Large-Scale Analysis Reveals Gene Signature for Survival Prediction in Primary Glioblastoma

Birbal Prasad, Yongji Tian, Xinzhong Li

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Glioblastoma multiforme (GBM) is the most aggressive and common primary central nervous system tumour. Despite extensive therapy, GBM patients usually have poor prognosis with a median survival of 12–15 months. Novel molecular biomarkers that can improve survival prediction and help with treatment strategies are still urgently required. Here we aimed to robustly identify a gene signature panel for improved survival prediction in primary GBM patients. We identified 2166 differentially expressed genes (DEGs) using meta-analysis of microarray datasets comprising of 955 samples (biggest primary GBM cohort for such studies as per our knowledge) and 3368 DEGs from RNA-seq dataset with 165 samples. Based on the 1443 common DEGs, using univariate Cox and least absolute shrinkage and selection operator (LASSO) with multivariate Cox regression, we identified a survival associated 4-gene signature panel including IGFBP2, PTPRN, STEAP2 and SLC39A10 and thereafter established a risk score model that performed well in survival prediction. High-risk group patients had significantly poorer survival as compared with those in the low-risk group (AUC = 0.766 for 1-year prediction). Multivariate analysis demonstrated that predictive value of the 4-gene signature panel was independent of other clinical and pathological features and hence is a potential prognostic biomarker. More importantly, we validated this signature in three independent GBM cohorts to test its generality. In conclusion, our integrated analysis using meta-analysis approach maximizes the use of the available gene expression data and robustly identified a 4-gene panel for predicting survival in primary GBM.

Original languageEnglish
Pages (from-to)5235–5246
Number of pages12
JournalMolecular Neurobiology
Issue number12
Publication statusPublished - 1 Sep 2020

Bibliographical note

Funding Information:
This research was supported by AiPBAND ( ), European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement 764281. BP is a Marie-Curie early stage research fellow of AiPBAND.

Publisher Copyright:
© 2020, The Author(s).

Copyright 2020 Elsevier B.V., All rights reserved.


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