Dataset Information

Integrative Analysis of HNF1B mRNA in Human Cancers Based on Data Mining.

ABSTRACT: Cancer incidence is rapidly growing, and cancer is the leading cause of death worldwide in the 21st century. Hepatocyte nuclear factor 1B (HNF1B) is a transcription factor that involves the growth and development of multiple organs. The aim of this study was to explore the significance of HNF1B in human cancer by an integrative analysis of online databases. The UALCAN database, cBio cancer genomics portal, Cancer Regulome tools, Kaplan-Meier plotter and Tumor IMmune Estimation Resource (TIMER) website were used to perform the corresponding analysis. The results showed that HNF1B is dysregulated in various cancers and associated with the differential overall survival of cancer patients. HNF1B showed many mutation forms and high mutation levels in different cancer types. In addition, we found that HNF1B interacted with different genes in multiple aspects. Moreover, HNF1B expression is associated with many immune cell infiltration levels and influences the prognostic prediction of immune cells in some kinds of cancers. In conclusion, HNF1B plays a significant role in cancer and may be a potential target for cancer immunotherapy.

SUBMITTER: Nie C

PROVIDER: S-EPMC7646120 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Integrative Analysis of HNF1B mRNA in Human Cancers Based on Data Mining.

Nie Chunhui C Wang Bei B Wang Baoquan B Lv Ning N Zhang Enfan E

International journal of medical sciences 20201018 18

Cancer incidence is rapidly growing, and cancer is the leading cause of death worldwide in the 21st century. Hepatocyte nuclear factor 1B (HNF1B) is a transcription factor that involves the growth and development of multiple organs. The aim of this study was to explore the significance of HNF1B in human cancer by an integrative analysis of online databases. The UALCAN database, cBio cancer genomics portal, Cancer Regulome tools, Kaplan-Meier plotter and Tumor IMmune Estimation Resource (TIMER) w ...[more]

PMID: 33173410

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Project description: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 Interactive Analysis 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.

Project description:BACKGROUND: Technological improvements have shifted the focus from data generation to data analysis. The availability of large amounts of data from transcriptomics, protemics and metabolomics experiments raise new questions concerning suitable integrative analysis methods. We compare three integrative analysis techniques (co-inertia analysis, generalized singular value decomposition and integrative biclustering) by applying them to gene and protein abundance data from the six life cycle stages of Plasmodium falciparum. Co-inertia analysis is an analysis method used to visualize and explore gene and protein data. The generalized singular value decomposition has shown its potential in the analysis of two transcriptome data sets. Integrative Biclustering applies biclustering to gene and protein data. RESULTS: Using CIA, we visualize the six life cycle stages of Plasmodium falciparum, as well as GO terms in a 2D plane and interpret the spatial configuration. With GSVD, we decompose the transcriptomic and proteomic data sets into matrices with biologically meaningful interpretations and explore the processes captured by the data sets. IBC identifies groups of genes, proteins, GO Terms and life cycle stages of Plasmodium falciparum. We show method-specific results as well as a network view of the life cycle stages based on the results common to all three methods. Additionally, by combining the results of the three methods, we create a three-fold validated network of life cycle stage specific GO terms: Sporozoites are associated with transcription and transport; merozoites with entry into host cell as well as biosynthetic and metabolic processes; rings with oxidation-reduction processes; trophozoites with glycolysis and energy production; schizonts with antigenic variation and immune response; gametocyctes with DNA packaging and mitochondrial transport. Furthermore, the network connectivity underlines the separation of the intraerythrocytic cycle from the gametocyte and sporozoite stages. CONCLUSION: Using integrative analysis techniques, we can integrate knowledge from different levels and obtain a wider view of the system under study. The overlap between method-specific and common results is considerable, even if the basic mathematical assumptions are very different. The three-fold validated network of life cycle stage characteristics of Plasmodium falciparum could identify a large amount of the known associations from literature in only one study.

Dataset Information

Integrative Analysis of HNF1B mRNA in Human Cancers Based on Data Mining.

Publications

Integrative Analysis of HNF1B mRNA in Human Cancers Based on Data Mining.

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