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GPGPS: a robust prognostic gene pair signature of glioma ensembling IDH mutation and 1p/19q co-deletion
MOTIVATION: Many studies have shown that IDH mutation and 1p/19q co-deletion can serve as prognostic signatures of glioma. Although these genetic variations affect the expression of one or more genes, the prognostic value of gene expression related to IDH and 1p/19q status is still unclear. RESULTS:...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9843586/ https://www.ncbi.nlm.nih.gov/pubmed/36637205 http://dx.doi.org/10.1093/bioinformatics/btac850 |
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author | Cheng, Lixin Wu, Haonan Zheng, Xubin Zhang, Ning Zhao, Pengfei Wang, Ran Wu, Qiong Liu, Tao Yang, Xiaojun Geng, Qingshan |
author_facet | Cheng, Lixin Wu, Haonan Zheng, Xubin Zhang, Ning Zhao, Pengfei Wang, Ran Wu, Qiong Liu, Tao Yang, Xiaojun Geng, Qingshan |
author_sort | Cheng, Lixin |
collection | PubMed |
description | MOTIVATION: Many studies have shown that IDH mutation and 1p/19q co-deletion can serve as prognostic signatures of glioma. Although these genetic variations affect the expression of one or more genes, the prognostic value of gene expression related to IDH and 1p/19q status is still unclear. RESULTS: We constructed an ensemble gene pair signature for the risk evaluation and survival prediction of glioma based on the prior knowledge of the IDH and 1p/19q status. First, we separately built two gene pair signatures IDH-GPS and 1p/19q-GPS and elucidated that they were useful transcriptome markers projecting from corresponding genome variations. Then, the gene pairs in these two models were assembled to develop an integrated model named Glioma Prognostic Gene Pair Signature (GPGPS), which demonstrated high area under the curves (AUCs) to predict 1-, 3- and 5-year overall survival (0.92, 0.88 and 0.80) of glioma. GPGPS was superior to the single GPSs and other existing prognostic signatures (avg AUC = 0.70, concordance index = 0.74). In conclusion, the ensemble prognostic signature with 10 gene pairs could serve as an independent predictor for risk stratification and survival prediction in glioma. This study shed light on transferring knowledge from genetic alterations to expression changes to facilitate prognostic studies. AVAILABILITY AND IMPLEMENTATION: Codes are available at https://github.com/Kimxbzheng/GPGPS.git SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-9843586 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-98435862023-01-19 GPGPS: a robust prognostic gene pair signature of glioma ensembling IDH mutation and 1p/19q co-deletion Cheng, Lixin Wu, Haonan Zheng, Xubin Zhang, Ning Zhao, Pengfei Wang, Ran Wu, Qiong Liu, Tao Yang, Xiaojun Geng, Qingshan Bioinformatics Original Paper MOTIVATION: Many studies have shown that IDH mutation and 1p/19q co-deletion can serve as prognostic signatures of glioma. Although these genetic variations affect the expression of one or more genes, the prognostic value of gene expression related to IDH and 1p/19q status is still unclear. RESULTS: We constructed an ensemble gene pair signature for the risk evaluation and survival prediction of glioma based on the prior knowledge of the IDH and 1p/19q status. First, we separately built two gene pair signatures IDH-GPS and 1p/19q-GPS and elucidated that they were useful transcriptome markers projecting from corresponding genome variations. Then, the gene pairs in these two models were assembled to develop an integrated model named Glioma Prognostic Gene Pair Signature (GPGPS), which demonstrated high area under the curves (AUCs) to predict 1-, 3- and 5-year overall survival (0.92, 0.88 and 0.80) of glioma. GPGPS was superior to the single GPSs and other existing prognostic signatures (avg AUC = 0.70, concordance index = 0.74). In conclusion, the ensemble prognostic signature with 10 gene pairs could serve as an independent predictor for risk stratification and survival prediction in glioma. This study shed light on transferring knowledge from genetic alterations to expression changes to facilitate prognostic studies. AVAILABILITY AND IMPLEMENTATION: Codes are available at https://github.com/Kimxbzheng/GPGPS.git SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2023-01-13 /pmc/articles/PMC9843586/ /pubmed/36637205 http://dx.doi.org/10.1093/bioinformatics/btac850 Text en © The Author(s) 2023. Published by Oxford University Press. 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 reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Paper Cheng, Lixin Wu, Haonan Zheng, Xubin Zhang, Ning Zhao, Pengfei Wang, Ran Wu, Qiong Liu, Tao Yang, Xiaojun Geng, Qingshan GPGPS: a robust prognostic gene pair signature of glioma ensembling IDH mutation and 1p/19q co-deletion |
title | GPGPS: a robust prognostic gene pair signature of glioma ensembling IDH mutation and 1p/19q co-deletion |
title_full | GPGPS: a robust prognostic gene pair signature of glioma ensembling IDH mutation and 1p/19q co-deletion |
title_fullStr | GPGPS: a robust prognostic gene pair signature of glioma ensembling IDH mutation and 1p/19q co-deletion |
title_full_unstemmed | GPGPS: a robust prognostic gene pair signature of glioma ensembling IDH mutation and 1p/19q co-deletion |
title_short | GPGPS: a robust prognostic gene pair signature of glioma ensembling IDH mutation and 1p/19q co-deletion |
title_sort | gpgps: a robust prognostic gene pair signature of glioma ensembling idh mutation and 1p/19q co-deletion |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9843586/ https://www.ncbi.nlm.nih.gov/pubmed/36637205 http://dx.doi.org/10.1093/bioinformatics/btac850 |
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