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Weighted Gene Co-expression Network Analysis Identified a Novel Thirteen-Gene Signature Associated With Progression, Prognosis, and Immune Microenvironment of Colon Adenocarcinoma Patients

Colon adenocarcinoma (COAD) is one of the most common malignant tumors and has high migration and invasion capacity. In this study, we attempted to establish a multigene signature for predicting the prognosis of COAD patients. Weighted gene co-expression network analysis and differential gene expres...

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Autores principales: Zhang, Cangang, Zhao, Zhe, Liu, Haibo, Yao, Shukun, Zhao, Dongyan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8312261/
https://www.ncbi.nlm.nih.gov/pubmed/34322151
http://dx.doi.org/10.3389/fgene.2021.657658
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author Zhang, Cangang
Zhao, Zhe
Liu, Haibo
Yao, Shukun
Zhao, Dongyan
author_facet Zhang, Cangang
Zhao, Zhe
Liu, Haibo
Yao, Shukun
Zhao, Dongyan
author_sort Zhang, Cangang
collection PubMed
description Colon adenocarcinoma (COAD) is one of the most common malignant tumors and has high migration and invasion capacity. In this study, we attempted to establish a multigene signature for predicting the prognosis of COAD patients. Weighted gene co-expression network analysis and differential gene expression analysis methods were first applied to identify differentially co-expressed genes between COAD tissues and normal tissues from the Cancer Genome Atlas (TCGA)-COAD dataset and GSE39582 dataset, and a total of 309 overlapping genes were screened out. Then, our study employed TCGA-COAD cohort as the training dataset and an independent cohort by merging the GES39582 and GSE17536 datasets as the testing dataset. After univariate and multivariate Cox regression analyses were performed for these overlapping genes and overall survival (OS) of COAD patients in the training dataset, a 13-gene signature was constructed to divide COAD patients into high- and low-risk subgroups with significantly different OS. The testing dataset exhibited the same results utilizing the same predictive signature. The area under the curve of receiver operating characteristic analysis for predicting OS in the training and testing datasets were 0.789 and 0.868, respectively, which revealed the enhanced predictive power of the signature. Multivariate Cox regression analysis further suggested that the 13-gene signature could independently predict OS. Among the 13 prognostic genes, NAT1 and NAT2 were downregulated with deep deletions in tumor tissues in multiple COAD cohorts and exhibited significant correlations with poorer OS based on the GEPIA database. Notably, NAT1 and NAT2 expression levels were positively correlated with infiltrating levels of CD8+ T cells and dendritic cells, exhibiting a foundation for further research investigating the antitumor immune roles played by NAT1 and NAT2 in COAD. Taken together, the results of our study showed that the 13-gene signature could efficiently predict OS and that NAT1 and NAT2 could function as biomarkers for prognosis and the immune response in COAD.
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spelling pubmed-83122612021-07-27 Weighted Gene Co-expression Network Analysis Identified a Novel Thirteen-Gene Signature Associated With Progression, Prognosis, and Immune Microenvironment of Colon Adenocarcinoma Patients Zhang, Cangang Zhao, Zhe Liu, Haibo Yao, Shukun Zhao, Dongyan Front Genet Genetics Colon adenocarcinoma (COAD) is one of the most common malignant tumors and has high migration and invasion capacity. In this study, we attempted to establish a multigene signature for predicting the prognosis of COAD patients. Weighted gene co-expression network analysis and differential gene expression analysis methods were first applied to identify differentially co-expressed genes between COAD tissues and normal tissues from the Cancer Genome Atlas (TCGA)-COAD dataset and GSE39582 dataset, and a total of 309 overlapping genes were screened out. Then, our study employed TCGA-COAD cohort as the training dataset and an independent cohort by merging the GES39582 and GSE17536 datasets as the testing dataset. After univariate and multivariate Cox regression analyses were performed for these overlapping genes and overall survival (OS) of COAD patients in the training dataset, a 13-gene signature was constructed to divide COAD patients into high- and low-risk subgroups with significantly different OS. The testing dataset exhibited the same results utilizing the same predictive signature. The area under the curve of receiver operating characteristic analysis for predicting OS in the training and testing datasets were 0.789 and 0.868, respectively, which revealed the enhanced predictive power of the signature. Multivariate Cox regression analysis further suggested that the 13-gene signature could independently predict OS. Among the 13 prognostic genes, NAT1 and NAT2 were downregulated with deep deletions in tumor tissues in multiple COAD cohorts and exhibited significant correlations with poorer OS based on the GEPIA database. Notably, NAT1 and NAT2 expression levels were positively correlated with infiltrating levels of CD8+ T cells and dendritic cells, exhibiting a foundation for further research investigating the antitumor immune roles played by NAT1 and NAT2 in COAD. Taken together, the results of our study showed that the 13-gene signature could efficiently predict OS and that NAT1 and NAT2 could function as biomarkers for prognosis and the immune response in COAD. Frontiers Media S.A. 2021-07-12 /pmc/articles/PMC8312261/ /pubmed/34322151 http://dx.doi.org/10.3389/fgene.2021.657658 Text en Copyright © 2021 Zhang, Zhao, Liu, Yao and Zhao. 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 Genetics
Zhang, Cangang
Zhao, Zhe
Liu, Haibo
Yao, Shukun
Zhao, Dongyan
Weighted Gene Co-expression Network Analysis Identified a Novel Thirteen-Gene Signature Associated With Progression, Prognosis, and Immune Microenvironment of Colon Adenocarcinoma Patients
title Weighted Gene Co-expression Network Analysis Identified a Novel Thirteen-Gene Signature Associated With Progression, Prognosis, and Immune Microenvironment of Colon Adenocarcinoma Patients
title_full Weighted Gene Co-expression Network Analysis Identified a Novel Thirteen-Gene Signature Associated With Progression, Prognosis, and Immune Microenvironment of Colon Adenocarcinoma Patients
title_fullStr Weighted Gene Co-expression Network Analysis Identified a Novel Thirteen-Gene Signature Associated With Progression, Prognosis, and Immune Microenvironment of Colon Adenocarcinoma Patients
title_full_unstemmed Weighted Gene Co-expression Network Analysis Identified a Novel Thirteen-Gene Signature Associated With Progression, Prognosis, and Immune Microenvironment of Colon Adenocarcinoma Patients
title_short Weighted Gene Co-expression Network Analysis Identified a Novel Thirteen-Gene Signature Associated With Progression, Prognosis, and Immune Microenvironment of Colon Adenocarcinoma Patients
title_sort weighted gene co-expression network analysis identified a novel thirteen-gene signature associated with progression, prognosis, and immune microenvironment of colon adenocarcinoma patients
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8312261/
https://www.ncbi.nlm.nih.gov/pubmed/34322151
http://dx.doi.org/10.3389/fgene.2021.657658
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