Cargando…
A prognostic nomogram for lung adenocarcinoma based on immune-infiltrating Treg-related genes: from bench to bedside
BACKGROUND: Accumulating evidence suggests that lymphocyte infiltration in the tumor microenvironment is positively correlated with tumorigenesis and development, while the role of Tregs (regulatory T cells) has been controversial. Therefore, we attempted to discover the possible value of Tregs for...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7867791/ https://www.ncbi.nlm.nih.gov/pubmed/33569302 http://dx.doi.org/10.21037/tlcr-20-822 |
_version_ | 1783648343448092672 |
---|---|
author | Wang, Xiaofei Xiao, Zengtuan Gong, Jialin Liu, Zuo Zhang, Mengzhe Zhang, Zhenfa |
author_facet | Wang, Xiaofei Xiao, Zengtuan Gong, Jialin Liu, Zuo Zhang, Mengzhe Zhang, Zhenfa |
author_sort | Wang, Xiaofei |
collection | PubMed |
description | BACKGROUND: Accumulating evidence suggests that lymphocyte infiltration in the tumor microenvironment is positively correlated with tumorigenesis and development, while the role of Tregs (regulatory T cells) has been controversial. Therefore, we attempted to discover the possible value of Tregs for lung adenocarcinoma (LUAD). METHODS: The gene-sequencing data of LUAD were applied from three Gene Expression Omnibus (GEO) datasets—GSE10072, GSE32863 and GSE43458; the corresponding fractions of tumor-infiltrating immune cells were extracted from the CIBERSORTx portal. Weighted gene coexpression network analysis (WGCNA) and protein-protein interaction (PPI) network analysis were conducted to identify the significant module and candidate genes related to Tregs. The role of candidate genes in LUAD was further verified using data from The Cancer Genome Atlas (TCGA) database. Finally, we constructed a nomogram model to predict the prognosis of LUAD by plotting Kaplan-Meier (K-M), receiver operating characteristic (ROC) and calibration curves, which elucidated the performance of the nomogram. RESULTS: In total, 10,047 genes in 333 samples (196 tumor and 137 normal samples) from the GEO database were included. By WGCNA and PPI analysis, we identified a significant black module and 36 candidate genes related to Treg. Next, the candidate genes were verified using TCGA data by Cox regression analysis to screen 13 hub genes that stratified LUAD patients into low- or high-risk groups. Low-risk patients showed a significantly longer overall survival (OS) than high-risk patients (3-year OS: 70.2% vs. 35.2%; 5-year OS: 36.6% vs. 0; P=1.651E-09), and the areas under the ROC curves (AUCs) showed good (3-year AUC: 0.733; 5-year AUC: 0.777). Next, we constructed a survival nomogram combining the hub genes and clinical parameters; the low-risk patients still showed a favorable prognosis compared with that of the high-risk patients (P=7.073E-13), and the AUCs were better (3-year AUC: 0.763; 5-year AUC: 0.873). CONCLUSIONS: We revealed the role of immune-infiltrating Treg-related genes in LUAD and constructed a prognostic nomogram, which may help clinicians make optimal therapeutic decisions and help patients obtain better outcomes. |
format | Online Article Text |
id | pubmed-7867791 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-78677912021-02-09 A prognostic nomogram for lung adenocarcinoma based on immune-infiltrating Treg-related genes: from bench to bedside Wang, Xiaofei Xiao, Zengtuan Gong, Jialin Liu, Zuo Zhang, Mengzhe Zhang, Zhenfa Transl Lung Cancer Res Original Article BACKGROUND: Accumulating evidence suggests that lymphocyte infiltration in the tumor microenvironment is positively correlated with tumorigenesis and development, while the role of Tregs (regulatory T cells) has been controversial. Therefore, we attempted to discover the possible value of Tregs for lung adenocarcinoma (LUAD). METHODS: The gene-sequencing data of LUAD were applied from three Gene Expression Omnibus (GEO) datasets—GSE10072, GSE32863 and GSE43458; the corresponding fractions of tumor-infiltrating immune cells were extracted from the CIBERSORTx portal. Weighted gene coexpression network analysis (WGCNA) and protein-protein interaction (PPI) network analysis were conducted to identify the significant module and candidate genes related to Tregs. The role of candidate genes in LUAD was further verified using data from The Cancer Genome Atlas (TCGA) database. Finally, we constructed a nomogram model to predict the prognosis of LUAD by plotting Kaplan-Meier (K-M), receiver operating characteristic (ROC) and calibration curves, which elucidated the performance of the nomogram. RESULTS: In total, 10,047 genes in 333 samples (196 tumor and 137 normal samples) from the GEO database were included. By WGCNA and PPI analysis, we identified a significant black module and 36 candidate genes related to Treg. Next, the candidate genes were verified using TCGA data by Cox regression analysis to screen 13 hub genes that stratified LUAD patients into low- or high-risk groups. Low-risk patients showed a significantly longer overall survival (OS) than high-risk patients (3-year OS: 70.2% vs. 35.2%; 5-year OS: 36.6% vs. 0; P=1.651E-09), and the areas under the ROC curves (AUCs) showed good (3-year AUC: 0.733; 5-year AUC: 0.777). Next, we constructed a survival nomogram combining the hub genes and clinical parameters; the low-risk patients still showed a favorable prognosis compared with that of the high-risk patients (P=7.073E-13), and the AUCs were better (3-year AUC: 0.763; 5-year AUC: 0.873). CONCLUSIONS: We revealed the role of immune-infiltrating Treg-related genes in LUAD and constructed a prognostic nomogram, which may help clinicians make optimal therapeutic decisions and help patients obtain better outcomes. AME Publishing Company 2021-01 /pmc/articles/PMC7867791/ /pubmed/33569302 http://dx.doi.org/10.21037/tlcr-20-822 Text en 2021 Translational Lung Cancer Research. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Original Article Wang, Xiaofei Xiao, Zengtuan Gong, Jialin Liu, Zuo Zhang, Mengzhe Zhang, Zhenfa A prognostic nomogram for lung adenocarcinoma based on immune-infiltrating Treg-related genes: from bench to bedside |
title | A prognostic nomogram for lung adenocarcinoma based on immune-infiltrating Treg-related genes: from bench to bedside |
title_full | A prognostic nomogram for lung adenocarcinoma based on immune-infiltrating Treg-related genes: from bench to bedside |
title_fullStr | A prognostic nomogram for lung adenocarcinoma based on immune-infiltrating Treg-related genes: from bench to bedside |
title_full_unstemmed | A prognostic nomogram for lung adenocarcinoma based on immune-infiltrating Treg-related genes: from bench to bedside |
title_short | A prognostic nomogram for lung adenocarcinoma based on immune-infiltrating Treg-related genes: from bench to bedside |
title_sort | prognostic nomogram for lung adenocarcinoma based on immune-infiltrating treg-related genes: from bench to bedside |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7867791/ https://www.ncbi.nlm.nih.gov/pubmed/33569302 http://dx.doi.org/10.21037/tlcr-20-822 |
work_keys_str_mv | AT wangxiaofei aprognosticnomogramforlungadenocarcinomabasedonimmuneinfiltratingtregrelatedgenesfrombenchtobedside AT xiaozengtuan aprognosticnomogramforlungadenocarcinomabasedonimmuneinfiltratingtregrelatedgenesfrombenchtobedside AT gongjialin aprognosticnomogramforlungadenocarcinomabasedonimmuneinfiltratingtregrelatedgenesfrombenchtobedside AT liuzuo aprognosticnomogramforlungadenocarcinomabasedonimmuneinfiltratingtregrelatedgenesfrombenchtobedside AT zhangmengzhe aprognosticnomogramforlungadenocarcinomabasedonimmuneinfiltratingtregrelatedgenesfrombenchtobedside AT zhangzhenfa aprognosticnomogramforlungadenocarcinomabasedonimmuneinfiltratingtregrelatedgenesfrombenchtobedside AT wangxiaofei prognosticnomogramforlungadenocarcinomabasedonimmuneinfiltratingtregrelatedgenesfrombenchtobedside AT xiaozengtuan prognosticnomogramforlungadenocarcinomabasedonimmuneinfiltratingtregrelatedgenesfrombenchtobedside AT gongjialin prognosticnomogramforlungadenocarcinomabasedonimmuneinfiltratingtregrelatedgenesfrombenchtobedside AT liuzuo prognosticnomogramforlungadenocarcinomabasedonimmuneinfiltratingtregrelatedgenesfrombenchtobedside AT zhangmengzhe prognosticnomogramforlungadenocarcinomabasedonimmuneinfiltratingtregrelatedgenesfrombenchtobedside AT zhangzhenfa prognosticnomogramforlungadenocarcinomabasedonimmuneinfiltratingtregrelatedgenesfrombenchtobedside |