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Nuclear factor erythroid-2-related factor 2 (NRF2), a transcriptional gene factor related to nuclear factor erythroid 2, plays a role in the development of gliomas with isocitrate dehydrogenase (IDH) mutation. Its impact on tumour recurrence has seldom been investigated. A group of 34 patients diagnosed with Grade 4 astrocytoma was included in a retrospective cohort. NRF2 protein and gene expressions were assessed using different profiling assays. The association between IDH mutation, NRF2 expression, and tumor recurrence was investigated. The mean patients' age in this study was 50 years. Out of the total number of tumors analyzed, 21 of them had IDH mutation. NRF2 protein expression was found to be overexpressed in 27 tumors and reduced in 7 tumors. Additionally, NRF2 gene was upregulated in 24 tumors and downregulated in 10 tumors. Insignificant statistical difference was observed in recurrence-free interval (RFI) between patients with upregulated or downregulated NRF2 gene or protein expressions (p-value>0.05). However, this relationship is distinctive when NRF2 expression was compared to IDH mutation. Tumors with different levels of NRF2 expressions and IDH mutations showed significant statistical variation in RFI (p-value=0.001). There was insignificant impact on RFI among patients receiving different chemotherapies (TMZ or TMZ plus) who had abnormal NRF2 gene activities (p-value=0.97). WHO-Grade 4 astrocytoma with IDH mutation and altered NRF2 expression showed a delayed tumor recurrence compared to IDH-wildtype glioblastoma. [ABSTRACT FROM AUTHOR]

Identifying and distinguishing cancer driver genes among thousands of candidate mutations remains a major challenge. Accurate identification of driver genes and driver mutations is critical for advancing cancer research and personalizing treatment based on accurate stratification of patients. Due to inter-tumor genetic heterogeneity many driver mutations within a gene occur at low frequencies, which make it challenging to distinguish them from non-driver mutations. We have developed a novel method for identifying cancer driver genes. Our approach utilizes multiple complementary types of information, specifically cellular phenotypes, cellular locations, functions, and whole body physiological phenotypes as features. We demonstrate that our method can accurately identify known cancer driver genes and distinguish between their role in different types of cancer. In addition to confirming known driver genes, we identify several novel candidate driver genes. We demonstrate the utility of our method by validating its predictions in nasopharyngeal cancer and colorectal cancer using whole exome and whole genome sequencing. [ABSTRACT FROM AUTHOR]