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• W2087345890 endingPage "S9" @default.
• W2087345890 startingPage "S9" @default.
• W2087345890 abstract "With recent advances in microarray technology, including genomics, proteomics, and metabolomics, it brings a great challenge for integrating this -omics data to analysis complex disease. Glioma is an extremely aggressive and lethal form of brain tumor, and thus the study of the molecule mechanism underlying glioma remains very important. To date, most studies focus on detecting the differentially expressed genes in glioma. However, the meta-analysis for pathway analysis based on multiple microarray datasets has not been systematically pursued. In this study, we therefore developed a systems biology based approach by integrating three types of omics data to identify common pathways in glioma. Firstly, the meta-analysis has been performed to study the overlapping of signatures at different levels based on the microarray gene expression data of glioma. Among these gene expression datasets, 12 pathways were found in GeneGO database that shared by four stages. Then, microRNA expression profiles and ChIP-seq data were integrated for the further pathway enrichment analysis. As a result, we suggest 5 of these pathways could be served as putative pathways in glioma. Among them, the pathway of TGF-beta-dependent induction of EMT via SMAD is of particular importance. Our results demonstrate that the meta-analysis based on systems biology level provide a more useful approach to study the molecule mechanism of complex disease. The integration of different types of omics data, including gene expression microarrays, microRNA and ChIP-seq data, suggest some common pathways correlated with glioma. These findings will offer useful potential candidates for targeted therapeutic intervention of glioma." @default.
• W2087345890 created "2016-06-24" @default.
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• W2087345890 date "2013-01-01" @default.
• W2087345890 modified "2023-09-26" @default.
• W2087345890 title "Identifying novel glioma associated pathways based on systems biology level meta-analysis" @default.
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• W2087345890 doi "https://doi.org/10.1186/1752-0509-7-s2-s9" @default.
• W2087345890 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3866263" @default.
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• W2087345890 hasPublicationYear "2013" @default.
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