A prognostic signature of five pseudogenes for predicting lower-grade gliomas

[Display omitted] •Five pseudogenes were identified as prognostic markers for patients with lower-grade gliomas.•A risk score model based on the five pseudogenes provides excellent prediction power for lower-grade gliomas.•The identified novel pseudogenes are associated with several tumor-related bi...

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Bibliographic Details
Published in:Biomedicine & pharmacotherapy 2019-09, Vol.117, p.109116-109116, Article 109116
Main Authors: Liu, Bo, Liu, Jingping, Liu, Kun, Huang, Hao, Li, Yexin, Hu, Xiqi, Wang, Ke, Cao, Hui, Cheng, Quan
Format: Article
Language:English
Subjects:
Adult
Brain Neoplasms - genetics
Brain Neoplasms - pathology
Databases, Genetic
Female
Gene Ontology
Glioma - genetics
Glioma - pathology
Humans
Lower-grade glioma
Male
Multivariate Analysis
Neoplasm Grading
Prognosis
Proportional Hazards Models
Pseudogene
Pseudogenes
Regression Analysis
Reproducibility of Results
Risk Factors
Risk score
Survival
Survival Analysis
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Summary:[Display omitted] •Five pseudogenes were identified as prognostic markers for patients with lower-grade gliomas.•A risk score model based on the five pseudogenes provides excellent prediction power for lower-grade gliomas.•The identified novel pseudogenes are associated with several tumor-related biological processes. A pseudogene is a gene copy that has lost its original coding ability. Pseudogenes participate in numerous biological processes including oncogenesis. We screened for prognostic pseudogenes for lower-grade glioma (LGG) and explored the potential molecular mechanisms. LGG data downloaded from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases were used as training and validation dataset, respectively. Univariate Cox proportional hazard regression was performed to identify pseudogenes with significant prognostic value. Robust likelihood-based survival model and LASSO regression were performed to screen for the most survival-relevant pseudogenes. A risk score model was constructed based on the prognostic pseudogenes to predict the prognosis of LGG patients. Five pseudogenes (PKMP3, AC027612.4, HILS1, RP5-1132H15.3 and HSPB1P1) were identified as prognostic gene-signatures. Using the risk score model established based on the five pseudogenes, LGG patients were stratified into distinct prognosis groups in both TCGA and CGGA datasets (P 
ISSN:0753-3322
1950-6007
DOI:10.1016/j.biopha.2019.109116