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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:...

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Detalles Bibliográficos
Autores principales: Cheng, Lixin, Wu, Haonan, Zheng, Xubin, Zhang, Ning, Zhao, Pengfei, Wang, Ran, Wu, Qiong, Liu, Tao, Yang, Xiaojun, Geng, Qingshan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
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
Descripción
Sumario: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.