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Identifying Common Genes Related to Platelet and Immunity for Lung Adenocarcinoma Prognosis Prediction
BACKGROUND: Although 1000s of immune-related and platelet receptor-related genes have been identified in lung adenocarcinoma, their role in prognosis prediction remains unclear. METHODS: We downloaded mRNA data from the Cancer Genome Atlas Dataset (TCGA), and GSE68465 or GSE14814 data sets from the...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7658298/ https://www.ncbi.nlm.nih.gov/pubmed/33195410 http://dx.doi.org/10.3389/fmolb.2020.563142 |
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author | Zhou, Chengmao Wang, Ying Lei, Lei Ji, Mu-Huo Yang, Jian-Jun Xia, Hongping |
author_facet | Zhou, Chengmao Wang, Ying Lei, Lei Ji, Mu-Huo Yang, Jian-Jun Xia, Hongping |
author_sort | Zhou, Chengmao |
collection | PubMed |
description | BACKGROUND: Although 1000s of immune-related and platelet receptor-related genes have been identified in lung adenocarcinoma, their role in prognosis prediction remains unclear. METHODS: We downloaded mRNA data from the Cancer Genome Atlas Dataset (TCGA), and GSE68465 or GSE14814 data sets from the Gene Expression Omnibus (GEO) database. RESULTS: The high-risk group’s overall survival (OS) time was lower than that of the low-risk group’s in TCGA (p = 1.15e-03). Additionally, the risk score was an independent prognostic survival factor for lung adenocarcinoma patients in TCGA (HR = 2.136, 95%CI = 1.553–2.937, p < 0.001). The model’s prognostic performance was verified with two independent GEO cohorts (GSE68465 and GSE14814). We also developed a nomogram and provided free webpage prediction tools.() The mechanism of the high-risk group in this risk score may be have been related to somatic mutations and copy number changes. In addition, this risk score can distinguish the prognosis of the other two cancers (ACC, p < 0.001 and KIRP, p < 0.001). Also, among the other seven cancers, the OS prognosis for high and low risk groups show wide variation (p < 0.05). CONCLUSION: Our research demonstrates that CCNA2 and TGFB2 are potential diagnostic and prognostic biomarkers, as well as therapeutic targets in lung adenocarcinoma (LUAD). We also determined a novel and reliable prognostic score for lung adenocarcinoma prognosis. The online nomogram prediction tool that contains this risk score may also help clinical medical staff. |
format | Online Article Text |
id | pubmed-7658298 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-76582982020-11-13 Identifying Common Genes Related to Platelet and Immunity for Lung Adenocarcinoma Prognosis Prediction Zhou, Chengmao Wang, Ying Lei, Lei Ji, Mu-Huo Yang, Jian-Jun Xia, Hongping Front Mol Biosci Molecular Biosciences BACKGROUND: Although 1000s of immune-related and platelet receptor-related genes have been identified in lung adenocarcinoma, their role in prognosis prediction remains unclear. METHODS: We downloaded mRNA data from the Cancer Genome Atlas Dataset (TCGA), and GSE68465 or GSE14814 data sets from the Gene Expression Omnibus (GEO) database. RESULTS: The high-risk group’s overall survival (OS) time was lower than that of the low-risk group’s in TCGA (p = 1.15e-03). Additionally, the risk score was an independent prognostic survival factor for lung adenocarcinoma patients in TCGA (HR = 2.136, 95%CI = 1.553–2.937, p < 0.001). The model’s prognostic performance was verified with two independent GEO cohorts (GSE68465 and GSE14814). We also developed a nomogram and provided free webpage prediction tools.() The mechanism of the high-risk group in this risk score may be have been related to somatic mutations and copy number changes. In addition, this risk score can distinguish the prognosis of the other two cancers (ACC, p < 0.001 and KIRP, p < 0.001). Also, among the other seven cancers, the OS prognosis for high and low risk groups show wide variation (p < 0.05). CONCLUSION: Our research demonstrates that CCNA2 and TGFB2 are potential diagnostic and prognostic biomarkers, as well as therapeutic targets in lung adenocarcinoma (LUAD). We also determined a novel and reliable prognostic score for lung adenocarcinoma prognosis. The online nomogram prediction tool that contains this risk score may also help clinical medical staff. Frontiers Media S.A. 2020-10-29 /pmc/articles/PMC7658298/ /pubmed/33195410 http://dx.doi.org/10.3389/fmolb.2020.563142 Text en Copyright © 2020 Zhou, Wang, Lei, Ji, Yang and Xia. http://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 | Molecular Biosciences Zhou, Chengmao Wang, Ying Lei, Lei Ji, Mu-Huo Yang, Jian-Jun Xia, Hongping Identifying Common Genes Related to Platelet and Immunity for Lung Adenocarcinoma Prognosis Prediction |
title | Identifying Common Genes Related to Platelet and Immunity for Lung Adenocarcinoma Prognosis Prediction |
title_full | Identifying Common Genes Related to Platelet and Immunity for Lung Adenocarcinoma Prognosis Prediction |
title_fullStr | Identifying Common Genes Related to Platelet and Immunity for Lung Adenocarcinoma Prognosis Prediction |
title_full_unstemmed | Identifying Common Genes Related to Platelet and Immunity for Lung Adenocarcinoma Prognosis Prediction |
title_short | Identifying Common Genes Related to Platelet and Immunity for Lung Adenocarcinoma Prognosis Prediction |
title_sort | identifying common genes related to platelet and immunity for lung adenocarcinoma prognosis prediction |
topic | Molecular Biosciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7658298/ https://www.ncbi.nlm.nih.gov/pubmed/33195410 http://dx.doi.org/10.3389/fmolb.2020.563142 |
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