Cargando…

An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Lung Squamous Carcinoma

Aging is an inevitable process characterized by a decline in many physiological activities, and has been known as a significant risk factor for many kinds of malignancies, but there are few studies about aging-related genes (ARGs) in lung squamous carcinoma (LUSC). We designed this study to explore...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhai, Wen-Yu, Duan, Fang-Fang, Chen, Si, Wang, Jun-Ye, Zhao, Ze-Rui, Wang, Yi-Zhi, Rao, Bing-Yu, Lin, Yao-Bin, Long, Hao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8921527/
https://www.ncbi.nlm.nih.gov/pubmed/35300428
http://dx.doi.org/10.3389/fcell.2022.770550
_version_ 1784669341534388224
author Zhai, Wen-Yu
Duan, Fang-Fang
Chen, Si
Wang, Jun-Ye
Zhao, Ze-Rui
Wang, Yi-Zhi
Rao, Bing-Yu
Lin, Yao-Bin
Long, Hao
author_facet Zhai, Wen-Yu
Duan, Fang-Fang
Chen, Si
Wang, Jun-Ye
Zhao, Ze-Rui
Wang, Yi-Zhi
Rao, Bing-Yu
Lin, Yao-Bin
Long, Hao
author_sort Zhai, Wen-Yu
collection PubMed
description Aging is an inevitable process characterized by a decline in many physiological activities, and has been known as a significant risk factor for many kinds of malignancies, but there are few studies about aging-related genes (ARGs) in lung squamous carcinoma (LUSC). We designed this study to explore the prognostic value of ARGs and establish an ARG-based prognosis signature for LUSC patients. RNA-sequencing and corresponding clinicopathological data of patients with LUSC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The ARG risk signature was developed on the basis of results of LASSO and multivariate Cox analysis in the TCGA training dataset (n = 492). Furthermore, the GSE73403 dataset (n = 69) validated the prognostic performance of this ARG signature. Immunohistochemistry (IHC) staining was used to verify the expression of the ARGs in the signature. A five ARG-based signature, including A2M, CHEK2, ELN, FOS, and PLAU, was constructed in the TCGA dataset, and stratified patients into low- and high-risk groups with significantly different overall survival (OS) rates. The ARG risk score remained to be considered as an independent indicator of OS in the multivariate Cox regression model for LUSC patients. Then, a prognostic nomogram incorporating the ARG risk score with T-, N-, and M-classification was established. It achieved a good discriminative ability with a C-index of 0.628 (95% confidence interval [CI]: 0.586–0.671) in the TCGA cohort and 0.648 (95% CI: 0.535–0.762) in the GSE73403 dataset. Calibration curves displayed excellent agreement between the actual observations and the nomogram-predicted survival. The IHC staining discovered that these five ARGs were overexpression in LUSC tissues. Besides, the immune infiltration analysis in the TCGA cohort represented a distinctly differentiated infiltration of anti-tumor immune cells between the low- and high-risk groups. We identified a novel ARG-related prognostic signature, which may serve as a potential biomarker for individualized survival predictions and personalized therapeutic recommendation of anti-tumor immunity for patients with LUSC.
format Online
Article
Text
id pubmed-8921527
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-89215272022-03-16 An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Lung Squamous Carcinoma Zhai, Wen-Yu Duan, Fang-Fang Chen, Si Wang, Jun-Ye Zhao, Ze-Rui Wang, Yi-Zhi Rao, Bing-Yu Lin, Yao-Bin Long, Hao Front Cell Dev Biol Cell and Developmental Biology Aging is an inevitable process characterized by a decline in many physiological activities, and has been known as a significant risk factor for many kinds of malignancies, but there are few studies about aging-related genes (ARGs) in lung squamous carcinoma (LUSC). We designed this study to explore the prognostic value of ARGs and establish an ARG-based prognosis signature for LUSC patients. RNA-sequencing and corresponding clinicopathological data of patients with LUSC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The ARG risk signature was developed on the basis of results of LASSO and multivariate Cox analysis in the TCGA training dataset (n = 492). Furthermore, the GSE73403 dataset (n = 69) validated the prognostic performance of this ARG signature. Immunohistochemistry (IHC) staining was used to verify the expression of the ARGs in the signature. A five ARG-based signature, including A2M, CHEK2, ELN, FOS, and PLAU, was constructed in the TCGA dataset, and stratified patients into low- and high-risk groups with significantly different overall survival (OS) rates. The ARG risk score remained to be considered as an independent indicator of OS in the multivariate Cox regression model for LUSC patients. Then, a prognostic nomogram incorporating the ARG risk score with T-, N-, and M-classification was established. It achieved a good discriminative ability with a C-index of 0.628 (95% confidence interval [CI]: 0.586–0.671) in the TCGA cohort and 0.648 (95% CI: 0.535–0.762) in the GSE73403 dataset. Calibration curves displayed excellent agreement between the actual observations and the nomogram-predicted survival. The IHC staining discovered that these five ARGs were overexpression in LUSC tissues. Besides, the immune infiltration analysis in the TCGA cohort represented a distinctly differentiated infiltration of anti-tumor immune cells between the low- and high-risk groups. We identified a novel ARG-related prognostic signature, which may serve as a potential biomarker for individualized survival predictions and personalized therapeutic recommendation of anti-tumor immunity for patients with LUSC. Frontiers Media S.A. 2022-03-01 /pmc/articles/PMC8921527/ /pubmed/35300428 http://dx.doi.org/10.3389/fcell.2022.770550 Text en Copyright © 2022 Zhai, Duan, Chen, Wang, Zhao, Wang, Rao, Lin and Long. 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 Cell and Developmental Biology
Zhai, Wen-Yu
Duan, Fang-Fang
Chen, Si
Wang, Jun-Ye
Zhao, Ze-Rui
Wang, Yi-Zhi
Rao, Bing-Yu
Lin, Yao-Bin
Long, Hao
An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Lung Squamous Carcinoma
title An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Lung Squamous Carcinoma
title_full An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Lung Squamous Carcinoma
title_fullStr An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Lung Squamous Carcinoma
title_full_unstemmed An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Lung Squamous Carcinoma
title_short An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Lung Squamous Carcinoma
title_sort aging-related gene signature-based model for risk stratification and prognosis prediction in lung squamous carcinoma
topic Cell and Developmental Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8921527/
https://www.ncbi.nlm.nih.gov/pubmed/35300428
http://dx.doi.org/10.3389/fcell.2022.770550
work_keys_str_mv AT zhaiwenyu anagingrelatedgenesignaturebasedmodelforriskstratificationandprognosispredictioninlungsquamouscarcinoma
AT duanfangfang anagingrelatedgenesignaturebasedmodelforriskstratificationandprognosispredictioninlungsquamouscarcinoma
AT chensi anagingrelatedgenesignaturebasedmodelforriskstratificationandprognosispredictioninlungsquamouscarcinoma
AT wangjunye anagingrelatedgenesignaturebasedmodelforriskstratificationandprognosispredictioninlungsquamouscarcinoma
AT zhaozerui anagingrelatedgenesignaturebasedmodelforriskstratificationandprognosispredictioninlungsquamouscarcinoma
AT wangyizhi anagingrelatedgenesignaturebasedmodelforriskstratificationandprognosispredictioninlungsquamouscarcinoma
AT raobingyu anagingrelatedgenesignaturebasedmodelforriskstratificationandprognosispredictioninlungsquamouscarcinoma
AT linyaobin anagingrelatedgenesignaturebasedmodelforriskstratificationandprognosispredictioninlungsquamouscarcinoma
AT longhao anagingrelatedgenesignaturebasedmodelforriskstratificationandprognosispredictioninlungsquamouscarcinoma
AT zhaiwenyu agingrelatedgenesignaturebasedmodelforriskstratificationandprognosispredictioninlungsquamouscarcinoma
AT duanfangfang agingrelatedgenesignaturebasedmodelforriskstratificationandprognosispredictioninlungsquamouscarcinoma
AT chensi agingrelatedgenesignaturebasedmodelforriskstratificationandprognosispredictioninlungsquamouscarcinoma
AT wangjunye agingrelatedgenesignaturebasedmodelforriskstratificationandprognosispredictioninlungsquamouscarcinoma
AT zhaozerui agingrelatedgenesignaturebasedmodelforriskstratificationandprognosispredictioninlungsquamouscarcinoma
AT wangyizhi agingrelatedgenesignaturebasedmodelforriskstratificationandprognosispredictioninlungsquamouscarcinoma
AT raobingyu agingrelatedgenesignaturebasedmodelforriskstratificationandprognosispredictioninlungsquamouscarcinoma
AT linyaobin agingrelatedgenesignaturebasedmodelforriskstratificationandprognosispredictioninlungsquamouscarcinoma
AT longhao agingrelatedgenesignaturebasedmodelforriskstratificationandprognosispredictioninlungsquamouscarcinoma