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Identification of cuproptosis and immune-related gene prognostic signature in lung adenocarcinoma

BACKGROUND: Cuproptosis is a novel form of programmed cell death that differs from other types such as pyroptosis, ferroptosis, and autophagy. It is a promising new target for cancer therapy. Additionally, immune-related genes play a crucial role in cancer progression and patient prognosis. Therefor...

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Autores principales: Zhang, Wentao, Qu, Haizeng, Ma, Xiaoqing, Li, Liang, Wei, Yanjun, Wang, Ye, Zeng, Renya, Nie, Yuanliu, Zhang, Chenggui, Yin, Ke, Zhou, Fengge, Yang, Zhe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10445162/
https://www.ncbi.nlm.nih.gov/pubmed/37622116
http://dx.doi.org/10.3389/fimmu.2023.1179742
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author Zhang, Wentao
Qu, Haizeng
Ma, Xiaoqing
Li, Liang
Wei, Yanjun
Wang, Ye
Zeng, Renya
Nie, Yuanliu
Zhang, Chenggui
Yin, Ke
Zhou, Fengge
Yang, Zhe
author_facet Zhang, Wentao
Qu, Haizeng
Ma, Xiaoqing
Li, Liang
Wei, Yanjun
Wang, Ye
Zeng, Renya
Nie, Yuanliu
Zhang, Chenggui
Yin, Ke
Zhou, Fengge
Yang, Zhe
author_sort Zhang, Wentao
collection PubMed
description BACKGROUND: Cuproptosis is a novel form of programmed cell death that differs from other types such as pyroptosis, ferroptosis, and autophagy. It is a promising new target for cancer therapy. Additionally, immune-related genes play a crucial role in cancer progression and patient prognosis. Therefore, our study aimed to create a survival prediction model for lung adenocarcinoma patients based on cuproptosis and immune-related genes. This model can be utilized to enhance personalized treatment for patients. METHODS: RNA sequencing (RNA-seq) data of lung adenocarcinoma (LUAD) patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The levels of immune cell infiltration in the GSE68465 cohort were determined using gene set variation analysis (GSVA), and immune-related genes (IRGs) were identified using weighted gene coexpression network analysis (WGCNA). Additionally, cuproptosis-related genes (CRGs) were identified using unsupervised clustering. Univariate COX regression analysis and least absolute shrinkage selection operator (LASSO) regression analysis were performed to develop a risk prognostic model for cuproptosis and immune-related genes (CIRGs), which was subsequently validated. Various algorithms were utilized to explore the relationship between risk scores and immune infiltration levels, and model genes were analyzed based on single-cell sequencing. Finally, the expression of signature genes was confirmed through quantitative real-time PCR (qRT-PCR), immunohistochemistry (IHC), and Western blotting (WB). RESULTS: We have identified 5 Oncogenic Driver Genes namely CD79B, PEBP1, PTK2B, STXBP1, and ZNF671, and developed proportional hazards regression models. The results of the study indicate significantly reduced survival rates in both the training and validation sets among the high-risk group. Additionally, the high-risk group displayed lower levels of immune cell infiltration and expression of immune checkpoint compared to the low-risk group.
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spelling pubmed-104451622023-08-24 Identification of cuproptosis and immune-related gene prognostic signature in lung adenocarcinoma Zhang, Wentao Qu, Haizeng Ma, Xiaoqing Li, Liang Wei, Yanjun Wang, Ye Zeng, Renya Nie, Yuanliu Zhang, Chenggui Yin, Ke Zhou, Fengge Yang, Zhe Front Immunol Immunology BACKGROUND: Cuproptosis is a novel form of programmed cell death that differs from other types such as pyroptosis, ferroptosis, and autophagy. It is a promising new target for cancer therapy. Additionally, immune-related genes play a crucial role in cancer progression and patient prognosis. Therefore, our study aimed to create a survival prediction model for lung adenocarcinoma patients based on cuproptosis and immune-related genes. This model can be utilized to enhance personalized treatment for patients. METHODS: RNA sequencing (RNA-seq) data of lung adenocarcinoma (LUAD) patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The levels of immune cell infiltration in the GSE68465 cohort were determined using gene set variation analysis (GSVA), and immune-related genes (IRGs) were identified using weighted gene coexpression network analysis (WGCNA). Additionally, cuproptosis-related genes (CRGs) were identified using unsupervised clustering. Univariate COX regression analysis and least absolute shrinkage selection operator (LASSO) regression analysis were performed to develop a risk prognostic model for cuproptosis and immune-related genes (CIRGs), which was subsequently validated. Various algorithms were utilized to explore the relationship between risk scores and immune infiltration levels, and model genes were analyzed based on single-cell sequencing. Finally, the expression of signature genes was confirmed through quantitative real-time PCR (qRT-PCR), immunohistochemistry (IHC), and Western blotting (WB). RESULTS: We have identified 5 Oncogenic Driver Genes namely CD79B, PEBP1, PTK2B, STXBP1, and ZNF671, and developed proportional hazards regression models. The results of the study indicate significantly reduced survival rates in both the training and validation sets among the high-risk group. Additionally, the high-risk group displayed lower levels of immune cell infiltration and expression of immune checkpoint compared to the low-risk group. Frontiers Media S.A. 2023-08-09 /pmc/articles/PMC10445162/ /pubmed/37622116 http://dx.doi.org/10.3389/fimmu.2023.1179742 Text en Copyright © 2023 Zhang, Qu, Ma, Li, Wei, Wang, Zeng, Nie, Zhang, Yin, Zhou and Yang 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
Zhang, Wentao
Qu, Haizeng
Ma, Xiaoqing
Li, Liang
Wei, Yanjun
Wang, Ye
Zeng, Renya
Nie, Yuanliu
Zhang, Chenggui
Yin, Ke
Zhou, Fengge
Yang, Zhe
Identification of cuproptosis and immune-related gene prognostic signature in lung adenocarcinoma
title Identification of cuproptosis and immune-related gene prognostic signature in lung adenocarcinoma
title_full Identification of cuproptosis and immune-related gene prognostic signature in lung adenocarcinoma
title_fullStr Identification of cuproptosis and immune-related gene prognostic signature in lung adenocarcinoma
title_full_unstemmed Identification of cuproptosis and immune-related gene prognostic signature in lung adenocarcinoma
title_short Identification of cuproptosis and immune-related gene prognostic signature in lung adenocarcinoma
title_sort identification of cuproptosis and immune-related gene prognostic signature in lung adenocarcinoma
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10445162/
https://www.ncbi.nlm.nih.gov/pubmed/37622116
http://dx.doi.org/10.3389/fimmu.2023.1179742
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