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Integrating Genomic Data with Transcriptomic Data for Improved Survival Prediction for Adult Diffuse Glioma
Background: Glioma is the most common type of primary central nervous system tumors. However, the relationship between gene mutations and transcriptome is unclear in diffuse glioma, and there are no systemic analyses with regard to the genotype-phenotype association currently. Methods: We performed...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Ivyspring International Publisher
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7171505/ https://www.ncbi.nlm.nih.gov/pubmed/32328184 http://dx.doi.org/10.7150/jca.44032 |
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author | Yang, Qi Xiong, Yi Jiang, Nian Zeng, Fanyuan Huang, Chunhai Li, Xuejun |
author_facet | Yang, Qi Xiong, Yi Jiang, Nian Zeng, Fanyuan Huang, Chunhai Li, Xuejun |
author_sort | Yang, Qi |
collection | PubMed |
description | Background: Glioma is the most common type of primary central nervous system tumors. However, the relationship between gene mutations and transcriptome is unclear in diffuse glioma, and there are no systemic analyses with regard to the genotype-phenotype association currently. Methods: We performed the multi-omics analysis in large glioblastoma multiforme (GBM, n=126) and low-grade glioma (LGG, n=481) cohorts obtained from The Cancer Genome Atlas (TCGA) database. We used multivariate linear models to evaluate associations between driver gene mutations and global gene expression. We developed generalized linear models to evaluate associations between genetic/expression factors with clinicopathologic features. Multivariate Cox proportional hazards models were used to predict the overall survival. Results: The potential relationship between genotype and genetics, clinical as well as pathologic features, on diffused glioma was observed. At least one driver mutation correlated with expression changes of about 10% of genes in GBMs while about 80% of genes in LGGs. The strongest association between mutations and expression changes was observed for DRG2 and LRCC41 gene in GBMs and LGGs, respectively. Additionally, the association between genomics features and clinicopathologic features suggested the different underlying molecular mechanisms in molecular subtypes or histology subtypes. For predicting survival, among genetics, transcriptome and clinical variables, transcriptome features made the largest contribution. By combining all the available data, the accuracy in predicting the prognosis of diffuse glioma in patients was also improved. Conclusion: Our study results revealed the influences of driver gene mutations on global gene expression in diffuse glioma patients. A more accurate model in predicting the prognosis of patients was achieved when combining with all the available data than just transcriptomic data. |
format | Online Article Text |
id | pubmed-7171505 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Ivyspring International Publisher |
record_format | MEDLINE/PubMed |
spelling | pubmed-71715052020-04-23 Integrating Genomic Data with Transcriptomic Data for Improved Survival Prediction for Adult Diffuse Glioma Yang, Qi Xiong, Yi Jiang, Nian Zeng, Fanyuan Huang, Chunhai Li, Xuejun J Cancer Research Paper Background: Glioma is the most common type of primary central nervous system tumors. However, the relationship between gene mutations and transcriptome is unclear in diffuse glioma, and there are no systemic analyses with regard to the genotype-phenotype association currently. Methods: We performed the multi-omics analysis in large glioblastoma multiforme (GBM, n=126) and low-grade glioma (LGG, n=481) cohorts obtained from The Cancer Genome Atlas (TCGA) database. We used multivariate linear models to evaluate associations between driver gene mutations and global gene expression. We developed generalized linear models to evaluate associations between genetic/expression factors with clinicopathologic features. Multivariate Cox proportional hazards models were used to predict the overall survival. Results: The potential relationship between genotype and genetics, clinical as well as pathologic features, on diffused glioma was observed. At least one driver mutation correlated with expression changes of about 10% of genes in GBMs while about 80% of genes in LGGs. The strongest association between mutations and expression changes was observed for DRG2 and LRCC41 gene in GBMs and LGGs, respectively. Additionally, the association between genomics features and clinicopathologic features suggested the different underlying molecular mechanisms in molecular subtypes or histology subtypes. For predicting survival, among genetics, transcriptome and clinical variables, transcriptome features made the largest contribution. By combining all the available data, the accuracy in predicting the prognosis of diffuse glioma in patients was also improved. Conclusion: Our study results revealed the influences of driver gene mutations on global gene expression in diffuse glioma patients. A more accurate model in predicting the prognosis of patients was achieved when combining with all the available data than just transcriptomic data. Ivyspring International Publisher 2020-04-06 /pmc/articles/PMC7171505/ /pubmed/32328184 http://dx.doi.org/10.7150/jca.44032 Text en © The author(s) This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions. |
spellingShingle | Research Paper Yang, Qi Xiong, Yi Jiang, Nian Zeng, Fanyuan Huang, Chunhai Li, Xuejun Integrating Genomic Data with Transcriptomic Data for Improved Survival Prediction for Adult Diffuse Glioma |
title | Integrating Genomic Data with Transcriptomic Data for Improved Survival Prediction for Adult Diffuse Glioma |
title_full | Integrating Genomic Data with Transcriptomic Data for Improved Survival Prediction for Adult Diffuse Glioma |
title_fullStr | Integrating Genomic Data with Transcriptomic Data for Improved Survival Prediction for Adult Diffuse Glioma |
title_full_unstemmed | Integrating Genomic Data with Transcriptomic Data for Improved Survival Prediction for Adult Diffuse Glioma |
title_short | Integrating Genomic Data with Transcriptomic Data for Improved Survival Prediction for Adult Diffuse Glioma |
title_sort | integrating genomic data with transcriptomic data for improved survival prediction for adult diffuse glioma |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7171505/ https://www.ncbi.nlm.nih.gov/pubmed/32328184 http://dx.doi.org/10.7150/jca.44032 |
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