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Identification of an Innate Immune-Related Prognostic Signature in Early-Stage Lung Squamous Cell Carcinoma

BACKGROUND: Early-stage lung squamous cell carcinoma (LUSC) progression is accompanied by changes in immune microenvironments and the expression of immune-related genes (IRGs). Identifying innate IRGs associated with prognosis may improve treatment and reveal new immunotherapeutic targets. METHODS:...

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Autores principales: Li, Liang, Yu, Xue, Ma, Guanqiang, Ji, Zhiqi, Bao, Shihao, He, Xiaopeng, Song, Liang, Yu, Yang, Shi, Mo, Liu, Xiangyan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8643179/
https://www.ncbi.nlm.nih.gov/pubmed/34876838
http://dx.doi.org/10.2147/IJGM.S341175
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author Li, Liang
Yu, Xue
Ma, Guanqiang
Ji, Zhiqi
Bao, Shihao
He, Xiaopeng
Song, Liang
Yu, Yang
Shi, Mo
Liu, Xiangyan
author_facet Li, Liang
Yu, Xue
Ma, Guanqiang
Ji, Zhiqi
Bao, Shihao
He, Xiaopeng
Song, Liang
Yu, Yang
Shi, Mo
Liu, Xiangyan
author_sort Li, Liang
collection PubMed
description BACKGROUND: Early-stage lung squamous cell carcinoma (LUSC) progression is accompanied by changes in immune microenvironments and the expression of immune-related genes (IRGs). Identifying innate IRGs associated with prognosis may improve treatment and reveal new immunotherapeutic targets. METHODS: Gene expression profiles and clinical data of early-stage LUSC patients were obtained from the Gene Expression Omnibus and The Cancer Genome Atlas databases and IRGs from the InnateDB database. Univariate and multivariate Cox regression and LASSO regression analyses were performed to identify an innate IRG signature model prognostic in patients with early-stage LUSC. The predictive ability of this model was assessed by time-dependent receiver operator characteristic curve analysis, with the independence of the model-determined risk score assessed by univariate and multivariate Cox regression analyses. Overall survival (OS) in early-stage LUSC patients was assessed using a nomogram and decision curve analysis (DCA). Functional and biological pathways were determined by gene set enrichment analysis, and differences in biological functions and immune microenvironments between the high- and low-risk groups were assessed by ESTIMATE and the CIBERSORT algorithm. RESULTS: A signature involving six IRGs (SREBF2, GP2, BMX, NR1H4, DDX41, and GOPC) was prognostic of OS. Samples were divided into high- and low-risk groups based on median risk scores. OS was significantly shorter in the high-risk than in the low-risk group in the training (P < 0.001), GEO validation (P = 0.00021) and TCGA validation (P = 0.034) cohorts. Multivariate Cox regression analysis showed that risk score was an independent risk factor for OS, with the combination of risk score and T stage being optimally predictive of clinical benefit. GSEA, ESTIMATE, and the CIBERSORT algorithm showed that immune cell infiltration was higher and immune-related pathways were more strongly expressed in the low-risk group. CONCLUSION: A signature that includes these six innate IRGs may predict prognosis in patients with early-stage LUSC.
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spelling pubmed-86431792021-12-06 Identification of an Innate Immune-Related Prognostic Signature in Early-Stage Lung Squamous Cell Carcinoma Li, Liang Yu, Xue Ma, Guanqiang Ji, Zhiqi Bao, Shihao He, Xiaopeng Song, Liang Yu, Yang Shi, Mo Liu, Xiangyan Int J Gen Med Original Research BACKGROUND: Early-stage lung squamous cell carcinoma (LUSC) progression is accompanied by changes in immune microenvironments and the expression of immune-related genes (IRGs). Identifying innate IRGs associated with prognosis may improve treatment and reveal new immunotherapeutic targets. METHODS: Gene expression profiles and clinical data of early-stage LUSC patients were obtained from the Gene Expression Omnibus and The Cancer Genome Atlas databases and IRGs from the InnateDB database. Univariate and multivariate Cox regression and LASSO regression analyses were performed to identify an innate IRG signature model prognostic in patients with early-stage LUSC. The predictive ability of this model was assessed by time-dependent receiver operator characteristic curve analysis, with the independence of the model-determined risk score assessed by univariate and multivariate Cox regression analyses. Overall survival (OS) in early-stage LUSC patients was assessed using a nomogram and decision curve analysis (DCA). Functional and biological pathways were determined by gene set enrichment analysis, and differences in biological functions and immune microenvironments between the high- and low-risk groups were assessed by ESTIMATE and the CIBERSORT algorithm. RESULTS: A signature involving six IRGs (SREBF2, GP2, BMX, NR1H4, DDX41, and GOPC) was prognostic of OS. Samples were divided into high- and low-risk groups based on median risk scores. OS was significantly shorter in the high-risk than in the low-risk group in the training (P < 0.001), GEO validation (P = 0.00021) and TCGA validation (P = 0.034) cohorts. Multivariate Cox regression analysis showed that risk score was an independent risk factor for OS, with the combination of risk score and T stage being optimally predictive of clinical benefit. GSEA, ESTIMATE, and the CIBERSORT algorithm showed that immune cell infiltration was higher and immune-related pathways were more strongly expressed in the low-risk group. CONCLUSION: A signature that includes these six innate IRGs may predict prognosis in patients with early-stage LUSC. Dove 2021-11-30 /pmc/articles/PMC8643179/ /pubmed/34876838 http://dx.doi.org/10.2147/IJGM.S341175 Text en © 2021 Li et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Li, Liang
Yu, Xue
Ma, Guanqiang
Ji, Zhiqi
Bao, Shihao
He, Xiaopeng
Song, Liang
Yu, Yang
Shi, Mo
Liu, Xiangyan
Identification of an Innate Immune-Related Prognostic Signature in Early-Stage Lung Squamous Cell Carcinoma
title Identification of an Innate Immune-Related Prognostic Signature in Early-Stage Lung Squamous Cell Carcinoma
title_full Identification of an Innate Immune-Related Prognostic Signature in Early-Stage Lung Squamous Cell Carcinoma
title_fullStr Identification of an Innate Immune-Related Prognostic Signature in Early-Stage Lung Squamous Cell Carcinoma
title_full_unstemmed Identification of an Innate Immune-Related Prognostic Signature in Early-Stage Lung Squamous Cell Carcinoma
title_short Identification of an Innate Immune-Related Prognostic Signature in Early-Stage Lung Squamous Cell Carcinoma
title_sort identification of an innate immune-related prognostic signature in early-stage lung squamous cell carcinoma
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8643179/
https://www.ncbi.nlm.nih.gov/pubmed/34876838
http://dx.doi.org/10.2147/IJGM.S341175
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