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High expression of MCM10 is predictive of poor outcomes in lung adenocarcinoma
BACKGROUNDS: Lung adenocarcinoma is a complex disease that results in over 1.8 million deaths a year. Recent advancements in treating and managing lung adenocarcinoma have led to modest decreases in associated mortality rates, owing in part to the multifactorial etiology of the disease. Novel progno...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7866887/ https://www.ncbi.nlm.nih.gov/pubmed/33604163 http://dx.doi.org/10.7717/peerj.10560 |
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author | Shao, Mingrui Yang, Shize Dong, Siyuan |
author_facet | Shao, Mingrui Yang, Shize Dong, Siyuan |
author_sort | Shao, Mingrui |
collection | PubMed |
description | BACKGROUNDS: Lung adenocarcinoma is a complex disease that results in over 1.8 million deaths a year. Recent advancements in treating and managing lung adenocarcinoma have led to modest decreases in associated mortality rates, owing in part to the multifactorial etiology of the disease. Novel prognostic biomarkers are needed to accurately stage the disease and act as the basis of adjuvant treatments. MATERIAL AND METHODS: The microarray datasets GSE75037, GSE31210 and GSE32863 were downloaded from the Gene Expression Omnibus (GEO) database to identify prognostic biomarkers for lung adenocarcinoma and therapy. The differentially expressed genes (DEGs) were identified by GEO2R. Functional and pathway enrichment analysis were performed by Kyoto Encyclopedia of Genes and Genomes and Gene Ontology (GO). Validation was performed based on 72 pairs of lung adenocarcinoma and adjacent normal lung tissues. RESULTS: Results showed that the DEGs were mainly focused on cell cycle and DNA replication initiation. Forty-one hub genes were identified and further analyzed by CytoScape. Here, we provide evidence which suggests MCM10 is a potential target with prognostic, diagnostic and therapeutic value. We base this on an integrated approach of comprehensive bioinformatics analysis and in vitro validation using the A549 lung adenocarcinoma cell line. We show that MCM10 overexpression correlates with a poor prognosis, while silencing of this gene decreases aberrant growth by 2-fold. Finally, evaluation of 72 clinical biopsy samples suggests that overexpression of MCM10 in the lung adenocarcinoma highly correlates with larger tumor size. Together, this work suggests that MCM10 may be a clinically relevant gene with both predictive and therapeutic value in lung adenocarcinoma. |
format | Online Article Text |
id | pubmed-7866887 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78668872021-02-17 High expression of MCM10 is predictive of poor outcomes in lung adenocarcinoma Shao, Mingrui Yang, Shize Dong, Siyuan PeerJ Bioinformatics BACKGROUNDS: Lung adenocarcinoma is a complex disease that results in over 1.8 million deaths a year. Recent advancements in treating and managing lung adenocarcinoma have led to modest decreases in associated mortality rates, owing in part to the multifactorial etiology of the disease. Novel prognostic biomarkers are needed to accurately stage the disease and act as the basis of adjuvant treatments. MATERIAL AND METHODS: The microarray datasets GSE75037, GSE31210 and GSE32863 were downloaded from the Gene Expression Omnibus (GEO) database to identify prognostic biomarkers for lung adenocarcinoma and therapy. The differentially expressed genes (DEGs) were identified by GEO2R. Functional and pathway enrichment analysis were performed by Kyoto Encyclopedia of Genes and Genomes and Gene Ontology (GO). Validation was performed based on 72 pairs of lung adenocarcinoma and adjacent normal lung tissues. RESULTS: Results showed that the DEGs were mainly focused on cell cycle and DNA replication initiation. Forty-one hub genes were identified and further analyzed by CytoScape. Here, we provide evidence which suggests MCM10 is a potential target with prognostic, diagnostic and therapeutic value. We base this on an integrated approach of comprehensive bioinformatics analysis and in vitro validation using the A549 lung adenocarcinoma cell line. We show that MCM10 overexpression correlates with a poor prognosis, while silencing of this gene decreases aberrant growth by 2-fold. Finally, evaluation of 72 clinical biopsy samples suggests that overexpression of MCM10 in the lung adenocarcinoma highly correlates with larger tumor size. Together, this work suggests that MCM10 may be a clinically relevant gene with both predictive and therapeutic value in lung adenocarcinoma. PeerJ Inc. 2021-02-03 /pmc/articles/PMC7866887/ /pubmed/33604163 http://dx.doi.org/10.7717/peerj.10560 Text en © 2021 Shao et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Shao, Mingrui Yang, Shize Dong, Siyuan High expression of MCM10 is predictive of poor outcomes in lung adenocarcinoma |
title | High expression of MCM10 is predictive of poor outcomes in lung adenocarcinoma |
title_full | High expression of MCM10 is predictive of poor outcomes in lung adenocarcinoma |
title_fullStr | High expression of MCM10 is predictive of poor outcomes in lung adenocarcinoma |
title_full_unstemmed | High expression of MCM10 is predictive of poor outcomes in lung adenocarcinoma |
title_short | High expression of MCM10 is predictive of poor outcomes in lung adenocarcinoma |
title_sort | high expression of mcm10 is predictive of poor outcomes in lung adenocarcinoma |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7866887/ https://www.ncbi.nlm.nih.gov/pubmed/33604163 http://dx.doi.org/10.7717/peerj.10560 |
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