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Integrated multi-omics analysis identifies CD73 as a prognostic biomarker and immunotherapy response predictor in head and neck squamous cell carcinoma

BACKGROUND: Advances in tumor immunotherapy have been developed for patients with advanced recurrent or metastatic (R/M) HNSCC. However, the response of most HNSCC patients to immune checkpoint inhibitors (ICI) remains unsatisfactory. CD73 is a promising target for tumor immunotherapy, but its role...

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Autores principales: Shen, Ao, Ye, Yafen, Chen, Fan, Xu, Yunyun, Zhang, Zhen, Zhao, Qi, Zeng, Zhao-lei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9708745/
https://www.ncbi.nlm.nih.gov/pubmed/36466881
http://dx.doi.org/10.3389/fimmu.2022.969034
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author Shen, Ao
Ye, Yafen
Chen, Fan
Xu, Yunyun
Zhang, Zhen
Zhao, Qi
Zeng, Zhao-lei
author_facet Shen, Ao
Ye, Yafen
Chen, Fan
Xu, Yunyun
Zhang, Zhen
Zhao, Qi
Zeng, Zhao-lei
author_sort Shen, Ao
collection PubMed
description BACKGROUND: Advances in tumor immunotherapy have been developed for patients with advanced recurrent or metastatic (R/M) HNSCC. However, the response of most HNSCC patients to immune checkpoint inhibitors (ICI) remains unsatisfactory. CD73 is a promising target for tumor immunotherapy, but its role in HNSCC remains insufficient. In this study, we aim to explore the function of CD73 in HNSCC. METHODS: Transcriptomic and clinical data of TCGA-HNSC were downloaded from UCSC Xena for analysis of CD73 mRNA expression and prognosis. Immunohistochemical assay were performed to validate the expression of CD73 in tumor tissues and its relationship with CD8(+) T cells. GSEA analysis was performed with the “clusterProfiler” R package. Immune infiltration analysis was calculated with ESTIMATE, CIBERSORT and MCP-counter algorithms. Single-cell transcriptomic data was originated from GSE103322. Cell clustering, annotation and CD73 expression were from the TISCH database. Correlation data between CD73 and tumor signatures were obtained from the CancerSEA database. Somatic mutation data were obtained from TCGA-HNSC and analyzed by “maftools” R package. Immune efficacy prediction was performed using TIDE algorithm and validated with the IMvigor210 cohort. RESULTS: Compared with normal tissues, both mRNA and protein expressions of CD73 were elevated in tumor tissues (P = 9.7×10(-10), P = 7.6×10(-5), respectively). Kaplan-Meier analysis revealed that patients with high expression of CD73 had worse overall survival (log-rank P = 0.0094), and CD73 could be used as a diagnostic factor for HNSCC (AUC = 0.778). Both bulk RNA-seq and single-cell RNA-seq analysis showed that high CD73 expression can promote EMT and metastasis, samples with high CD73 expression had reduced CD8(+) T cells. Furthermore, it was found that CD73-high group was more prone to have mutations in TP53, HRAS and CDKN2A, and were negatively correlated with TMB (P = 0.0055) and MSI (P = 0.00034). Mutational signature analysis found that CD73 was associated with APOBEC signature. Immunotherapy efficacy analysis showed that CD73-high group was less sensitive to immune efficacy. CONCLUSIONS: Our results demonstrate that CD73 has an inhibitory effect on the tumor microenvironment, and is more likely to be unresponsive to ICI therapy. Collectively, targeting CD73 may provide new insights for tumor targeted therapy and/or immunotherapy.
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spelling pubmed-97087452022-12-01 Integrated multi-omics analysis identifies CD73 as a prognostic biomarker and immunotherapy response predictor in head and neck squamous cell carcinoma Shen, Ao Ye, Yafen Chen, Fan Xu, Yunyun Zhang, Zhen Zhao, Qi Zeng, Zhao-lei Front Immunol Immunology BACKGROUND: Advances in tumor immunotherapy have been developed for patients with advanced recurrent or metastatic (R/M) HNSCC. However, the response of most HNSCC patients to immune checkpoint inhibitors (ICI) remains unsatisfactory. CD73 is a promising target for tumor immunotherapy, but its role in HNSCC remains insufficient. In this study, we aim to explore the function of CD73 in HNSCC. METHODS: Transcriptomic and clinical data of TCGA-HNSC were downloaded from UCSC Xena for analysis of CD73 mRNA expression and prognosis. Immunohistochemical assay were performed to validate the expression of CD73 in tumor tissues and its relationship with CD8(+) T cells. GSEA analysis was performed with the “clusterProfiler” R package. Immune infiltration analysis was calculated with ESTIMATE, CIBERSORT and MCP-counter algorithms. Single-cell transcriptomic data was originated from GSE103322. Cell clustering, annotation and CD73 expression were from the TISCH database. Correlation data between CD73 and tumor signatures were obtained from the CancerSEA database. Somatic mutation data were obtained from TCGA-HNSC and analyzed by “maftools” R package. Immune efficacy prediction was performed using TIDE algorithm and validated with the IMvigor210 cohort. RESULTS: Compared with normal tissues, both mRNA and protein expressions of CD73 were elevated in tumor tissues (P = 9.7×10(-10), P = 7.6×10(-5), respectively). Kaplan-Meier analysis revealed that patients with high expression of CD73 had worse overall survival (log-rank P = 0.0094), and CD73 could be used as a diagnostic factor for HNSCC (AUC = 0.778). Both bulk RNA-seq and single-cell RNA-seq analysis showed that high CD73 expression can promote EMT and metastasis, samples with high CD73 expression had reduced CD8(+) T cells. Furthermore, it was found that CD73-high group was more prone to have mutations in TP53, HRAS and CDKN2A, and were negatively correlated with TMB (P = 0.0055) and MSI (P = 0.00034). Mutational signature analysis found that CD73 was associated with APOBEC signature. Immunotherapy efficacy analysis showed that CD73-high group was less sensitive to immune efficacy. CONCLUSIONS: Our results demonstrate that CD73 has an inhibitory effect on the tumor microenvironment, and is more likely to be unresponsive to ICI therapy. Collectively, targeting CD73 may provide new insights for tumor targeted therapy and/or immunotherapy. Frontiers Media S.A. 2022-11-16 /pmc/articles/PMC9708745/ /pubmed/36466881 http://dx.doi.org/10.3389/fimmu.2022.969034 Text en Copyright © 2022 Shen, Ye, Chen, Xu, Zhang, Zhao and Zeng 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
Shen, Ao
Ye, Yafen
Chen, Fan
Xu, Yunyun
Zhang, Zhen
Zhao, Qi
Zeng, Zhao-lei
Integrated multi-omics analysis identifies CD73 as a prognostic biomarker and immunotherapy response predictor in head and neck squamous cell carcinoma
title Integrated multi-omics analysis identifies CD73 as a prognostic biomarker and immunotherapy response predictor in head and neck squamous cell carcinoma
title_full Integrated multi-omics analysis identifies CD73 as a prognostic biomarker and immunotherapy response predictor in head and neck squamous cell carcinoma
title_fullStr Integrated multi-omics analysis identifies CD73 as a prognostic biomarker and immunotherapy response predictor in head and neck squamous cell carcinoma
title_full_unstemmed Integrated multi-omics analysis identifies CD73 as a prognostic biomarker and immunotherapy response predictor in head and neck squamous cell carcinoma
title_short Integrated multi-omics analysis identifies CD73 as a prognostic biomarker and immunotherapy response predictor in head and neck squamous cell carcinoma
title_sort integrated multi-omics analysis identifies cd73 as a prognostic biomarker and immunotherapy response predictor in head and neck squamous cell carcinoma
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9708745/
https://www.ncbi.nlm.nih.gov/pubmed/36466881
http://dx.doi.org/10.3389/fimmu.2022.969034
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