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A tumor microenvironment gene set–Based prognostic signature for non-small-cell lung cancer
Background: The tumor microenvironment (TME) is involved in the development and progression of lung carcinomas. A deeper understanding of TME landscape would offer insight into prognostic biomarkers and potential therapeutic targets investigation. To this end, we aimed to identify the TME components...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9400803/ https://www.ncbi.nlm.nih.gov/pubmed/36032673 http://dx.doi.org/10.3389/fmolb.2022.849108 |
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author | Wen, Yingsheng Guo, Guangran Yang, Longjun Chen, Lianjuan Zhao, Dechang He, Xiaotian Zhang, Rusi Huang, Zirui Wang, Gongming Zhang, Lanjun |
author_facet | Wen, Yingsheng Guo, Guangran Yang, Longjun Chen, Lianjuan Zhao, Dechang He, Xiaotian Zhang, Rusi Huang, Zirui Wang, Gongming Zhang, Lanjun |
author_sort | Wen, Yingsheng |
collection | PubMed |
description | Background: The tumor microenvironment (TME) is involved in the development and progression of lung carcinomas. A deeper understanding of TME landscape would offer insight into prognostic biomarkers and potential therapeutic targets investigation. To this end, we aimed to identify the TME components of lung cancer and develop a prognostic signature to predict overall survival (OS). Methods: Expression data was retrieved from The Cancer Genome Atlas (TCGA) database and differentially expressed TME-related genes were calculated between tumor and normal tissues. Then nonnegative matrix factorization (NMF) clustering was used to identify two distinct subtypes. Results: Our analysis yielded a gene panel consisting of seven TME-related genes as candidate signature set. With this panel, our model showed that the high-risk group experienced a shorter survival time. This model was further validated by an independent cohort with data from Gene Expression Omnibus (GEO) database (GSE50081 and GSE13213). Additionally, we integrated the clinical factors and risk score to construct a nomogram for predicting prognosis. Our data suggested less immune cells infiltration but more fibroblasts were found in tumor tissues derived from patients at high-risk and those patients exhibited a worse immunotherapy response. Conclusion: The signature set proposed in this work could be an effective model for estimating OS in lung cancer patients. Hopefully analysis of the TME could have the potential to provide novel diagnostic, prognostic and therapeutic opportunities. |
format | Online Article Text |
id | pubmed-9400803 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94008032022-08-25 A tumor microenvironment gene set–Based prognostic signature for non-small-cell lung cancer Wen, Yingsheng Guo, Guangran Yang, Longjun Chen, Lianjuan Zhao, Dechang He, Xiaotian Zhang, Rusi Huang, Zirui Wang, Gongming Zhang, Lanjun Front Mol Biosci Molecular Biosciences Background: The tumor microenvironment (TME) is involved in the development and progression of lung carcinomas. A deeper understanding of TME landscape would offer insight into prognostic biomarkers and potential therapeutic targets investigation. To this end, we aimed to identify the TME components of lung cancer and develop a prognostic signature to predict overall survival (OS). Methods: Expression data was retrieved from The Cancer Genome Atlas (TCGA) database and differentially expressed TME-related genes were calculated between tumor and normal tissues. Then nonnegative matrix factorization (NMF) clustering was used to identify two distinct subtypes. Results: Our analysis yielded a gene panel consisting of seven TME-related genes as candidate signature set. With this panel, our model showed that the high-risk group experienced a shorter survival time. This model was further validated by an independent cohort with data from Gene Expression Omnibus (GEO) database (GSE50081 and GSE13213). Additionally, we integrated the clinical factors and risk score to construct a nomogram for predicting prognosis. Our data suggested less immune cells infiltration but more fibroblasts were found in tumor tissues derived from patients at high-risk and those patients exhibited a worse immunotherapy response. Conclusion: The signature set proposed in this work could be an effective model for estimating OS in lung cancer patients. Hopefully analysis of the TME could have the potential to provide novel diagnostic, prognostic and therapeutic opportunities. Frontiers Media S.A. 2022-08-10 /pmc/articles/PMC9400803/ /pubmed/36032673 http://dx.doi.org/10.3389/fmolb.2022.849108 Text en Copyright © 2022 Wen, Guo, Yang, Chen, Zhao, He, Zhang, Huang, Wang and Zhang. 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 | Molecular Biosciences Wen, Yingsheng Guo, Guangran Yang, Longjun Chen, Lianjuan Zhao, Dechang He, Xiaotian Zhang, Rusi Huang, Zirui Wang, Gongming Zhang, Lanjun A tumor microenvironment gene set–Based prognostic signature for non-small-cell lung cancer |
title | A tumor microenvironment gene set–Based prognostic signature for non-small-cell lung cancer |
title_full | A tumor microenvironment gene set–Based prognostic signature for non-small-cell lung cancer |
title_fullStr | A tumor microenvironment gene set–Based prognostic signature for non-small-cell lung cancer |
title_full_unstemmed | A tumor microenvironment gene set–Based prognostic signature for non-small-cell lung cancer |
title_short | A tumor microenvironment gene set–Based prognostic signature for non-small-cell lung cancer |
title_sort | tumor microenvironment gene set–based prognostic signature for non-small-cell lung cancer |
topic | Molecular Biosciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9400803/ https://www.ncbi.nlm.nih.gov/pubmed/36032673 http://dx.doi.org/10.3389/fmolb.2022.849108 |
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