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Identification of fatty acid signature to predict prognosis and guide clinical therapy in patients with ovarian cancer

High-grade serous ovarian cancer (HGSOC) is a heterogeneous cancer characterized by high relapse rate. Approximately 80% of women are diagnosed with late-stage disease, and 15–25% of patients experience primary treatment resistance. Ovarian cancer brings tremendous suffering and is the most malignan...

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Autores principales: Cao, Tiefeng, Dong, Jiaqi, Huang, Jiaming, Tang, Zihao, Shen, Huimin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9577003/
https://www.ncbi.nlm.nih.gov/pubmed/36267966
http://dx.doi.org/10.3389/fonc.2022.979565
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author Cao, Tiefeng
Dong, Jiaqi
Huang, Jiaming
Tang, Zihao
Shen, Huimin
author_facet Cao, Tiefeng
Dong, Jiaqi
Huang, Jiaming
Tang, Zihao
Shen, Huimin
author_sort Cao, Tiefeng
collection PubMed
description High-grade serous ovarian cancer (HGSOC) is a heterogeneous cancer characterized by high relapse rate. Approximately 80% of women are diagnosed with late-stage disease, and 15–25% of patients experience primary treatment resistance. Ovarian cancer brings tremendous suffering and is the most malignant type in all gynecologic malignancies. Metabolic reprogramming in tumor microenvironment (TME), especially fatty acid metabolism, has been identified to play a crucial role in cancer prognosis. Yet, the underlying mechanism of fatty acid metabolism on ovarian cancer progression is severely understudied. Recently, studies have demonstrated the role of fatty acid metabolism reprogramming in immune cells, but their roles on cancer cell metastasis and cancer immunotherapy response are poorly characterized. Here, we reported that the fatty acid–related genes are aberrantly varied between ovarian cancer and normal samples. Using samples in publicly databases and bio-informatic analyses with fatty acid–related genes, we disentangled that cancer cases can be classified into high- and low-risk groups related with prognosis. Furthermore, the nomogram model was constructed to predict the overall survival. Additionally, we reported that different immune cells infiltration was presented between groups, and immunotherapy response differed in two groups. Results showed that our signature may have good prediction value on immunotherapy efficacy, especially for anti–PD-1 and anti–CTLA-4. Our study systematically marked the critical association between cancer immunity in TME and fatty acid metabolism, and bridged immune phenotype and metabolism programming in tumors, thereby constructed the metabolic-related prognostic model and help to understand the underlying mechanism of immunotherapy response.
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spelling pubmed-95770032022-10-19 Identification of fatty acid signature to predict prognosis and guide clinical therapy in patients with ovarian cancer Cao, Tiefeng Dong, Jiaqi Huang, Jiaming Tang, Zihao Shen, Huimin Front Oncol Oncology High-grade serous ovarian cancer (HGSOC) is a heterogeneous cancer characterized by high relapse rate. Approximately 80% of women are diagnosed with late-stage disease, and 15–25% of patients experience primary treatment resistance. Ovarian cancer brings tremendous suffering and is the most malignant type in all gynecologic malignancies. Metabolic reprogramming in tumor microenvironment (TME), especially fatty acid metabolism, has been identified to play a crucial role in cancer prognosis. Yet, the underlying mechanism of fatty acid metabolism on ovarian cancer progression is severely understudied. Recently, studies have demonstrated the role of fatty acid metabolism reprogramming in immune cells, but their roles on cancer cell metastasis and cancer immunotherapy response are poorly characterized. Here, we reported that the fatty acid–related genes are aberrantly varied between ovarian cancer and normal samples. Using samples in publicly databases and bio-informatic analyses with fatty acid–related genes, we disentangled that cancer cases can be classified into high- and low-risk groups related with prognosis. Furthermore, the nomogram model was constructed to predict the overall survival. Additionally, we reported that different immune cells infiltration was presented between groups, and immunotherapy response differed in two groups. Results showed that our signature may have good prediction value on immunotherapy efficacy, especially for anti–PD-1 and anti–CTLA-4. Our study systematically marked the critical association between cancer immunity in TME and fatty acid metabolism, and bridged immune phenotype and metabolism programming in tumors, thereby constructed the metabolic-related prognostic model and help to understand the underlying mechanism of immunotherapy response. Frontiers Media S.A. 2022-10-04 /pmc/articles/PMC9577003/ /pubmed/36267966 http://dx.doi.org/10.3389/fonc.2022.979565 Text en Copyright © 2022 Cao, Dong, Huang, Tang and Shen 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 Oncology
Cao, Tiefeng
Dong, Jiaqi
Huang, Jiaming
Tang, Zihao
Shen, Huimin
Identification of fatty acid signature to predict prognosis and guide clinical therapy in patients with ovarian cancer
title Identification of fatty acid signature to predict prognosis and guide clinical therapy in patients with ovarian cancer
title_full Identification of fatty acid signature to predict prognosis and guide clinical therapy in patients with ovarian cancer
title_fullStr Identification of fatty acid signature to predict prognosis and guide clinical therapy in patients with ovarian cancer
title_full_unstemmed Identification of fatty acid signature to predict prognosis and guide clinical therapy in patients with ovarian cancer
title_short Identification of fatty acid signature to predict prognosis and guide clinical therapy in patients with ovarian cancer
title_sort identification of fatty acid signature to predict prognosis and guide clinical therapy in patients with ovarian cancer
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9577003/
https://www.ncbi.nlm.nih.gov/pubmed/36267966
http://dx.doi.org/10.3389/fonc.2022.979565
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