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Screening and Identification of Key Biomarkers in Lower Grade Glioma via Bioinformatical Analysis
Lower-grade glioma (LGG) is a common type of central nervous system tumor. Due to its complicated pathogenesis, the choice and timing of adjuvant therapy after tumor treatment are controversial. This study explored and identified potential therapeutic targets for lower-grade. The bioinformatics meth...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8759910/ https://www.ncbi.nlm.nih.gov/pubmed/35035531 http://dx.doi.org/10.1155/2022/6959237 |
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author | Guo, Fangzhou Yan, Jun Ling, Guoyuan Chen, Hainan Huang, Qianrong Mu, Junbo Mo, Ligen |
author_facet | Guo, Fangzhou Yan, Jun Ling, Guoyuan Chen, Hainan Huang, Qianrong Mu, Junbo Mo, Ligen |
author_sort | Guo, Fangzhou |
collection | PubMed |
description | Lower-grade glioma (LGG) is a common type of central nervous system tumor. Due to its complicated pathogenesis, the choice and timing of adjuvant therapy after tumor treatment are controversial. This study explored and identified potential therapeutic targets for lower-grade. The bioinformatics method was employed to identify potential biomarkers and LGG molecular mechanisms. Firstly, we selected and downloaded GSE15824, GSE50161, and GSE86574 from the GEO database, which included 40 LGG tissue and 28 normal brain tissue samples. GEO and VENN software identified of 206 codifference expressed genes (DEGs). Secondly, we applied the DAVID online software to investigate the DEG biological function and KEGG pathway enrichment, as well as to build the protein interaction visualization network through Cytoscape and STRING website. Then, the MCODE plug is used in the analysis of 22 core genes. Thirdly, the 22 core genes were analyzed with UNCLA software, of which 18 genes were associated with a worse prognosis. Fourthly, GEPIA was used to analyze the 18 selected genes, and 14 genes were found to be a significantly different expression between LGGs and normal brain tumor samples. Fifthly, hierarchical gene clustering was used to examine the 14 important gene expression differences in different histologies, as well as analysis of the KEGG pathway. Five of these genes were shown to be abundant in the natural killer cell-mediated cytokines (NKCC) and phagosome pathways. The five key genes that may be affected by the immune microenvironment play a crucial role in LGG development. |
format | Online Article Text |
id | pubmed-8759910 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-87599102022-01-15 Screening and Identification of Key Biomarkers in Lower Grade Glioma via Bioinformatical Analysis Guo, Fangzhou Yan, Jun Ling, Guoyuan Chen, Hainan Huang, Qianrong Mu, Junbo Mo, Ligen Appl Bionics Biomech Research Article Lower-grade glioma (LGG) is a common type of central nervous system tumor. Due to its complicated pathogenesis, the choice and timing of adjuvant therapy after tumor treatment are controversial. This study explored and identified potential therapeutic targets for lower-grade. The bioinformatics method was employed to identify potential biomarkers and LGG molecular mechanisms. Firstly, we selected and downloaded GSE15824, GSE50161, and GSE86574 from the GEO database, which included 40 LGG tissue and 28 normal brain tissue samples. GEO and VENN software identified of 206 codifference expressed genes (DEGs). Secondly, we applied the DAVID online software to investigate the DEG biological function and KEGG pathway enrichment, as well as to build the protein interaction visualization network through Cytoscape and STRING website. Then, the MCODE plug is used in the analysis of 22 core genes. Thirdly, the 22 core genes were analyzed with UNCLA software, of which 18 genes were associated with a worse prognosis. Fourthly, GEPIA was used to analyze the 18 selected genes, and 14 genes were found to be a significantly different expression between LGGs and normal brain tumor samples. Fifthly, hierarchical gene clustering was used to examine the 14 important gene expression differences in different histologies, as well as analysis of the KEGG pathway. Five of these genes were shown to be abundant in the natural killer cell-mediated cytokines (NKCC) and phagosome pathways. The five key genes that may be affected by the immune microenvironment play a crucial role in LGG development. Hindawi 2022-01-07 /pmc/articles/PMC8759910/ /pubmed/35035531 http://dx.doi.org/10.1155/2022/6959237 Text en Copyright © 2022 Fangzhou Guo 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 Guo, Fangzhou Yan, Jun Ling, Guoyuan Chen, Hainan Huang, Qianrong Mu, Junbo Mo, Ligen Screening and Identification of Key Biomarkers in Lower Grade Glioma via Bioinformatical Analysis |
title | Screening and Identification of Key Biomarkers in Lower Grade Glioma via Bioinformatical Analysis |
title_full | Screening and Identification of Key Biomarkers in Lower Grade Glioma via Bioinformatical Analysis |
title_fullStr | Screening and Identification of Key Biomarkers in Lower Grade Glioma via Bioinformatical Analysis |
title_full_unstemmed | Screening and Identification of Key Biomarkers in Lower Grade Glioma via Bioinformatical Analysis |
title_short | Screening and Identification of Key Biomarkers in Lower Grade Glioma via Bioinformatical Analysis |
title_sort | screening and identification of key biomarkers in lower grade glioma via bioinformatical analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8759910/ https://www.ncbi.nlm.nih.gov/pubmed/35035531 http://dx.doi.org/10.1155/2022/6959237 |
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