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Construction of a novel anoikis-related prognostic model and analysis of its correlation with infiltration of immune cells in neuroblastoma
BACKGROUND: Anoikis resistance (AR) plays an important role in the process of metastasis, which is an important factor affecting the risk stage of neuroblastoma (NB). This study aims to construct an anoikis-related prognostic model and analyze the characteristics of hub genes, important pathways and...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10111050/ https://www.ncbi.nlm.nih.gov/pubmed/37081871 http://dx.doi.org/10.3389/fimmu.2023.1135617 |
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author | Chen, Ji Sun, Mengjiao Chen, Chuqin Kang, Meiyun Qian, Bo Sun, Jing Ma, Xiaopeng Zhou, Jianfeng Huang, Lei Jiang, Bin Fang, Yongjun |
author_facet | Chen, Ji Sun, Mengjiao Chen, Chuqin Kang, Meiyun Qian, Bo Sun, Jing Ma, Xiaopeng Zhou, Jianfeng Huang, Lei Jiang, Bin Fang, Yongjun |
author_sort | Chen, Ji |
collection | PubMed |
description | BACKGROUND: Anoikis resistance (AR) plays an important role in the process of metastasis, which is an important factor affecting the risk stage of neuroblastoma (NB). This study aims to construct an anoikis-related prognostic model and analyze the characteristics of hub genes, important pathways and tumor microenvironment of anoikis-related subtypes of NB, so as to provide help for the clinical diagnosis, treatment and research of NB. METHODS: We combined transcriptome data of GSE49710 and E-MTAB-8248, screened anoikis-related genes (Args) closely related to the prognosis of NB by univariate cox regression analysis, and divided the samples into anoikis-related subtypes by consistent cluster analysis. WGCNA was used to screen hub genes, GSVA and GSEA were used to analyze the differentially enriched pathways between anoikis-related subtypes. We analyzed the infiltration levels of immune cells between different groups by SsGSEA and CIBERSORT. Lasso and multivariate regression analyses were used to construct a prognostic model. Finally, we analyzed drug sensitivity through the GDSC database. RESULTS: 721 cases and 283 Args were included in this study. All samples were grouped into two subtypes with different prognoses. The analyses of WGCNA, GSVA and GSEA suggested the existence of differentially expressed hub genes and important pathways in the two subtypes. We further constructed an anoikis-related prognostic model, in which 15 Args participated. This model had more advantages in evaluating the prognoses of NB than other commonly used clinical indicators. The infiltration levels of 9 immune cells were significantly different between different risk groups, and 13 Args involved in the model construction were correlated with the infiltration levels of immune cells. There was a relationship between the infiltration levels of 6 immune cells and riskscores. Finally, we screened 15 drugs with more obvious effects on NB in high-risk group. CONCLUSION: There are two anoikis-related subtypes with different prognoses in the population of NB. The anoikis-related prognostic model constructed in this study can accurately predict the prognoses of children with NB, and has a good guiding significance for clinical diagnosis, treatment and research of NB. |
format | Online Article Text |
id | pubmed-10111050 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101110502023-04-19 Construction of a novel anoikis-related prognostic model and analysis of its correlation with infiltration of immune cells in neuroblastoma Chen, Ji Sun, Mengjiao Chen, Chuqin Kang, Meiyun Qian, Bo Sun, Jing Ma, Xiaopeng Zhou, Jianfeng Huang, Lei Jiang, Bin Fang, Yongjun Front Immunol Immunology BACKGROUND: Anoikis resistance (AR) plays an important role in the process of metastasis, which is an important factor affecting the risk stage of neuroblastoma (NB). This study aims to construct an anoikis-related prognostic model and analyze the characteristics of hub genes, important pathways and tumor microenvironment of anoikis-related subtypes of NB, so as to provide help for the clinical diagnosis, treatment and research of NB. METHODS: We combined transcriptome data of GSE49710 and E-MTAB-8248, screened anoikis-related genes (Args) closely related to the prognosis of NB by univariate cox regression analysis, and divided the samples into anoikis-related subtypes by consistent cluster analysis. WGCNA was used to screen hub genes, GSVA and GSEA were used to analyze the differentially enriched pathways between anoikis-related subtypes. We analyzed the infiltration levels of immune cells between different groups by SsGSEA and CIBERSORT. Lasso and multivariate regression analyses were used to construct a prognostic model. Finally, we analyzed drug sensitivity through the GDSC database. RESULTS: 721 cases and 283 Args were included in this study. All samples were grouped into two subtypes with different prognoses. The analyses of WGCNA, GSVA and GSEA suggested the existence of differentially expressed hub genes and important pathways in the two subtypes. We further constructed an anoikis-related prognostic model, in which 15 Args participated. This model had more advantages in evaluating the prognoses of NB than other commonly used clinical indicators. The infiltration levels of 9 immune cells were significantly different between different risk groups, and 13 Args involved in the model construction were correlated with the infiltration levels of immune cells. There was a relationship between the infiltration levels of 6 immune cells and riskscores. Finally, we screened 15 drugs with more obvious effects on NB in high-risk group. CONCLUSION: There are two anoikis-related subtypes with different prognoses in the population of NB. The anoikis-related prognostic model constructed in this study can accurately predict the prognoses of children with NB, and has a good guiding significance for clinical diagnosis, treatment and research of NB. Frontiers Media S.A. 2023-04-04 /pmc/articles/PMC10111050/ /pubmed/37081871 http://dx.doi.org/10.3389/fimmu.2023.1135617 Text en Copyright © 2023 Chen, Sun, Chen, Kang, Qian, Sun, Ma, Zhou, Huang, Jiang and Fang 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 Chen, Ji Sun, Mengjiao Chen, Chuqin Kang, Meiyun Qian, Bo Sun, Jing Ma, Xiaopeng Zhou, Jianfeng Huang, Lei Jiang, Bin Fang, Yongjun Construction of a novel anoikis-related prognostic model and analysis of its correlation with infiltration of immune cells in neuroblastoma |
title | Construction of a novel anoikis-related prognostic model and analysis of its correlation with infiltration of immune cells in neuroblastoma |
title_full | Construction of a novel anoikis-related prognostic model and analysis of its correlation with infiltration of immune cells in neuroblastoma |
title_fullStr | Construction of a novel anoikis-related prognostic model and analysis of its correlation with infiltration of immune cells in neuroblastoma |
title_full_unstemmed | Construction of a novel anoikis-related prognostic model and analysis of its correlation with infiltration of immune cells in neuroblastoma |
title_short | Construction of a novel anoikis-related prognostic model and analysis of its correlation with infiltration of immune cells in neuroblastoma |
title_sort | construction of a novel anoikis-related prognostic model and analysis of its correlation with infiltration of immune cells in neuroblastoma |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10111050/ https://www.ncbi.nlm.nih.gov/pubmed/37081871 http://dx.doi.org/10.3389/fimmu.2023.1135617 |
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