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Cuproptosis-related lncRNA signature for prognostic prediction in patients with acute myeloid leukemia

BACKGROUND: Long non-coding RNAs (lncRNAs) have been reported to have a crucial impact on the pathogenesis of acute myeloid leukemia (AML). Cuproptosis, a copper-triggered modality of mitochondrial cell death, might serve as a promising therapeutic target for cancer treatment and clinical outcome pr...

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Autores principales: Zhu, Yidong, He, Jun, Li, Zihua, Yang, Wenzhong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9896718/
https://www.ncbi.nlm.nih.gov/pubmed/36737692
http://dx.doi.org/10.1186/s12859-023-05148-9
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author Zhu, Yidong
He, Jun
Li, Zihua
Yang, Wenzhong
author_facet Zhu, Yidong
He, Jun
Li, Zihua
Yang, Wenzhong
author_sort Zhu, Yidong
collection PubMed
description BACKGROUND: Long non-coding RNAs (lncRNAs) have been reported to have a crucial impact on the pathogenesis of acute myeloid leukemia (AML). Cuproptosis, a copper-triggered modality of mitochondrial cell death, might serve as a promising therapeutic target for cancer treatment and clinical outcome prediction. Nevertheless, the role of cuproptosis-related lncRNAs in AML is not fully understood. METHODS: The RNA sequencing data and demographic characteristics of AML patients were downloaded from The Cancer Genome Atlas database. Pearson correlation analysis, the least absolute shrinkage and selection operator algorithm, and univariable and multivariable Cox regression analyses were applied to identify the cuproptosis-related lncRNA signature and determine its feasibility for AML prognosis prediction. The performance of the proposed signature was evaluated via Kaplan–Meier survival analysis, receiver operating characteristic curves, and principal component analysis. Functional analysis was implemented to uncover the potential prognostic mechanisms. Additionally, quantitative real-time PCR (qRT-PCR) was employed to validate the expression of the prognostic lncRNAs in AML samples. RESULTS: A signature consisting of seven cuproptosis-related lncRNAs (namely NFE4, LINC00989, LINC02062, AC006460.2, AL353796.1, PSMB8-AS1, and AC000120.1) was proposed. Multivariable cox regression analysis revealed that the proposed signature was an independent prognostic factor for AML. Notably, the nomogram based on this signature showed excellent accuracy in predicting the 1-, 3-, and 5-year survival (area under curve = 0.846, 0.801, and 0.895, respectively). Functional analysis results suggested the existence of a significant association between the prognostic signature and immune-related pathways. The expression pattern of the lncRNAs was validated in AML samples. CONCLUSION: Collectively, we constructed a prediction model based on seven cuproptosis-related lncRNAs for AML prognosis. The obtained risk score may reveal the immunotherapy response in patients with this disease. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05148-9.
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spelling pubmed-98967182023-02-04 Cuproptosis-related lncRNA signature for prognostic prediction in patients with acute myeloid leukemia Zhu, Yidong He, Jun Li, Zihua Yang, Wenzhong BMC Bioinformatics Research BACKGROUND: Long non-coding RNAs (lncRNAs) have been reported to have a crucial impact on the pathogenesis of acute myeloid leukemia (AML). Cuproptosis, a copper-triggered modality of mitochondrial cell death, might serve as a promising therapeutic target for cancer treatment and clinical outcome prediction. Nevertheless, the role of cuproptosis-related lncRNAs in AML is not fully understood. METHODS: The RNA sequencing data and demographic characteristics of AML patients were downloaded from The Cancer Genome Atlas database. Pearson correlation analysis, the least absolute shrinkage and selection operator algorithm, and univariable and multivariable Cox regression analyses were applied to identify the cuproptosis-related lncRNA signature and determine its feasibility for AML prognosis prediction. The performance of the proposed signature was evaluated via Kaplan–Meier survival analysis, receiver operating characteristic curves, and principal component analysis. Functional analysis was implemented to uncover the potential prognostic mechanisms. Additionally, quantitative real-time PCR (qRT-PCR) was employed to validate the expression of the prognostic lncRNAs in AML samples. RESULTS: A signature consisting of seven cuproptosis-related lncRNAs (namely NFE4, LINC00989, LINC02062, AC006460.2, AL353796.1, PSMB8-AS1, and AC000120.1) was proposed. Multivariable cox regression analysis revealed that the proposed signature was an independent prognostic factor for AML. Notably, the nomogram based on this signature showed excellent accuracy in predicting the 1-, 3-, and 5-year survival (area under curve = 0.846, 0.801, and 0.895, respectively). Functional analysis results suggested the existence of a significant association between the prognostic signature and immune-related pathways. The expression pattern of the lncRNAs was validated in AML samples. CONCLUSION: Collectively, we constructed a prediction model based on seven cuproptosis-related lncRNAs for AML prognosis. The obtained risk score may reveal the immunotherapy response in patients with this disease. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05148-9. BioMed Central 2023-02-03 /pmc/articles/PMC9896718/ /pubmed/36737692 http://dx.doi.org/10.1186/s12859-023-05148-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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.
spellingShingle Research
Zhu, Yidong
He, Jun
Li, Zihua
Yang, Wenzhong
Cuproptosis-related lncRNA signature for prognostic prediction in patients with acute myeloid leukemia
title Cuproptosis-related lncRNA signature for prognostic prediction in patients with acute myeloid leukemia
title_full Cuproptosis-related lncRNA signature for prognostic prediction in patients with acute myeloid leukemia
title_fullStr Cuproptosis-related lncRNA signature for prognostic prediction in patients with acute myeloid leukemia
title_full_unstemmed Cuproptosis-related lncRNA signature for prognostic prediction in patients with acute myeloid leukemia
title_short Cuproptosis-related lncRNA signature for prognostic prediction in patients with acute myeloid leukemia
title_sort cuproptosis-related lncrna signature for prognostic prediction in patients with acute myeloid leukemia
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9896718/
https://www.ncbi.nlm.nih.gov/pubmed/36737692
http://dx.doi.org/10.1186/s12859-023-05148-9
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