ABSTRACT
Glioblastoma multiforme (GBM) is an aggressive form of brain tumours that remains incurable despite recent advances in clinical treatments. Previous studies have focused on sub-categorizing patient samples based on clustering various transcriptomic data. While functional genomics data are rapidly accumulating, there exist opportunities to leverage these data to decipher glioma-associated biomarkers. We sought to implement a systematic approach to integrating data from high throughput CRISPR-Cas9 screening studies with machine learning algorithms to infer a glioma functional network. We demonstrated the network significantly enriched various biological pathways and may play roles in glioma tumorigenesis. From densely connected glioma functional modules, we further predicted 12 potential Wnt/ß-catenin signalling pathway targeted genes, including AARSD1, HOXB5, ITGA6, LRRC71, MED19, MED24, METTL11B, SMARCB1, SMARCE1, TAF6L, TENT5A and ZNF281. Cox regression modelling with these targets was significantly associated with glioma overall survival prognosis. Additionally, TRIB2 was identified as a glioma neoplastic cell marker in single-cell RNA-seq of GBM samples. This work establishes novel strategies for constructing functional networks to identify glioma biomarkers for the development of diagnosis and treatment in clinical practice.
Subject(s)
Brain Neoplasms , Glioblastoma , Glioma , Brain Neoplasms/pathology , Calcium-Calmodulin-Dependent Protein Kinases/genetics , Calcium-Calmodulin-Dependent Protein Kinases/metabolism , Chromosomal Proteins, Non-Histone/genetics , DNA-Binding Proteins/genetics , Gene Expression Regulation, Neoplastic , Glioblastoma/pathology , Glioma/genetics , Humans , Machine Learning , Mediator Complex/genetics , Repressor Proteins/geneticsABSTRACT
Gliomas are the most common lethal brain tumours and remain great heterogeneity in terms of histopathology and clinical outcomes. Among them, glioblastomas are the most aggressive tumours that lead to a median of less than one-year survival in patients. Despite the little improvement of in diagnosis and treatments for last decades, there is an urgent need for prognostic markers to distinguish high- and low-risk patients before treatment.Here, we generated a list of genes associated with glioblastoma progressions and then performed a comprehensive statistical modelling strategy to derive a 10-gene (GLO10) score from genome wide expression profiles of a large glioblastoma cohort (n=844). Our study demonstrated that the GLO10 score could successfully distinguish high- and low-risk patients with glioblastomas regardless their traditional pathological factors. Validated in four independent cohorts, the utility of GLO10 score could provide clinicians a robust prognostic prediction tool to assess risk levels upfront treatments.
ABSTRACT
Current staging methods are inadequate for predicting the overall survival of meningioma. DNA microarray technologies improve the understanding of tumour progression. We analysed genome wide expression profiles of 119 meningioma samples from two previous published DNA microarray studies. The Cox proportional hazards regression models were applied to identify overall survival related gene signature. A total of 449 genes (109 upregulated and 340 downregulated) were identified as differentially expressed in meningioma. Among these differentially expressed genes, 37 genes were identified to be related to meningioma overall survival. Our 37-gene signature is closely associated with overall survival among patients with meningioma. This gene expression profile could provide an optimization of the clinical management and development of new therapeutic strategies for meningioma.
