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Identification of copper metabolism-related subtypes and establishment of the prognostic model in ovarian cancer

BACKGROUND: Ovarian cancer (OC) is one of the most common and most malignant gynecological malignancies in gynecology. On the other hand, dysregulation of copper metabolism (CM) is closely associated with tumourigenesis and progression. Here, we investigated the impact of genes associated with coppe...

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Autores principales: Zhao, Songyun, Zhang, Xin, Gao, Feng, Chi, Hao, Zhang, Jinhao, Xia, Zhijia, Cheng, Chao, Liu, Jinhui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10025496/
https://www.ncbi.nlm.nih.gov/pubmed/36950684
http://dx.doi.org/10.3389/fendo.2023.1145797
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author Zhao, Songyun
Zhang, Xin
Gao, Feng
Chi, Hao
Zhang, Jinhao
Xia, Zhijia
Cheng, Chao
Liu, Jinhui
author_facet Zhao, Songyun
Zhang, Xin
Gao, Feng
Chi, Hao
Zhang, Jinhao
Xia, Zhijia
Cheng, Chao
Liu, Jinhui
author_sort Zhao, Songyun
collection PubMed
description BACKGROUND: Ovarian cancer (OC) is one of the most common and most malignant gynecological malignancies in gynecology. On the other hand, dysregulation of copper metabolism (CM) is closely associated with tumourigenesis and progression. Here, we investigated the impact of genes associated with copper metabolism (CMRGs) on the prognosis of OC, discovered various CM clusters, and built a risk model to evaluate patient prognosis, immunological features, and therapy response. METHODS: 15 CMRGs affecting the prognosis of OC patients were identified in The Cancer Genome Atlas (TCGA). Consensus Clustering was used to identify two CM clusters. lasso-cox methods were used to establish the copper metabolism-related gene prognostic signature (CMRGPS) based on differentially expressed genes in the two clusters. The GSE63885 cohort was used as an external validation cohort. Expression of CM risk score-associated genes was verified by single-cell sequencing and quantitative real-time PCR (qRT-PCR). Nomograms were used to visually depict the clinical value of CMRGPS. Differences in clinical traits, immune cell infiltration, and tumor mutational load (TMB) between risk groups were also extensively examined. Tumour Immune Dysfunction and Rejection (TIDE) and Immune Phenotype Score (IPS) were used to validate whether CMRGPS could predict response to immunotherapy in OC patients. RESULTS: In the TCGA and GSE63885 cohorts, we identified two CM clusters that differed significantly in terms of overall survival (OS) and tumor microenvironment. We then created a CMRGPS containing 11 genes to predict overall survival and confirmed its reliable predictive power for OC patients. The expression of CM risk score-related genes was validated by qRT-PCR. Patients with OC were divided into low-risk (LR) and high-risk (HR) groups based on the median CM risk score, with better survival in the LR group. The 5-year AUC value reached 0.74. Enrichment analysis showed that the LR group was associated with tumor immune-related pathways. The results of TIDE and IPS showed a better response to immunotherapy in the LR group. CONCLUSION: Our study, therefore, provides a valuable tool to further guide clinical management and tailor the treatment of patients with OC, offering new insights into individualized treatment.
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spelling pubmed-100254962023-03-21 Identification of copper metabolism-related subtypes and establishment of the prognostic model in ovarian cancer Zhao, Songyun Zhang, Xin Gao, Feng Chi, Hao Zhang, Jinhao Xia, Zhijia Cheng, Chao Liu, Jinhui Front Endocrinol (Lausanne) Endocrinology BACKGROUND: Ovarian cancer (OC) is one of the most common and most malignant gynecological malignancies in gynecology. On the other hand, dysregulation of copper metabolism (CM) is closely associated with tumourigenesis and progression. Here, we investigated the impact of genes associated with copper metabolism (CMRGs) on the prognosis of OC, discovered various CM clusters, and built a risk model to evaluate patient prognosis, immunological features, and therapy response. METHODS: 15 CMRGs affecting the prognosis of OC patients were identified in The Cancer Genome Atlas (TCGA). Consensus Clustering was used to identify two CM clusters. lasso-cox methods were used to establish the copper metabolism-related gene prognostic signature (CMRGPS) based on differentially expressed genes in the two clusters. The GSE63885 cohort was used as an external validation cohort. Expression of CM risk score-associated genes was verified by single-cell sequencing and quantitative real-time PCR (qRT-PCR). Nomograms were used to visually depict the clinical value of CMRGPS. Differences in clinical traits, immune cell infiltration, and tumor mutational load (TMB) between risk groups were also extensively examined. Tumour Immune Dysfunction and Rejection (TIDE) and Immune Phenotype Score (IPS) were used to validate whether CMRGPS could predict response to immunotherapy in OC patients. RESULTS: In the TCGA and GSE63885 cohorts, we identified two CM clusters that differed significantly in terms of overall survival (OS) and tumor microenvironment. We then created a CMRGPS containing 11 genes to predict overall survival and confirmed its reliable predictive power for OC patients. The expression of CM risk score-related genes was validated by qRT-PCR. Patients with OC were divided into low-risk (LR) and high-risk (HR) groups based on the median CM risk score, with better survival in the LR group. The 5-year AUC value reached 0.74. Enrichment analysis showed that the LR group was associated with tumor immune-related pathways. The results of TIDE and IPS showed a better response to immunotherapy in the LR group. CONCLUSION: Our study, therefore, provides a valuable tool to further guide clinical management and tailor the treatment of patients with OC, offering new insights into individualized treatment. Frontiers Media S.A. 2023-03-06 /pmc/articles/PMC10025496/ /pubmed/36950684 http://dx.doi.org/10.3389/fendo.2023.1145797 Text en Copyright © 2023 Zhao, Zhang, Gao, Chi, Zhang, Xia, Cheng and Liu 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 Endocrinology
Zhao, Songyun
Zhang, Xin
Gao, Feng
Chi, Hao
Zhang, Jinhao
Xia, Zhijia
Cheng, Chao
Liu, Jinhui
Identification of copper metabolism-related subtypes and establishment of the prognostic model in ovarian cancer
title Identification of copper metabolism-related subtypes and establishment of the prognostic model in ovarian cancer
title_full Identification of copper metabolism-related subtypes and establishment of the prognostic model in ovarian cancer
title_fullStr Identification of copper metabolism-related subtypes and establishment of the prognostic model in ovarian cancer
title_full_unstemmed Identification of copper metabolism-related subtypes and establishment of the prognostic model in ovarian cancer
title_short Identification of copper metabolism-related subtypes and establishment of the prognostic model in ovarian cancer
title_sort identification of copper metabolism-related subtypes and establishment of the prognostic model in ovarian cancer
topic Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10025496/
https://www.ncbi.nlm.nih.gov/pubmed/36950684
http://dx.doi.org/10.3389/fendo.2023.1145797
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