Tumor mutation burden (TMB)-associated signature constructed to predict survival of lung squamous cell carcinoma patients

Abstract Lung squamous cell carcinoma (LUSC) is a common type of lung cancer with high incidence and mortality rate. Tumor mutational burden (TMB) is an emerging biomarker for selecting patients with non-small cell lung cancer (NSCLC) for immunotherapy. This study aimed to reveal TMB involved in the...

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Autores principales:	Dan Yan, Yi Chen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d01fd21c42fb47f7bb59d61136ca06e3
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spelling	oai:doaj.org-article:d01fd21c42fb47f7bb59d61136ca06e32021-12-02T13:41:00ZTumor mutation burden (TMB)-associated signature constructed to predict survival of lung squamous cell carcinoma patients10.1038/s41598-021-88694-72045-2322https://doaj.org/article/d01fd21c42fb47f7bb59d61136ca06e32021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88694-7https://doaj.org/toc/2045-2322Abstract Lung squamous cell carcinoma (LUSC) is a common type of lung cancer with high incidence and mortality rate. Tumor mutational burden (TMB) is an emerging biomarker for selecting patients with non-small cell lung cancer (NSCLC) for immunotherapy. This study aimed to reveal TMB involved in the mechanisms of LUSC and develop a model to predict the overall survival of LUSC patients. The information of patients with LUSC were obtained from the cancer genome atlas database (TCGA). Differentially expressed genes (DEGs) between low- and the high-TMB groups were identified and taken as nodes for the protein–protein interaction (PPI) network construction. Gene oncology (GO) enrichment analysis and gene set enrichment analysis (GSEA) were used to investigate the potential molecular mechanism. Then, we identified the factors affecting the prognosis of LUSC through cox analysis, and developed a risk score signature. Kaplan–Meier method was conducted to analyze the difference in survival between the high- and low-risk groups. We constructed a nomogram based on the risk score model and clinical characteristics to predict the overall survival of patients with LUSC. Finally, the signature and nomogram were further validated by using the gene expression data downloaded from the Gene Expression Omnibus (GEO) database. 30 DEGs between high- and low-TMB groups were identified. PPI analysis identified CD22, TLR10, PIGR and SELE as the hub genes. Cox analysis indicated that FAM107A, IGLL1, SELE and T stage were independent prognostic factors of LUSC. Low-risk scores group lived longer than that of patients with high-risk scores in LUSC. Finally, we built a nomogram that integrated the clinical characteristics (TMN stage, age, gender) with the three-gene signature to predict the survival probability of LUSC patients. Further verification in the GEO dataset. TMB might contribute to the pathogenesis of LUSC. TMB-associated genes can be used to develope a model to predict the OS of lung squamous cell carcinoma patients.Dan YanYi ChenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Dan Yan Yi Chen Tumor mutation burden (TMB)-associated signature constructed to predict survival of lung squamous cell carcinoma patients
description	Abstract Lung squamous cell carcinoma (LUSC) is a common type of lung cancer with high incidence and mortality rate. Tumor mutational burden (TMB) is an emerging biomarker for selecting patients with non-small cell lung cancer (NSCLC) for immunotherapy. This study aimed to reveal TMB involved in the mechanisms of LUSC and develop a model to predict the overall survival of LUSC patients. The information of patients with LUSC were obtained from the cancer genome atlas database (TCGA). Differentially expressed genes (DEGs) between low- and the high-TMB groups were identified and taken as nodes for the protein–protein interaction (PPI) network construction. Gene oncology (GO) enrichment analysis and gene set enrichment analysis (GSEA) were used to investigate the potential molecular mechanism. Then, we identified the factors affecting the prognosis of LUSC through cox analysis, and developed a risk score signature. Kaplan–Meier method was conducted to analyze the difference in survival between the high- and low-risk groups. We constructed a nomogram based on the risk score model and clinical characteristics to predict the overall survival of patients with LUSC. Finally, the signature and nomogram were further validated by using the gene expression data downloaded from the Gene Expression Omnibus (GEO) database. 30 DEGs between high- and low-TMB groups were identified. PPI analysis identified CD22, TLR10, PIGR and SELE as the hub genes. Cox analysis indicated that FAM107A, IGLL1, SELE and T stage were independent prognostic factors of LUSC. Low-risk scores group lived longer than that of patients with high-risk scores in LUSC. Finally, we built a nomogram that integrated the clinical characteristics (TMN stage, age, gender) with the three-gene signature to predict the survival probability of LUSC patients. Further verification in the GEO dataset. TMB might contribute to the pathogenesis of LUSC. TMB-associated genes can be used to develope a model to predict the OS of lung squamous cell carcinoma patients.
format	article
author	Dan Yan Yi Chen
author_facet	Dan Yan Yi Chen
author_sort	Dan Yan
title	Tumor mutation burden (TMB)-associated signature constructed to predict survival of lung squamous cell carcinoma patients
title_short	Tumor mutation burden (TMB)-associated signature constructed to predict survival of lung squamous cell carcinoma patients
title_full	Tumor mutation burden (TMB)-associated signature constructed to predict survival of lung squamous cell carcinoma patients
title_fullStr	Tumor mutation burden (TMB)-associated signature constructed to predict survival of lung squamous cell carcinoma patients
title_full_unstemmed	Tumor mutation burden (TMB)-associated signature constructed to predict survival of lung squamous cell carcinoma patients
title_sort	tumor mutation burden (tmb)-associated signature constructed to predict survival of lung squamous cell carcinoma patients
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d01fd21c42fb47f7bb59d61136ca06e3
work_keys_str_mv	AT danyan tumormutationburdentmbassociatedsignatureconstructedtopredictsurvivaloflungsquamouscellcarcinomapatients AT yichen tumormutationburdentmbassociatedsignatureconstructedtopredictsurvivaloflungsquamouscellcarcinomapatients
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Tumor mutation burden (TMB)-associated signature constructed to predict survival of lung squamous cell carcinoma patients

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