvironment. Additionally, the significant difference in landscape of gene mutation, tumour mutational burden, microsatellite instability, and drug sensitivity between both risk groups was observed. Conclusions A novel cuproptosis-related lncRNA signature was constructed using machine learning algorithms to predict the prognosis of prostate cancer. It was closely with associated with several common clinical traits, immune cell infiltration, immune-related functions, immune checkpoints, gene mutation, TMB, MSI, and the drug sensitivity, which may be useful to improve the clinical outcome. Cuproptosis, an emerging form of programmed cell death, has recently been identified. However, the association between cuproptosis-related long non-coding RNA (lncRNA) signature and the prognosis in prostate carcinoma remains elusive. This study aims to develop the novel cuproptosis-related lncRNA signature in prostate cancer and explore its latent molecular function. RNA-seq data and clinical information were downloaded from the TCGA datasets. Then, cuproptosis-related gene was identified from the previous literature and further applied to screen the cuproptosis-related differentially expressed lncRNAs. Patients were randomly assigned to the training cohort or the validation cohort with a 1:1 ratio. Subsequently, the machine learning algorithms (Lasso and stepwise Cox (direction = both)) were used to construct a novel prognostic signature in the training cohorts, which was validated by the validation and the entire TCGA cohorts. The nomogram base on the lncRNA signature and several clinicopathological traits were constructed to predict the prognosis. Functional enrichment and immune analysis were performed to evaluate its potential mechanism. Furthermore, differences in the landscape of gene mutation, tumour mutational burden (TMB), microsatellite instability (MSI), drug sensitivity between both risk groups were also assessed to explicit their relationships. The cuproptosis-related lncRNA signature was constructed based on the differentially expressed cuproptosis-related lncRNAs, including AC005790.1, AC011472.4, AC099791.2, AC144450.1, LIPE-AS1, and STPG3-AS1. Kaplan-Meier survival and ROC curves demonstrate that the prognosis signature as an independent risk indicator had excellent potential to predict the prognosis in prostate cancer. The signature was closely associated with age, T stage, N stage, and the Gleason score. Immune analysis shows that the high-risk group was in an immunosuppressive microenvironment. Additionally, the significant difference in landscape of gene mutation, tumour mutational burden, microsatellite instability, and drug sensitivity between both risk groups was observed. A novel cuproptosis-related lncRNA signature was constructed using machine learning algorithms to predict the prognosis of prostate cancer. It was closely with associated with several common clinical traits, immune cell infiltration, immune-related functions, immune checkpoints, gene mutation, TMB, MSI, and the drug sensitivity, which may be useful to improve the clinical outcome. BackgroundCuproptosis, an emerging form of programmed cell death, has recently been identified. However, the association between cuproptosis-related long non-coding RNA (lncRNA) signature and the prognosis in prostate carcinoma remains elusive. This study aims to develop the novel cuproptosis-related lncRNA signature in prostate cancer and explore its latent molecular function.MethodsRNA-seq data and clinical information were downloaded from the TCGA datasets. Then, cuproptosis-related gene was identified from the previous literature and further applied to screen the cuproptosis-related differentially expressed lncRNAs. Patients were randomly assigned to the training cohort or the validation cohort with a 1:1 ratio. Subsequently, the machine learning algorithms (Lasso and stepwise Cox (direction = both)) were used to construct a novel prognostic signature in the training cohorts, which was validated by the validation and the entire TCGA cohorts. The nomogram base on the lncRNA signature and several clinicopathological traits were constructed to predict the prognosis. Functional enrichment and immune analysis were performed to evaluate its potential mechanism. Furthermore, differences in the landscape of gene mutation, tumour mutational burden (TMB), microsatellite instability (MSI), drug sensitivity between both risk groups were also assessed to explicit their relationships.ResultsThe cuproptosis-related lncRNA signature was constructed based on the differentially expressed cuproptosis-related lncRNAs, including AC005790.1, AC011472.4, AC099791.2, AC144450.1, LIPE-AS1, and STPG3-AS1. Kaplan–Meier survival and ROC curves demonstrate that the prognosis signature as an independent risk indicator had excellent potential to predict the prognosis in prostate cancer. The signature was closely associated with age, T stage, N stage, and the Gleason score. Immune analysis shows that the high-risk group was in an immunosuppressive microenvironment. Additionally, the significant difference in landscape of gene mutation, tumour mutational burden, microsatellite instability, and drug sensitivity between both risk groups was observed.ConclusionsA novel cuproptosis-related lncRNA signature was constructed using machine learning algorithms to predict the prognosis of prostate cancer. It was closely with associated with several common clinical traits, immune cell infiltration, immune-related functions, immune checkpoints, gene mutation, TMB, MSI, and the drug sensitivity, which may be useful to improve the clinical outcome. |
| ArticleNumber | 105 |
| Audience | Academic |
| Author | Cheng, Xiaofeng Zhou, Xiaochen Zhang, Cheng Yang, Heng Chen, Yujun Zeng, Zhenhao Liu, Yifu Wang, Gongxian |
| Author_xml | – sequence: 1 givenname: Xiaofeng surname: Cheng fullname: Cheng, Xiaofeng organization: Department of Urology, Jiangxi Province, The First Affiliated Hospital of Nanchang University, Jiangxi Institute of Urology – sequence: 2 givenname: Zhenhao surname: Zeng fullname: Zeng, Zhenhao organization: Department of Urology, Jiangxi Province, The First Affiliated Hospital of Nanchang University, Jiangxi Institute of Urology – sequence: 3 givenname: Heng surname: Yang fullname: Yang, Heng organization: Department of Urology, Jiangxi Province, The First Affiliated Hospital of Nanchang University, Jiangxi Institute of Urology – sequence: 4 givenname: Yujun surname: Chen fullname: Chen, Yujun organization: Department of Urology, Jiangxi Province, The First Affiliated Hospital of Nanchang University, Jiangxi Institute of Urology – sequence: 5 givenname: Yifu surname: Liu fullname: Liu, Yifu organization: Department of Urology, Jiangxi Province, The First Affiliated Hospital of Nanchang University, Jiangxi Institute of Urology – sequence: 6 givenname: Xiaochen surname: Zhou fullname: Zhou, Xiaochen organization: Department of Urology, Jiangxi Province, The First Affiliated Hospital of Nanchang University, Jiangxi Institute of Urology – sequence: 7 givenname: Cheng surname: Zhang fullname: Zhang, Cheng organization: Department of Urology, Jiangxi Province, The First Affiliated Hospital of Nanchang University, Jiangxi Institute of Urology – sequence: 8 givenname: Gongxian surname: Wang fullname: Wang, Gongxian email: wanggx-mr@126.com organization: Department of Urology, Jiangxi Province, The First Affiliated Hospital of Nanchang University, Jiangxi Institute of Urology |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36717792$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1200/JCO.2014.56.2728 10.3322/caac.21708 10.1016/S0952-7915(00)00191-6 10.1038/nri1961 10.1200/JCO.2011.39.4767 10.1016/S0022-1759(98)00128-8 10.1186/s12894-021-00795-7 10.1007/s11912-020-0863-6 10.1126/science.abf0529 10.1016/j.ctrv.2008.07.004 10.1016/j.cell.2007.03.019 10.1016/j.celrep.2016.12.019 10.1016/j.canlet.2021.10.027 10.3390/cells8091015 10.1159/000362332 10.1080/14728222.2021.1951226 10.1517/14728222.7.3.405 10.1016/j.ecoenv.2021.112376 10.1001/jamaoncol.2018.5801 10.3390/ijms22189926 10.3390/ijms22189783 10.1186/s12943-020-01293-4 10.1038/nrc2090 10.23736/S1824-4785.20.03277-X 10.1001/jama.294.2.238 10.1016/S0140-6736(21)02437-5 10.1186/s12885-022-09876-8 10.1158/0008-5472.CAN-16-0431 10.1007/s00345-013-1065-8 10.3390/cancers14163888 10.1038/nchembio.72 10.1007/s00432-022-04040-y 10.3389/fcell.2020.00503 10.1021/acsami.9b19180 10.1016/j.bbamcr.2020.118893 10.1038/s41467-019-09234-6 10.3390/diagnostics10080549 10.1038/nrc.2017.121 10.3389/fimmu.2021.687975 10.1038/nrg2521 10.1371/journal.pone.0107468 10.1200/JCO.2016.34.15_suppl.e17045 10.3390/ijms222312628 10.1158/1055-9965.EPI-13-1126 |
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| Keywords | LncRNA Prognostic signature Prostate carcinoma Cuproptosis Machine learning |
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| License | 2023. The Author(s). Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. cc-by |
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| References | C Liu (10584_CR15) 2022; 22 L Wang (10584_CR26) 2021; 21 B Kaufman (10584_CR47) 2015; 33 S Boussios (10584_CR21) 2022; 14 G Gannot (10584_CR36) 2011; 1 JC Klink (10584_CR44) 2013; 31 JA Burger (10584_CR34) 2018; 18 P Tsvetkov (10584_CR14) 2022; 375 A Ghose (10584_CR46) 2021; 22 SL Reiner (10584_CR39) 2007; 129 S Boussios (10584_CR4) 2021; 25 M Sproull (10584_CR9) 2003; 7 RL Siegel (10584_CR1) 2022; 72 VC Shanbhag (10584_CR30) 2021; 1868 H Saxby (10584_CR31) 2020; 10 WK Kelly (10584_CR12) 2012; 30 P Tsou (10584_CR35) 2016; 76 WH Fridman (10584_CR37) 2014; 164 AO Sokolova (10584_CR22) 2020; 22 G Attard (10584_CR3) 2022; 399 10584_CR29 Y Chu (10584_CR32) 2020; 8 BE Kim (10584_CR8) 2008; 4 E Ioannidou (10584_CR11) 2021; 22 A Piccardo (10584_CR13) 2020; 64 P Charoentong (10584_CR23) 2017; 18 J Barkin (10584_CR38) 2017; 24 S Shah (10584_CR25) 2021; 22 AM De Marzo (10584_CR42) 2007; 7 J Liu (10584_CR33) 2021; 13 S Gallucci (10584_CR40) 2001; 13 R Hu (10584_CR7) 2020; 43 A Gupte (10584_CR10) 2009; 35 N Sharifi (10584_CR2) 2005; 294 GP Dunn (10584_CR45) 2006; 6 P Konstantinopoulos (10584_CR48) 2016; 34 JM Wang (10584_CR41) 1998; 220 Y Zhang (10584_CR16) 2021; 220 C Wang (10584_CR20) 2022; 525 Y Zhou (10584_CR19) 2019; 10 W Abida (10584_CR27) 2019; 5 S Wen (10584_CR18) 2020; 19 TR Mercer (10584_CR5) 2009; 10 B Gurel (10584_CR43) 2014; 23 D Zeng (10584_CR24) 2021; 12 10584_CR17 P Geeleher (10584_CR28) 2014; 9 JC Soares (10584_CR6) 2019; 11 |
| References_xml | – volume: 33 start-page: 244 issue: 3 year: 2015 ident: 10584_CR47 publication-title: J Clin Oncol doi: 10.1200/JCO.2014.56.2728 – volume: 72 start-page: 7 issue: 1 year: 2022 ident: 10584_CR1 publication-title: CA Cancer J Clin doi: 10.3322/caac.21708 – volume: 13 start-page: 114 issue: 1 year: 2001 ident: 10584_CR40 publication-title: Curr Opin Immunol doi: 10.1016/S0952-7915(00)00191-6 – volume: 6 start-page: 836 issue: 11 year: 2006 ident: 10584_CR45 publication-title: Nat Rev Immunol doi: 10.1038/nri1961 – volume: 30 start-page: 1534 issue: 13 year: 2012 ident: 10584_CR12 publication-title: J Clin Oncol doi: 10.1200/JCO.2011.39.4767 – volume: 220 start-page: 1 issue: 1–2 year: 1998 ident: 10584_CR41 publication-title: J Immunol Methods doi: 10.1016/S0022-1759(98)00128-8 – volume: 21 start-page: 29 issue: 1 year: 2021 ident: 10584_CR26 publication-title: BMC Urol doi: 10.1186/s12894-021-00795-7 – volume: 22 start-page: 5 issue: 1 year: 2020 ident: 10584_CR22 publication-title: Curr Oncol Rep doi: 10.1007/s11912-020-0863-6 – volume: 375 start-page: 1254 issue: 6586 year: 2022 ident: 10584_CR14 publication-title: Science doi: 10.1126/science.abf0529 – volume: 35 start-page: 32 issue: 1 year: 2009 ident: 10584_CR10 publication-title: Cancer Treat Rev doi: 10.1016/j.ctrv.2008.07.004 – volume: 129 start-page: 33 issue: 1 year: 2007 ident: 10584_CR39 publication-title: Cell doi: 10.1016/j.cell.2007.03.019 – volume: 18 start-page: 248 issue: 1 year: 2017 ident: 10584_CR23 publication-title: Cell Rep doi: 10.1016/j.celrep.2016.12.019 – volume: 525 start-page: 9 year: 2022 ident: 10584_CR20 publication-title: Cancer Lett doi: 10.1016/j.canlet.2021.10.027 – ident: 10584_CR29 doi: 10.3390/cells8091015 – volume: 164 start-page: 13 issue: 1 year: 2014 ident: 10584_CR37 publication-title: Int Arch Allergy Immunol doi: 10.1159/000362332 – volume: 25 start-page: 329 issue: 5 year: 2021 ident: 10584_CR4 publication-title: Expert Opin Ther Targets doi: 10.1080/14728222.2021.1951226 – volume: 7 start-page: 405 issue: 3 year: 2003 ident: 10584_CR9 publication-title: Expert Opin Ther Targets doi: 10.1517/14728222.7.3.405 – volume: 220 start-page: 112376 year: 2021 ident: 10584_CR16 publication-title: Ecotoxicol Environ Saf doi: 10.1016/j.ecoenv.2021.112376 – volume: 5 start-page: 471 issue: 4 year: 2019 ident: 10584_CR27 publication-title: JAMA Oncol doi: 10.1001/jamaoncol.2018.5801 – volume: 22 start-page: 9926 issue: 18 year: 2021 ident: 10584_CR11 publication-title: Int J Mol Sci doi: 10.3390/ijms22189926 – volume: 22 start-page: 9783 issue: 18 year: 2021 ident: 10584_CR46 publication-title: Int J Mol Sci doi: 10.3390/ijms22189783 – volume: 19 start-page: 171 issue: 1 year: 2020 ident: 10584_CR18 publication-title: Mol Cancer doi: 10.1186/s12943-020-01293-4 – volume: 7 start-page: 256 issue: 4 year: 2007 ident: 10584_CR42 publication-title: Nat Rev Cancer doi: 10.1038/nrc2090 – volume: 13 start-page: 11771 issue: 10 year: 2021 ident: 10584_CR33 publication-title: Am J Transl Res – volume: 64 start-page: 382 issue: 4 year: 2020 ident: 10584_CR13 publication-title: Q J Nucl Med Mol Imaging doi: 10.23736/S1824-4785.20.03277-X – volume: 294 start-page: 238 issue: 2 year: 2005 ident: 10584_CR2 publication-title: JAMA doi: 10.1001/jama.294.2.238 – volume: 399 start-page: 447 issue: 10323 year: 2022 ident: 10584_CR3 publication-title: Lancet doi: 10.1016/S0140-6736(21)02437-5 – volume: 22 start-page: 788 issue: 1 year: 2022 ident: 10584_CR15 publication-title: BMC Cancer doi: 10.1186/s12885-022-09876-8 – volume: 76 start-page: 5597 issue: 19 year: 2016 ident: 10584_CR35 publication-title: Cancer Res doi: 10.1158/0008-5472.CAN-16-0431 – volume: 31 start-page: 1497 issue: 6 year: 2013 ident: 10584_CR44 publication-title: World J Urol doi: 10.1007/s00345-013-1065-8 – volume: 43 start-page: 328 issue: 1 year: 2020 ident: 10584_CR7 publication-title: Oncol Rep – volume: 14 start-page: 3888 issue: 16 year: 2022 ident: 10584_CR21 publication-title: Cancers (Basel) doi: 10.3390/cancers14163888 – volume: 4 start-page: 176 issue: 3 year: 2008 ident: 10584_CR8 publication-title: Nat Chem Biol doi: 10.1038/nchembio.72 – ident: 10584_CR17 doi: 10.1007/s00432-022-04040-y – volume: 8 start-page: 503 year: 2020 ident: 10584_CR32 publication-title: Front Cell Dev Biol doi: 10.3389/fcell.2020.00503 – volume: 11 start-page: 46645 issue: 50 year: 2019 ident: 10584_CR6 publication-title: ACS Appl Mater Interfaces doi: 10.1021/acsami.9b19180 – volume: 1868 start-page: 118893 issue: 2 year: 2021 ident: 10584_CR30 publication-title: Biochim Biophys Acta Mol Cell Res doi: 10.1016/j.bbamcr.2020.118893 – volume: 10 start-page: 1523 issue: 1 year: 2019 ident: 10584_CR19 publication-title: Nat Commun doi: 10.1038/s41467-019-09234-6 – volume: 10 start-page: 549 issue: 8 year: 2020 ident: 10584_CR31 publication-title: Diagnostics (Basel) doi: 10.3390/diagnostics10080549 – volume: 18 start-page: 148 issue: 3 year: 2018 ident: 10584_CR34 publication-title: Nat Rev Cancer doi: 10.1038/nrc.2017.121 – volume: 12 start-page: 687975 year: 2021 ident: 10584_CR24 publication-title: Front Immunol doi: 10.3389/fimmu.2021.687975 – volume: 10 start-page: 155 issue: 3 year: 2009 ident: 10584_CR5 publication-title: Nat Rev Genet doi: 10.1038/nrg2521 – volume: 9 start-page: e107468 issue: 9 year: 2014 ident: 10584_CR28 publication-title: PLoS ONE doi: 10.1371/journal.pone.0107468 – volume: 34 start-page: e17045-e issue: 15_suppl year: 2016 ident: 10584_CR48 publication-title: J Clin Oncol doi: 10.1200/JCO.2016.34.15_suppl.e17045 – volume: 24 start-page: 8708 issue: 2 year: 2017 ident: 10584_CR38 publication-title: Can J Urol – volume: 22 start-page: 12628 issue: 23 year: 2021 ident: 10584_CR25 publication-title: Int J Mol Sci doi: 10.3390/ijms222312628 – volume: 1 start-page: 120 issue: 1 year: 2011 ident: 10584_CR36 publication-title: Am J Cancer Res – volume: 23 start-page: 847 issue: 5 year: 2014 ident: 10584_CR43 publication-title: Cancer Epidemiol Biomarkers Prev doi: 10.1158/1055-9965.EPI-13-1126 |
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Cuproptosis, an emerging form of programmed cell death, has recently been identified. However, the association between cuproptosis-related long... Cuproptosis, an emerging form of programmed cell death, has recently been identified. However, the association between cuproptosis-related long non-coding RNA... Background Cuproptosis, an emerging form of programmed cell death, has recently been identified. However, the association between cuproptosis-related long... BackgroundCuproptosis, an emerging form of programmed cell death, has recently been identified. However, the association between cuproptosis-related long... Abstract Background Cuproptosis, an emerging form of programmed cell death, has recently been identified. However, the association between cuproptosis-related... |
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| SubjectTerms | Algorithms Analysis Androgens Angiogenesis Apoptosis Biomedical and Life Sciences Biomedicine Cancer Cancer Research Cancer therapies Carcinoma Care and treatment Cell death Copper Cuproptosis Data mining Diagnosis Gene expression Gene mutations Genetic aspects Genomics Health aspects Health Promotion and Disease Prevention Humans Immune checkpoint Immunotherapy Learning algorithms LncRNA Machine learning Male Medical prognosis Medicine/Public Health Metastases Metastasis Methods Microenvironments Microsatellite Instability Mutation Nomograms Non-coding RNA Oncology Patients Point mutation Prognosis Prognostic signature Prostate Prostate cancer Prostate carcinoma Prostatic Neoplasms - genetics Risk groups RNA RNA, Long Noncoding - genetics Surgical Oncology Tumor Microenvironment Tumors |
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| Title | Novel cuproptosis-related long non-coding RNA signature to predict prognosis in prostate carcinoma |
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