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Identification and validation of a novel senescence-related biomarker for thyroid cancer to predict the prognosis and immunotherapy
INTRODUCTION: Cellular senescence is a hallmark of tumors and has potential for cancer therapy. Cellular senescence of tumor cells plays a role in tumor progression, and patient prognosis is related to the tumor microenvironment (TME). This study aimed to explore the predictive value of senescence-r...
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/PMC9902917/ https://www.ncbi.nlm.nih.gov/pubmed/36761753 http://dx.doi.org/10.3389/fimmu.2023.1128390 |
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author | Guo, Yangyang Cen, Kenan Chen, Qiaoqiao Dai, Ying Mai, Yifeng Hong, Kai |
author_facet | Guo, Yangyang Cen, Kenan Chen, Qiaoqiao Dai, Ying Mai, Yifeng Hong, Kai |
author_sort | Guo, Yangyang |
collection | PubMed |
description | INTRODUCTION: Cellular senescence is a hallmark of tumors and has potential for cancer therapy. Cellular senescence of tumor cells plays a role in tumor progression, and patient prognosis is related to the tumor microenvironment (TME). This study aimed to explore the predictive value of senescence-related genes in thyroid cancer (THCA) and their relationship with the TME. METHODS: Senescence-related genes were identified from the Molecular Signatures Database and used to conduct consensus clustering across TCGA-THCA. Differentially expressed genes (DEGs) were identified between the clusters used to perform multivariate Cox regression and least absolute shrinkage and selection operator regression (LASSO) analyses to construct a senescence-related signature. TCGA dataset was randomly divided into training and test datasets to verify the prognostic ability of the signature. Subsequently, the immune cell infiltration pattern, immunotherapy response, and drug sensitivity of the two subtypes were analyzed. Finally, the expression of signature genes was detected across TCGA-THCA and GSE33630 datasets, and further validated by RT-qPCR. RESULTS: Three senescence clusters were identified based on the expression of 432 senescence-related genes. Then, 23 prognostic DEGs were identified in TCGA dataset. The signature, composed of six genes, showed a significant relationship with survival, immune cell infiltration, clinical characteristics, immune checkpoints, immunotherapy response, and drug sensitivity. Low-risk THCA shows a better prognosis and higher immunotherapy response than high-risk THCA. A nomogram with perfect stability constructed using signature and clinical characteristics can predict the survival of each patient. The validation part demonstrated that ADAMTSL4, DOCK6, FAM111B, and SEMA6B were expressed at higher levels in the tumor tissue, whereas lower expression of MRPS10 and PSMB7 was observed. DISCUSSION: In conclusion, the senescence-related signature is a promising biomarker for predicting the outcome of THCA and has the potential to guide immunotherapy. |
format | Online Article Text |
id | pubmed-9902917 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-99029172023-02-08 Identification and validation of a novel senescence-related biomarker for thyroid cancer to predict the prognosis and immunotherapy Guo, Yangyang Cen, Kenan Chen, Qiaoqiao Dai, Ying Mai, Yifeng Hong, Kai Front Immunol Immunology INTRODUCTION: Cellular senescence is a hallmark of tumors and has potential for cancer therapy. Cellular senescence of tumor cells plays a role in tumor progression, and patient prognosis is related to the tumor microenvironment (TME). This study aimed to explore the predictive value of senescence-related genes in thyroid cancer (THCA) and their relationship with the TME. METHODS: Senescence-related genes were identified from the Molecular Signatures Database and used to conduct consensus clustering across TCGA-THCA. Differentially expressed genes (DEGs) were identified between the clusters used to perform multivariate Cox regression and least absolute shrinkage and selection operator regression (LASSO) analyses to construct a senescence-related signature. TCGA dataset was randomly divided into training and test datasets to verify the prognostic ability of the signature. Subsequently, the immune cell infiltration pattern, immunotherapy response, and drug sensitivity of the two subtypes were analyzed. Finally, the expression of signature genes was detected across TCGA-THCA and GSE33630 datasets, and further validated by RT-qPCR. RESULTS: Three senescence clusters were identified based on the expression of 432 senescence-related genes. Then, 23 prognostic DEGs were identified in TCGA dataset. The signature, composed of six genes, showed a significant relationship with survival, immune cell infiltration, clinical characteristics, immune checkpoints, immunotherapy response, and drug sensitivity. Low-risk THCA shows a better prognosis and higher immunotherapy response than high-risk THCA. A nomogram with perfect stability constructed using signature and clinical characteristics can predict the survival of each patient. The validation part demonstrated that ADAMTSL4, DOCK6, FAM111B, and SEMA6B were expressed at higher levels in the tumor tissue, whereas lower expression of MRPS10 and PSMB7 was observed. DISCUSSION: In conclusion, the senescence-related signature is a promising biomarker for predicting the outcome of THCA and has the potential to guide immunotherapy. Frontiers Media S.A. 2023-01-24 /pmc/articles/PMC9902917/ /pubmed/36761753 http://dx.doi.org/10.3389/fimmu.2023.1128390 Text en Copyright © 2023 Guo, Cen, Chen, Dai, Mai and Hong 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 Guo, Yangyang Cen, Kenan Chen, Qiaoqiao Dai, Ying Mai, Yifeng Hong, Kai Identification and validation of a novel senescence-related biomarker for thyroid cancer to predict the prognosis and immunotherapy |
title | Identification and validation of a novel senescence-related biomarker for thyroid cancer to predict the prognosis and immunotherapy |
title_full | Identification and validation of a novel senescence-related biomarker for thyroid cancer to predict the prognosis and immunotherapy |
title_fullStr | Identification and validation of a novel senescence-related biomarker for thyroid cancer to predict the prognosis and immunotherapy |
title_full_unstemmed | Identification and validation of a novel senescence-related biomarker for thyroid cancer to predict the prognosis and immunotherapy |
title_short | Identification and validation of a novel senescence-related biomarker for thyroid cancer to predict the prognosis and immunotherapy |
title_sort | identification and validation of a novel senescence-related biomarker for thyroid cancer to predict the prognosis and immunotherapy |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9902917/ https://www.ncbi.nlm.nih.gov/pubmed/36761753 http://dx.doi.org/10.3389/fimmu.2023.1128390 |
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