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A novel DNA methylation-related gene signature for the prediction of overall survival and immune characteristics of ovarian cancer patients

BACKGROUND: Ovarian cancer (OC) is one of the most life-threatening cancers affecting women worldwide. Recent studies have shown that the DNA methylation state can be used in the diagnosis, treatment and prognosis prediction of diseases. Meanwhile, it has been reported that the DNA methylation state...

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Autores principales: Wang, Sixue, Fu, Jie, Fang, Xiaoling
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10053775/
https://www.ncbi.nlm.nih.gov/pubmed/36978087
http://dx.doi.org/10.1186/s13048-023-01142-0
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author Wang, Sixue
Fu, Jie
Fang, Xiaoling
author_facet Wang, Sixue
Fu, Jie
Fang, Xiaoling
author_sort Wang, Sixue
collection PubMed
description BACKGROUND: Ovarian cancer (OC) is one of the most life-threatening cancers affecting women worldwide. Recent studies have shown that the DNA methylation state can be used in the diagnosis, treatment and prognosis prediction of diseases. Meanwhile, it has been reported that the DNA methylation state can affect the function of immune cells. However, whether DNA methylation-related genes can be used for prognosis and immune response prediction in OC remains unclear. METHODS: In this study, DNA methylation-related genes in OC were identified by an integrated analysis of DNA methylation and transcriptome data. Prognostic values of the DNA methylation-related genes were investigated through least absolute shrinkage and selection operator (LASSO) and Cox progression analyses. Immune characteristics were investigated by CIBERSORT, correlation analysis and weighted gene co-expression network analysis (WGCNA). RESULTS: Twelve prognostic genes (CA2, CD3G, HABP2, KCTD14, PI3, SERPINB5, SLAMF7, SLC9A2, STC2, TBP, TREML2 and TRIM27) were identified and a risk score signature and a nomogram based on prognostic genes and clinicopathological features were constructed for the survival prediction of OC patients in the training and two validation cohorts. Subsequently, the differences in the immune landscape between the high- and low-risk score groups were systematically investigated. CONCLUSIONS: Taken together, our study explored a novel efficient risk score signature and a nomogram for the survival prediction of OC patients. In addition, the differences of the immune characteristics between the two risk groups were clarified preliminarily, which will guide the further exploration of synergistic targets to improve the efficacy of immunotherapy in OC patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13048-023-01142-0.
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spelling pubmed-100537752023-03-30 A novel DNA methylation-related gene signature for the prediction of overall survival and immune characteristics of ovarian cancer patients Wang, Sixue Fu, Jie Fang, Xiaoling J Ovarian Res Research BACKGROUND: Ovarian cancer (OC) is one of the most life-threatening cancers affecting women worldwide. Recent studies have shown that the DNA methylation state can be used in the diagnosis, treatment and prognosis prediction of diseases. Meanwhile, it has been reported that the DNA methylation state can affect the function of immune cells. However, whether DNA methylation-related genes can be used for prognosis and immune response prediction in OC remains unclear. METHODS: In this study, DNA methylation-related genes in OC were identified by an integrated analysis of DNA methylation and transcriptome data. Prognostic values of the DNA methylation-related genes were investigated through least absolute shrinkage and selection operator (LASSO) and Cox progression analyses. Immune characteristics were investigated by CIBERSORT, correlation analysis and weighted gene co-expression network analysis (WGCNA). RESULTS: Twelve prognostic genes (CA2, CD3G, HABP2, KCTD14, PI3, SERPINB5, SLAMF7, SLC9A2, STC2, TBP, TREML2 and TRIM27) were identified and a risk score signature and a nomogram based on prognostic genes and clinicopathological features were constructed for the survival prediction of OC patients in the training and two validation cohorts. Subsequently, the differences in the immune landscape between the high- and low-risk score groups were systematically investigated. CONCLUSIONS: Taken together, our study explored a novel efficient risk score signature and a nomogram for the survival prediction of OC patients. In addition, the differences of the immune characteristics between the two risk groups were clarified preliminarily, which will guide the further exploration of synergistic targets to improve the efficacy of immunotherapy in OC patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13048-023-01142-0. BioMed Central 2023-03-29 /pmc/articles/PMC10053775/ /pubmed/36978087 http://dx.doi.org/10.1186/s13048-023-01142-0 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
Wang, Sixue
Fu, Jie
Fang, Xiaoling
A novel DNA methylation-related gene signature for the prediction of overall survival and immune characteristics of ovarian cancer patients
title A novel DNA methylation-related gene signature for the prediction of overall survival and immune characteristics of ovarian cancer patients
title_full A novel DNA methylation-related gene signature for the prediction of overall survival and immune characteristics of ovarian cancer patients
title_fullStr A novel DNA methylation-related gene signature for the prediction of overall survival and immune characteristics of ovarian cancer patients
title_full_unstemmed A novel DNA methylation-related gene signature for the prediction of overall survival and immune characteristics of ovarian cancer patients
title_short A novel DNA methylation-related gene signature for the prediction of overall survival and immune characteristics of ovarian cancer patients
title_sort novel dna methylation-related gene signature for the prediction of overall survival and immune characteristics of ovarian cancer patients
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10053775/
https://www.ncbi.nlm.nih.gov/pubmed/36978087
http://dx.doi.org/10.1186/s13048-023-01142-0
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