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Integrated Bioinformatics Analysis Identifies a New Stemness Index-Related Survival Model for Prognostic Prediction in Lung Adenocarcinoma

Background: Lung adenocarcinoma (LUAD) is one of the most lethal malignancies and is currently lacking in effective biomarkers to assist in diagnosis and therapy. The aim of this study is to investigate hub genes and develop a risk signature for predicting prognosis of LUAD patients. Methods: RNA-se...

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Autores principales: Hou, Shaohui, Xu, Hongrui, Liu, Shuzhong, Yang, Bingjun, Li, Li, Zhao, Hui, Jiang, Chunyang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9026767/
https://www.ncbi.nlm.nih.gov/pubmed/35464867
http://dx.doi.org/10.3389/fgene.2022.860268
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author Hou, Shaohui
Xu, Hongrui
Liu, Shuzhong
Yang, Bingjun
Li, Li
Zhao, Hui
Jiang, Chunyang
author_facet Hou, Shaohui
Xu, Hongrui
Liu, Shuzhong
Yang, Bingjun
Li, Li
Zhao, Hui
Jiang, Chunyang
author_sort Hou, Shaohui
collection PubMed
description Background: Lung adenocarcinoma (LUAD) is one of the most lethal malignancies and is currently lacking in effective biomarkers to assist in diagnosis and therapy. The aim of this study is to investigate hub genes and develop a risk signature for predicting prognosis of LUAD patients. Methods: RNA-sequencing data and relevant clinical data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was performed to identify hub genes associated with mRNA expression-based stemness indices (mRNAsi) in TCGA. We utilized LASSO Cox regression to assemble our predictive model. To validate our predictive model, me applied it to an external cohort. Results: mRNAsi index was significantly associated with the tissue type of LUAD, and high mRNAsi scores may have a protective influence on survival outcomes seen in LUAD patients. WGCNA indicated that the turquoise module was significantly correlated with the mRNAsi. We identified a 9-gene signature (CENPW, MCM2, STIL, RACGAP1, ASPM, KIF14, ANLN, CDCA8, and PLK1) from the turquoise module that could effectively identify a high-risk subset of these patients. Using the Kaplan-Meier survival curve, as well as the time-dependent receiver operating characteristic (tdROC) analysis, we determined that this gene signature had a strong predictive ability (AUC = 0.716). By combining the 9-gene signature with clinicopathological features, we were able to design a predictive nomogram. Finally, we additionally validated the 9-gene signature using two external cohorts from GEO and the model proved to be of high value. Conclusion: Our study shows that the 9-gene mRNAsi-related signature can predict the prognosis of LUAD patient and contribute to decisions in the treatment and prevention of LUAD patients.
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spelling pubmed-90267672022-04-23 Integrated Bioinformatics Analysis Identifies a New Stemness Index-Related Survival Model for Prognostic Prediction in Lung Adenocarcinoma Hou, Shaohui Xu, Hongrui Liu, Shuzhong Yang, Bingjun Li, Li Zhao, Hui Jiang, Chunyang Front Genet Genetics Background: Lung adenocarcinoma (LUAD) is one of the most lethal malignancies and is currently lacking in effective biomarkers to assist in diagnosis and therapy. The aim of this study is to investigate hub genes and develop a risk signature for predicting prognosis of LUAD patients. Methods: RNA-sequencing data and relevant clinical data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was performed to identify hub genes associated with mRNA expression-based stemness indices (mRNAsi) in TCGA. We utilized LASSO Cox regression to assemble our predictive model. To validate our predictive model, me applied it to an external cohort. Results: mRNAsi index was significantly associated with the tissue type of LUAD, and high mRNAsi scores may have a protective influence on survival outcomes seen in LUAD patients. WGCNA indicated that the turquoise module was significantly correlated with the mRNAsi. We identified a 9-gene signature (CENPW, MCM2, STIL, RACGAP1, ASPM, KIF14, ANLN, CDCA8, and PLK1) from the turquoise module that could effectively identify a high-risk subset of these patients. Using the Kaplan-Meier survival curve, as well as the time-dependent receiver operating characteristic (tdROC) analysis, we determined that this gene signature had a strong predictive ability (AUC = 0.716). By combining the 9-gene signature with clinicopathological features, we were able to design a predictive nomogram. Finally, we additionally validated the 9-gene signature using two external cohorts from GEO and the model proved to be of high value. Conclusion: Our study shows that the 9-gene mRNAsi-related signature can predict the prognosis of LUAD patient and contribute to decisions in the treatment and prevention of LUAD patients. Frontiers Media S.A. 2022-04-08 /pmc/articles/PMC9026767/ /pubmed/35464867 http://dx.doi.org/10.3389/fgene.2022.860268 Text en Copyright © 2022 Hou, Xu, Liu, Yang, Li, Zhao and Jiang. 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
Hou, Shaohui
Xu, Hongrui
Liu, Shuzhong
Yang, Bingjun
Li, Li
Zhao, Hui
Jiang, Chunyang
Integrated Bioinformatics Analysis Identifies a New Stemness Index-Related Survival Model for Prognostic Prediction in Lung Adenocarcinoma
title Integrated Bioinformatics Analysis Identifies a New Stemness Index-Related Survival Model for Prognostic Prediction in Lung Adenocarcinoma
title_full Integrated Bioinformatics Analysis Identifies a New Stemness Index-Related Survival Model for Prognostic Prediction in Lung Adenocarcinoma
title_fullStr Integrated Bioinformatics Analysis Identifies a New Stemness Index-Related Survival Model for Prognostic Prediction in Lung Adenocarcinoma
title_full_unstemmed Integrated Bioinformatics Analysis Identifies a New Stemness Index-Related Survival Model for Prognostic Prediction in Lung Adenocarcinoma
title_short Integrated Bioinformatics Analysis Identifies a New Stemness Index-Related Survival Model for Prognostic Prediction in Lung Adenocarcinoma
title_sort integrated bioinformatics analysis identifies a new stemness index-related survival model for prognostic prediction in lung adenocarcinoma
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9026767/
https://www.ncbi.nlm.nih.gov/pubmed/35464867
http://dx.doi.org/10.3389/fgene.2022.860268
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