Developing the novel bioinformatics algorithms to systematically investigate the connections among survival time, key genes and proteins for Glioblastoma multiforme

BACKGROUND: Glioblastoma multiforme (GBM) is one of the most common malignant brain tumors and its average survival time is less than 1 year after diagnosis. RESULTS: Firstly, this study aims to develop the novel survival analysis algorithms to explore the key genes and proteins related to GBM. Then...

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Autores principales: You, Yujie, Ru, Xufang, Lei, Wanjing, Li, Tingting, Xiao, Ming, Zheng, Huiru, Chen, Yujie, Zhang, Le
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7646399/
https://www.ncbi.nlm.nih.gov/pubmed/32938364
http://dx.doi.org/10.1186/s12859-020-03674-4
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author You, Yujie
Ru, Xufang
Lei, Wanjing
Li, Tingting
Xiao, Ming
Zheng, Huiru
Chen, Yujie
Zhang, Le
author_facet You, Yujie
Ru, Xufang
Lei, Wanjing
Li, Tingting
Xiao, Ming
Zheng, Huiru
Chen, Yujie
Zhang, Le
author_sort You, Yujie
collection PubMed
description BACKGROUND: Glioblastoma multiforme (GBM) is one of the most common malignant brain tumors and its average survival time is less than 1 year after diagnosis. RESULTS: Firstly, this study aims to develop the novel survival analysis algorithms to explore the key genes and proteins related to GBM. Then, we explore the significant correlation between AEBP1 upregulation and increased EGFR expression in primary glioma, and employ a glioma cell line LN229 to identify relevant proteins and molecular pathways through protein network analysis. Finally, we identify that AEBP1 exerts its tumor-promoting effects by mainly activating mTOR pathway in Glioma. CONCLUSIONS: We summarize the whole process of the experiment and discuss how to expand our experiment in the future.
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spelling pubmed-76463992020-11-09 Developing the novel bioinformatics algorithms to systematically investigate the connections among survival time, key genes and proteins for Glioblastoma multiforme You, Yujie Ru, Xufang Lei, Wanjing Li, Tingting Xiao, Ming Zheng, Huiru Chen, Yujie Zhang, Le BMC Bioinformatics Research BACKGROUND: Glioblastoma multiforme (GBM) is one of the most common malignant brain tumors and its average survival time is less than 1 year after diagnosis. RESULTS: Firstly, this study aims to develop the novel survival analysis algorithms to explore the key genes and proteins related to GBM. Then, we explore the significant correlation between AEBP1 upregulation and increased EGFR expression in primary glioma, and employ a glioma cell line LN229 to identify relevant proteins and molecular pathways through protein network analysis. Finally, we identify that AEBP1 exerts its tumor-promoting effects by mainly activating mTOR pathway in Glioma. CONCLUSIONS: We summarize the whole process of the experiment and discuss how to expand our experiment in the future. BioMed Central 2020-09-17 /pmc/articles/PMC7646399/ /pubmed/32938364 http://dx.doi.org/10.1186/s12859-020-03674-4 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
You, Yujie
Ru, Xufang
Lei, Wanjing
Li, Tingting
Xiao, Ming
Zheng, Huiru
Chen, Yujie
Zhang, Le
Developing the novel bioinformatics algorithms to systematically investigate the connections among survival time, key genes and proteins for Glioblastoma multiforme
title Developing the novel bioinformatics algorithms to systematically investigate the connections among survival time, key genes and proteins for Glioblastoma multiforme
title_full Developing the novel bioinformatics algorithms to systematically investigate the connections among survival time, key genes and proteins for Glioblastoma multiforme
title_fullStr Developing the novel bioinformatics algorithms to systematically investigate the connections among survival time, key genes and proteins for Glioblastoma multiforme
title_full_unstemmed Developing the novel bioinformatics algorithms to systematically investigate the connections among survival time, key genes and proteins for Glioblastoma multiforme
title_short Developing the novel bioinformatics algorithms to systematically investigate the connections among survival time, key genes and proteins for Glioblastoma multiforme
title_sort developing the novel bioinformatics algorithms to systematically investigate the connections among survival time, key genes and proteins for glioblastoma multiforme
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7646399/
https://www.ncbi.nlm.nih.gov/pubmed/32938364
http://dx.doi.org/10.1186/s12859-020-03674-4
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