Only 2% of glioblastoma multiforme (GBM) patients respond to standard therapy and survive beyond 36 months (long-term survivors, LTS), while the majority survive less than 12 months (short-term survivors, STS). To understand the mechanism leading to poor survival, we analyzed publicly available datasets of 113 STS and 58 LTS. This analysis revealed 198 differentially expressed genes (DEGs) that characterize aggressive tumor growth and may be responsible for the poor prognosis. These genes belong largely to the Gene Ontology (GO) categories “epithelial-to-mesenchymal transition” and “response to hypoxia.” In this article, we applied an upstream analysis approach that involves state-of-the-art promoter analysis and network analysis of the dysregulated genes potentially responsible for short survival in GBM. Binding sites for transcription factors (TFs) associated with GBM pathology like NANOG, NF-κB, REST, FRA-1, PPARG, and seven others were found enriched in the promoters of the dysregulated genes. We reconstructed the gene regulatory network with several positive feedback loops controlled by five master regulators [insulin-like growth factor binding protein 2 (IGFBP2), vascular endothelial growth factor A (VEGFA), VEGF165, platelet-derived growth factor A (PDGFA), adipocyte enhancer-binding protein (AEBP1), and oncostatin M (OSMR)], which can be proposed as biomarkers and as therapeutic targets for enhancing GBM prognosis. A critical analysis of this gene regulatory network gives insights into the mechanism of gene regulation by IGFBP2 via several TFs including the key molecule of GBM tumor invasiveness and progression, FRA-1. All the observations were validated in independent cohorts, and their impact on overall survival has been investigated.
Glioblastoma multiforme (GBM) is a highly malignant brain tumor with average survival time of 15 months. Less than 2% of the patients survive beyond 36 months. To understand the molecular mechanism responsible for poor prognosis, we analyzed GBM samples of TCGA microarray (n=560) data. We have identified 720 genes that have a significant impact upon survival based on univariate cox regression. We applied the Genome Enhancer pipeline to analyze potential mechanisms of regulation of activity of these genes and to build gene regulatory networks. We identified 12 transcription factors enriched in the promoters of these genes including the key molecule of GBM – STAT3. We found that STAT3 had significant differential expression across extreme survivor groups (short-term survivors– survival 36 months) and also had a significant impact on survival. In the next step, we identified master regulators in the signal transduction network that regulate the activity of these transcription factors. Master regulators are filtered based on their differential expression across extreme survivors groups and impact on survival. This work validates our earlier report on master regulators IGFBP2, PDGFA, OSMR, and AEBP1 driving short survival. Additionally, we propose CD14, CD44, DUSP6, GRB10, IL1RAP, FGFR3, and POSTN as master regulators driving poor survival. These master regulators are proposed as promising therapeutic targets to counter poor prognosis in GBM. Finally, the algorithm has prioritized several drugs for the further study as potential remedies to conquer the aggressive forms of GBM and to extend survival of the patients.
Glioblastoma (GBM) is a very aggressive malignant brain tumor with the vast majority of patients surviving less than 12 months (Short-term survivors [STS]). Only around 2% of patients survive more than 36 months (Long-term survivors [LTS]). Studying these extreme survival groups might help in better understanding GBM biology. This work aims at exploring application of machine learning methods in predicting survival groups(STS, LTS). We used age and gene expression profiles belonging to 249 samples from publicly available datasets. 10 Machine learning methods have been implemented and compared for their performances. Hyperparameter tuned random forest model performed best with accuracy of 80% (AUC of 74% and F1_score of 85%). The performance of this model is validated on external test data of 16 samples. The model predicted the true survival group for 15 samples achieving an accuracy of 93.75%. This classification model is deployed as a web tool GlioSurvML. The top 1500 features which retained classification efficiency (Accuracy of 80%, AUC of 74%) were studied for enriched pathways and disease-causal biomarker associations using the HumanPSDTM database. We identified 199 genes as possible biomarkers of GBM and/or similar diseases (like Glioma, astrocytoma, and others). 57 of these genes are shown to be differentially expressed across survival groups and/or have impact on survival. This work demonstrates the application of machine learning methods in predicting survival groups of GBM.
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