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Identification and Verification of Necroptosis-Related Gene Signature With Prognosis and Tumor Immune Microenvironment in Ovarian Cancer

Ovarian cancer is the most lethal heterogeneous disease among gynecological tumors with a poor prognosis. Necroptosis, the most studied way of death in recent years, is different from apoptosis and pyroptosis. It is a kind of regulated programmed cell death and has been shown to be closely related t...

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Autores principales: Wang, Zitao, Chen, Ganhong, Dai, Fangfang, Liu, Shiyi, Hu, Wei, Cheng, Yanxiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9265217/
https://www.ncbi.nlm.nih.gov/pubmed/35812403
http://dx.doi.org/10.3389/fimmu.2022.894718
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author Wang, Zitao
Chen, Ganhong
Dai, Fangfang
Liu, Shiyi
Hu, Wei
Cheng, Yanxiang
author_facet Wang, Zitao
Chen, Ganhong
Dai, Fangfang
Liu, Shiyi
Hu, Wei
Cheng, Yanxiang
author_sort Wang, Zitao
collection PubMed
description Ovarian cancer is the most lethal heterogeneous disease among gynecological tumors with a poor prognosis. Necroptosis, the most studied way of death in recent years, is different from apoptosis and pyroptosis. It is a kind of regulated programmed cell death and has been shown to be closely related to a variety of tumors. However, the expression and prognosis of necroptosis-related genes in ovarian cancer are still unclear. Our study therefore firstly identified the expression profiles of necroptosis-related genes in normal and ovarian cancer tissues. Next, based on differentially expressed necroptosis-related genes, we clustered ovarian cancer patients into two subtypes and performed survival analysis. Subsequently, we constructed a risk model consisting of 5 genes by LASSO regression analysis based on the differentially expressed genes in the two subtypes, and confirmed the strong prognostic ability of the model and its potential as an independent risk factor via survival analysis and independent risk factor analysis. Based on this risk model, patients were divided into high and low risk groups. By exploring differentially expressed genes, enrichment functions and tumor immune microenvironment in patients in high and low risk groups, the results showed that patients in the low risk group were significantly enriched in immune signaling pathways. Besides, immune cells content, immune function activity was significantly better than the high-risk group. Eventually, we also investigated the sensitivity of patients with different risk groups to ICB immunotherapy and chemotherapy drugs. In conclusion, the risk model could effectively predict the survival and prognosis of patients, and explore the tumor microenvironment status of ovarian cancer patients to a certain extent, and provide promising and novel molecular markers for clinical diagnosis, individualized treatment and immunotherapy of patients.
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spelling pubmed-92652172022-07-09 Identification and Verification of Necroptosis-Related Gene Signature With Prognosis and Tumor Immune Microenvironment in Ovarian Cancer Wang, Zitao Chen, Ganhong Dai, Fangfang Liu, Shiyi Hu, Wei Cheng, Yanxiang Front Immunol Immunology Ovarian cancer is the most lethal heterogeneous disease among gynecological tumors with a poor prognosis. Necroptosis, the most studied way of death in recent years, is different from apoptosis and pyroptosis. It is a kind of regulated programmed cell death and has been shown to be closely related to a variety of tumors. However, the expression and prognosis of necroptosis-related genes in ovarian cancer are still unclear. Our study therefore firstly identified the expression profiles of necroptosis-related genes in normal and ovarian cancer tissues. Next, based on differentially expressed necroptosis-related genes, we clustered ovarian cancer patients into two subtypes and performed survival analysis. Subsequently, we constructed a risk model consisting of 5 genes by LASSO regression analysis based on the differentially expressed genes in the two subtypes, and confirmed the strong prognostic ability of the model and its potential as an independent risk factor via survival analysis and independent risk factor analysis. Based on this risk model, patients were divided into high and low risk groups. By exploring differentially expressed genes, enrichment functions and tumor immune microenvironment in patients in high and low risk groups, the results showed that patients in the low risk group were significantly enriched in immune signaling pathways. Besides, immune cells content, immune function activity was significantly better than the high-risk group. Eventually, we also investigated the sensitivity of patients with different risk groups to ICB immunotherapy and chemotherapy drugs. In conclusion, the risk model could effectively predict the survival and prognosis of patients, and explore the tumor microenvironment status of ovarian cancer patients to a certain extent, and provide promising and novel molecular markers for clinical diagnosis, individualized treatment and immunotherapy of patients. Frontiers Media S.A. 2022-06-24 /pmc/articles/PMC9265217/ /pubmed/35812403 http://dx.doi.org/10.3389/fimmu.2022.894718 Text en Copyright © 2022 Wang, Chen, Dai, Liu, Hu and Cheng 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 Immunology
Wang, Zitao
Chen, Ganhong
Dai, Fangfang
Liu, Shiyi
Hu, Wei
Cheng, Yanxiang
Identification and Verification of Necroptosis-Related Gene Signature With Prognosis and Tumor Immune Microenvironment in Ovarian Cancer
title Identification and Verification of Necroptosis-Related Gene Signature With Prognosis and Tumor Immune Microenvironment in Ovarian Cancer
title_full Identification and Verification of Necroptosis-Related Gene Signature With Prognosis and Tumor Immune Microenvironment in Ovarian Cancer
title_fullStr Identification and Verification of Necroptosis-Related Gene Signature With Prognosis and Tumor Immune Microenvironment in Ovarian Cancer
title_full_unstemmed Identification and Verification of Necroptosis-Related Gene Signature With Prognosis and Tumor Immune Microenvironment in Ovarian Cancer
title_short Identification and Verification of Necroptosis-Related Gene Signature With Prognosis and Tumor Immune Microenvironment in Ovarian Cancer
title_sort identification and verification of necroptosis-related gene signature with prognosis and tumor immune microenvironment in ovarian cancer
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9265217/
https://www.ncbi.nlm.nih.gov/pubmed/35812403
http://dx.doi.org/10.3389/fimmu.2022.894718
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