Background: Glutathione peroxidase-1 (GPX1) is a member of the GPX family, which considered an enzyme that interacts with oxidative stress. GPX1 differential expression is closely correlated with carcinogenesis and disease progression. In this study, we used bioinformatics analysis to investigate GPX1 expression level and explore the prognostic information in different human cancers.
Methods: Expression was analyzed via the Oncomine database and Gene Expression Profiling InteractiveAnalysis tool, and potential prognostic analysis was evaluated using the UALCAN, GEPIA, and DriverDBv3 databases. Then, the UALCAN database was used to find the promoter methylation of GPX1 in defied cancer types. While GPX1 related functional networks were found within the GeneMANIA interactive tool and Cytoscape software. Moreover, Metascape online website was used to analyze Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways.Results: We found that GPX1 was commonly overexpressed in most human cancers. High expression of GPX1 could lead to poor outcomes in Brain Lower Grade Glioma, while GPX1 over expression was correlated with better prognosis in Kidney renal papillary cell carcinoma (KIPP). High GPX1 expression was marginally associated with poor prognosis in acute myeloid leukemia (AML). Gene regulation network suggested that GPX1 mainly involved in pathways including the glutathione metabolism, ferroptosis, TP53 regulates metabolic genes, reactive oxygen species (ROS) metabolic process, and several other signaling pathways.Conclusions: Our findings revealed that GPX1 showed significant expression differences among cancers and served as a prognostic biomarker for defined cancer types. The data mining effectively revealed useful information about GPX1 expression, prognostic values, and potential functional networks in cancers, thus providing researchers with an available way to further explore the mechanism underlying carcinogenesis of genes of interest in different cancers.
E2F transcription factor 3 (E2F3), a member of the E2F transcription factor family and a member of the genes involved in the regulation of cell cycle, is an oncogene with strong proliferative potential. E2F3 is involved in many processes and plays important roles in the development of several types of cancer, while its relationship with prognosis in hepatocellular carcinoma (HCC) has yet to be reported. In the present study, based on 4 independent microarray data sets which covered 385 cases of HCC and 327 cases of normal livers retrieved from the Oncomine database, we demonstrated that E2F3 was upregulated at least 1.5-fold and on average 2.3-fold in HCC when compared with normal controls. Comprehensive bioinformatics analysis consisting of protein-protein interaction, gene co-occurrence, microRNA-mRNA interaction and biological process annotation indicated that E2F3 interacted with a large number of genes, proteins and microRNAs which were all associated with poor prognosis in patients with HCC and other types of cancer, suggesting that E2F3 may also serve as a biomarker for poor prognosis. Taken together, for the first time, we show that the overexpression of E2F3 may be associated with unfavorable prognosis in HCC.
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