The identification of microRNAs to predict glioma prognosis

Until now, personalised medicine for patients in oncology has been focused on the use of DNA-based techniques such as mutation detection and fluorescence in situ hybridisation, fluorescence-activated cell sorting and immuno-staining for classifying tumours. MicroRNAs are short non-coding RNAs that are involved in post-translational regulation of gene expression. Their expression levels are often altered in cancer. Due to their functional importance and stability in biological samples, they represent another tool that could be used to aid patient management.
Glioblastoma is a disease that has had little improvement in survival over the past decade in comparison to other cancers. A number of new drugs have been explored but even successful trials have shown limited success.
This thesis is focused on identification of microRNAs as signatures for prognosis prediction in glioblastoma. It is separated into four parts; the identification of a microRNA signature that can be used to predict prognosis in glioblastoma; the alignment of glioblastoma microRNA expression with the microRNA expression of oligodendrocyte precursors and its involvement in patient outcome; the use of the expression pattern of the most abundant and robust prognostic microRNA in glioma (miR-9) to delineate glioblastoma subtype and finally the identification of a microRNA signature to predict prognosis in patients treated with the anti-angiogenic drug bevacizumab. The research aims to create signatures suitable for clinical practice, with a small number of predictors, and where possible the function of the microRNAs has been predicted and reviewed to provide confirmation of their role in glioma biology.
The key findings of this research are the formation of robust signatures using microRNAs in a disease where few markers are available and proof of a technique that can be used in future drug studies to improve performance at clinical trials.