Analysis of cancer stem cell markers as prognostic biomarkers for glioblastomaHalilčević, Selma (Avtor) Sadikov, Aleksander (Mentor) Breznik, Barbara (Komentor) glioblastomabiomarkerscancer stem cellscorrelationsclassificationsurvival analysisBrain tumour glioblastoma (GBM) is one of the most aggressive, invasive, and unfortunately lethal tumours. The unfavourable prognosis of GBM has encouraged continued efforts to better understand its pathobiology to find more clinically relevant biomarkers and novel efficient therapeutic approaches. Treatment of GBM remains one of the hardest challenges in cancer therapy, firstly due to the resistance to therapy of glioblastoma stem cells and secondly due to the tumour heterogeneity, leading to variable treatment responses. Computer scientists join their forces with biological scientists and clinicians to bring GBM research to a next level. In this master thesis, we perform an analysis of glioblastoma stem cells and other GBM-related biomarker gene expressions. We analyse correlations of gene expression of several selected markers with clinical data, and their significance for survival of GBM patients. We find several biomarkers that can be used as prognostic biomarkers. With the use of machine learning methods, we define a new approach to determine GBM subtypes. We prove the existence of a new MIX subtype, which contains all GBM subtypes (classical, mesenchymal and proneural) due to the high GBM heterogeneity.20232023-12-06 09:40:01Magistrsko delo/naloga152753VisID: 37012COBISS_ID: 177581315sl