Prediction and Analysis of Key Genes in Glioblastoma Based on Bioinformatics

Understanding the mechanisms of glioblastoma at the molecular and structural level is not only interesting for basic science but also valuable for biotechnological application, such as the clinical treatment. In the present study, bioinformatics analysis was performed to reveal and identify the key...

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Detalles Bibliográficos
Autores principales: Long, Hao, Liang, Chaofeng, Zhang, Xi'an, Fang, Luxiong, Wang, Gang, Qi, Songtao, Huo, Haizhong, Song, Ye
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2017
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5278190/
https://www.ncbi.nlm.nih.gov/pubmed/28191466
http://dx.doi.org/10.1155/2017/7653101
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author Long, Hao
Liang, Chaofeng
Zhang, Xi'an
Fang, Luxiong
Wang, Gang
Qi, Songtao
Huo, Haizhong
Song, Ye
author_facet Long, Hao
Liang, Chaofeng
Zhang, Xi'an
Fang, Luxiong
Wang, Gang
Qi, Songtao
Huo, Haizhong
Song, Ye
author_sort Long, Hao
collection PubMed
description Understanding the mechanisms of glioblastoma at the molecular and structural level is not only interesting for basic science but also valuable for biotechnological application, such as the clinical treatment. In the present study, bioinformatics analysis was performed to reveal and identify the key genes of glioblastoma multiforme (GBM). The results obtained in the present study signified the importance of some genes, such as COL3A1, FN1, and MMP9, for glioblastoma. Based on the selected genes, a prediction model was built, which achieved 94.4% prediction accuracy. These findings might provide more insights into the genetic basis of glioblastoma.
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spelling pubmed-52781902017-02-12 Prediction and Analysis of Key Genes in Glioblastoma Based on Bioinformatics Long, Hao Liang, Chaofeng Zhang, Xi'an Fang, Luxiong Wang, Gang Qi, Songtao Huo, Haizhong Song, Ye Biomed Res Int Research Article Understanding the mechanisms of glioblastoma at the molecular and structural level is not only interesting for basic science but also valuable for biotechnological application, such as the clinical treatment. In the present study, bioinformatics analysis was performed to reveal and identify the key genes of glioblastoma multiforme (GBM). The results obtained in the present study signified the importance of some genes, such as COL3A1, FN1, and MMP9, for glioblastoma. Based on the selected genes, a prediction model was built, which achieved 94.4% prediction accuracy. These findings might provide more insights into the genetic basis of glioblastoma. Hindawi Publishing Corporation 2017 2017-01-16 /pmc/articles/PMC5278190/ /pubmed/28191466 http://dx.doi.org/10.1155/2017/7653101 Text en Copyright © 2017 Hao Long et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Long, Hao
Liang, Chaofeng
Zhang, Xi'an
Fang, Luxiong
Wang, Gang
Qi, Songtao
Huo, Haizhong
Song, Ye
Prediction and Analysis of Key Genes in Glioblastoma Based on Bioinformatics
title Prediction and Analysis of Key Genes in Glioblastoma Based on Bioinformatics
title_full Prediction and Analysis of Key Genes in Glioblastoma Based on Bioinformatics
title_fullStr Prediction and Analysis of Key Genes in Glioblastoma Based on Bioinformatics
title_full_unstemmed Prediction and Analysis of Key Genes in Glioblastoma Based on Bioinformatics
title_short Prediction and Analysis of Key Genes in Glioblastoma Based on Bioinformatics
title_sort prediction and analysis of key genes in glioblastoma based on bioinformatics
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5278190/
https://www.ncbi.nlm.nih.gov/pubmed/28191466
http://dx.doi.org/10.1155/2017/7653101
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