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Investigation of an FGFR-Signaling-Related Prognostic Model and Immune Landscape in Head and Neck Squamous Cell Carcinoma

Background: There is accumulating evidence on the clinical importance of the fibroblast growth factor receptor (FGFR) signal, hypoxia, and glycolysis in the immune microenvironment of head and neck squamous cell carcinoma (HNSCC), yet reliable prognostic signatures based on the combination of the fi...

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Autores principales: Chen, Qi, Chu, Ling, Li, Xinyu, Li, Hao, Zhang, Ying, Cao, Qingtai, Zhuang, Quan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8882630/
https://www.ncbi.nlm.nih.gov/pubmed/35237609
http://dx.doi.org/10.3389/fcell.2021.801715
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author Chen, Qi
Chu, Ling
Li, Xinyu
Li, Hao
Zhang, Ying
Cao, Qingtai
Zhuang, Quan
author_facet Chen, Qi
Chu, Ling
Li, Xinyu
Li, Hao
Zhang, Ying
Cao, Qingtai
Zhuang, Quan
author_sort Chen, Qi
collection PubMed
description Background: There is accumulating evidence on the clinical importance of the fibroblast growth factor receptor (FGFR) signal, hypoxia, and glycolysis in the immune microenvironment of head and neck squamous cell carcinoma (HNSCC), yet reliable prognostic signatures based on the combination of the fibrosis signal, hypoxia, and glycolysis have not been systematically investigated. Herein, we are committed to establish a fibrosis–hypoxia–glycolysis–related prediction model for the prognosis and related immune infiltration of HNSCC. Methods: Fibrotic signal status was estimated with microarray data of a discovery cohort from the TCGA database using the UMAP algorithm. Hypoxia, glycolysis, and immune-cell infiltration scores were imputed using the ssGSEA algorithm. Cox regression with the LASSO method was applied to define prognostic genes and develop a fibrosis–hypoxia–glycolysis–related gene signature. Immunohistochemistry (IHC) was conducted to identify the expression of specific genes in the prognostic model. Protein expression of several signature genes was evaluated in HPA. An independent cohort from the GEO database was used for external validation. Another scRNA-seq data set was used to clarify the related immune infiltration of HNSCC. Results: Six genes, including AREG, THBS1, SEMA3C, ANO1, IGHG2, and EPHX3, were identified to construct a prognostic model for risk stratification, which was mostly validated in the independent cohort. Multivariate analysis revealed that risk score calculated by our prognostic model was identified as an independent adverse prognostic factor (p < .001). Activated B cells, immature B cells, activated CD4(+) T cells, activated CD8(+) T cells, effector memory CD8(+) T cells, MDSCs, and mast cells were identified as key immune cells between high- and low-risk groups. IHC results showed that the expression of SEMA3C, IGHG2 were slightly higher in HNSCC tissue than normal head and neck squamous cell tissue. THBS1, ANO1, and EPHX3 were verified by IHC in HPA. By using single-cell analysis, FGFR-related genes and highly expressed DEGs in low-survival patients were more active in monocytes than in other immune cells. Conclusion: A fibrosis–hypoxia–glycolysis–related prediction model provides risk estimation for better prognoses to patients diagnosed with HNSCC.
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spelling pubmed-88826302022-03-01 Investigation of an FGFR-Signaling-Related Prognostic Model and Immune Landscape in Head and Neck Squamous Cell Carcinoma Chen, Qi Chu, Ling Li, Xinyu Li, Hao Zhang, Ying Cao, Qingtai Zhuang, Quan Front Cell Dev Biol Cell and Developmental Biology Background: There is accumulating evidence on the clinical importance of the fibroblast growth factor receptor (FGFR) signal, hypoxia, and glycolysis in the immune microenvironment of head and neck squamous cell carcinoma (HNSCC), yet reliable prognostic signatures based on the combination of the fibrosis signal, hypoxia, and glycolysis have not been systematically investigated. Herein, we are committed to establish a fibrosis–hypoxia–glycolysis–related prediction model for the prognosis and related immune infiltration of HNSCC. Methods: Fibrotic signal status was estimated with microarray data of a discovery cohort from the TCGA database using the UMAP algorithm. Hypoxia, glycolysis, and immune-cell infiltration scores were imputed using the ssGSEA algorithm. Cox regression with the LASSO method was applied to define prognostic genes and develop a fibrosis–hypoxia–glycolysis–related gene signature. Immunohistochemistry (IHC) was conducted to identify the expression of specific genes in the prognostic model. Protein expression of several signature genes was evaluated in HPA. An independent cohort from the GEO database was used for external validation. Another scRNA-seq data set was used to clarify the related immune infiltration of HNSCC. Results: Six genes, including AREG, THBS1, SEMA3C, ANO1, IGHG2, and EPHX3, were identified to construct a prognostic model for risk stratification, which was mostly validated in the independent cohort. Multivariate analysis revealed that risk score calculated by our prognostic model was identified as an independent adverse prognostic factor (p < .001). Activated B cells, immature B cells, activated CD4(+) T cells, activated CD8(+) T cells, effector memory CD8(+) T cells, MDSCs, and mast cells were identified as key immune cells between high- and low-risk groups. IHC results showed that the expression of SEMA3C, IGHG2 were slightly higher in HNSCC tissue than normal head and neck squamous cell tissue. THBS1, ANO1, and EPHX3 were verified by IHC in HPA. By using single-cell analysis, FGFR-related genes and highly expressed DEGs in low-survival patients were more active in monocytes than in other immune cells. Conclusion: A fibrosis–hypoxia–glycolysis–related prediction model provides risk estimation for better prognoses to patients diagnosed with HNSCC. Frontiers Media S.A. 2022-02-14 /pmc/articles/PMC8882630/ /pubmed/35237609 http://dx.doi.org/10.3389/fcell.2021.801715 Text en Copyright © 2022 Chen, Chu, Li, Li, Zhang, Cao and Zhuang. 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 Cell and Developmental Biology
Chen, Qi
Chu, Ling
Li, Xinyu
Li, Hao
Zhang, Ying
Cao, Qingtai
Zhuang, Quan
Investigation of an FGFR-Signaling-Related Prognostic Model and Immune Landscape in Head and Neck Squamous Cell Carcinoma
title Investigation of an FGFR-Signaling-Related Prognostic Model and Immune Landscape in Head and Neck Squamous Cell Carcinoma
title_full Investigation of an FGFR-Signaling-Related Prognostic Model and Immune Landscape in Head and Neck Squamous Cell Carcinoma
title_fullStr Investigation of an FGFR-Signaling-Related Prognostic Model and Immune Landscape in Head and Neck Squamous Cell Carcinoma
title_full_unstemmed Investigation of an FGFR-Signaling-Related Prognostic Model and Immune Landscape in Head and Neck Squamous Cell Carcinoma
title_short Investigation of an FGFR-Signaling-Related Prognostic Model and Immune Landscape in Head and Neck Squamous Cell Carcinoma
title_sort investigation of an fgfr-signaling-related prognostic model and immune landscape in head and neck squamous cell carcinoma
topic Cell and Developmental Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8882630/
https://www.ncbi.nlm.nih.gov/pubmed/35237609
http://dx.doi.org/10.3389/fcell.2021.801715
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