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Developing four cuproptosis-related lncRNAs signature to predict prognosis and immune activity in ovarian cancer
BACKGROUND: There has been a recent discovery of a new type of cell death produced by copper-iron ions, called Cuproptosis (copper death). The purpose of this study was to identify LncRNA signatures associated with Cuproptosis in ovarian cancer that could be used as prognostic indicators. METHODS: R...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10150549/ https://www.ncbi.nlm.nih.gov/pubmed/37122030 http://dx.doi.org/10.1186/s13048-023-01165-7 |
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author | Liu, Li Wang, Qing Zhou, Jia-Yun Zhang, Bei |
author_facet | Liu, Li Wang, Qing Zhou, Jia-Yun Zhang, Bei |
author_sort | Liu, Li |
collection | PubMed |
description | BACKGROUND: There has been a recent discovery of a new type of cell death produced by copper-iron ions, called Cuproptosis (copper death). The purpose of this study was to identify LncRNA signatures associated with Cuproptosis in ovarian cancer that could be used as prognostic indicators. METHODS: RNA sequencing (RNA-seq) profiles with clinicopathological data from TCGA database were used to select prognostic CRLs and then constructed prognostic risk model using multivariate regression analysis and LASSO algorithms. An independent dataset from GEO database was used to validate the prognostic performance. Combined with clinical factors, we further constructed a prognostic nomogram. In addition, tumor immune microenvironment, somatic mutation and drug sensitivity were analyzed using ssGSEA, GSVA, ESTIMATE and CIBERSORT algorithms. RESULT: A total of 129 CRLs were selected whose expression levels were significantly related to expression levels of 10 cuproptosis-related genes. The univariate Cox regression analysis showed that 12 CRLs were associated with overall survival (OS). Using LASSO algorithms and multivariate regression analysis, we constructed a four-CRLs prognostic signature in the training dataset. Patients in the training dataset could be classified into high- or low-risk subgroups with significantly different OS (log-rank p < 0.001). The prognostic performance was confirmed in TCGA-OC cohort (log-rank p < 0.001) and an independent GEO cohort (log-rank p = 0.023). Multivariate cox regression analysis proved the four-CRLs signature was an independent prognostic factor for OC. Additionally, different risk subtypes showed significantly different levels of immune cells, signal pathways, and drug response. CONCLUSION: We established a prognostic signature based on cuproptosis-related lncRNAs for OC patients, which will be of great value in predicting the prognosis patients and may provide a new perspective for research and individualized treatment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13048-023-01165-7. |
format | Online Article Text |
id | pubmed-10150549 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-101505492023-05-02 Developing four cuproptosis-related lncRNAs signature to predict prognosis and immune activity in ovarian cancer Liu, Li Wang, Qing Zhou, Jia-Yun Zhang, Bei J Ovarian Res Research BACKGROUND: There has been a recent discovery of a new type of cell death produced by copper-iron ions, called Cuproptosis (copper death). The purpose of this study was to identify LncRNA signatures associated with Cuproptosis in ovarian cancer that could be used as prognostic indicators. METHODS: RNA sequencing (RNA-seq) profiles with clinicopathological data from TCGA database were used to select prognostic CRLs and then constructed prognostic risk model using multivariate regression analysis and LASSO algorithms. An independent dataset from GEO database was used to validate the prognostic performance. Combined with clinical factors, we further constructed a prognostic nomogram. In addition, tumor immune microenvironment, somatic mutation and drug sensitivity were analyzed using ssGSEA, GSVA, ESTIMATE and CIBERSORT algorithms. RESULT: A total of 129 CRLs were selected whose expression levels were significantly related to expression levels of 10 cuproptosis-related genes. The univariate Cox regression analysis showed that 12 CRLs were associated with overall survival (OS). Using LASSO algorithms and multivariate regression analysis, we constructed a four-CRLs prognostic signature in the training dataset. Patients in the training dataset could be classified into high- or low-risk subgroups with significantly different OS (log-rank p < 0.001). The prognostic performance was confirmed in TCGA-OC cohort (log-rank p < 0.001) and an independent GEO cohort (log-rank p = 0.023). Multivariate cox regression analysis proved the four-CRLs signature was an independent prognostic factor for OC. Additionally, different risk subtypes showed significantly different levels of immune cells, signal pathways, and drug response. CONCLUSION: We established a prognostic signature based on cuproptosis-related lncRNAs for OC patients, which will be of great value in predicting the prognosis patients and may provide a new perspective for research and individualized treatment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13048-023-01165-7. BioMed Central 2023-04-30 /pmc/articles/PMC10150549/ /pubmed/37122030 http://dx.doi.org/10.1186/s13048-023-01165-7 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 Liu, Li Wang, Qing Zhou, Jia-Yun Zhang, Bei Developing four cuproptosis-related lncRNAs signature to predict prognosis and immune activity in ovarian cancer |
title | Developing four cuproptosis-related lncRNAs signature to predict prognosis and immune activity in ovarian cancer |
title_full | Developing four cuproptosis-related lncRNAs signature to predict prognosis and immune activity in ovarian cancer |
title_fullStr | Developing four cuproptosis-related lncRNAs signature to predict prognosis and immune activity in ovarian cancer |
title_full_unstemmed | Developing four cuproptosis-related lncRNAs signature to predict prognosis and immune activity in ovarian cancer |
title_short | Developing four cuproptosis-related lncRNAs signature to predict prognosis and immune activity in ovarian cancer |
title_sort | developing four cuproptosis-related lncrnas signature to predict prognosis and immune activity in ovarian cancer |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10150549/ https://www.ncbi.nlm.nih.gov/pubmed/37122030 http://dx.doi.org/10.1186/s13048-023-01165-7 |
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