Dataset Information

Integrative Analysis of Microarray Data to Reveal Regulation Patterns in the Pathogenesis of Hepatocellular Carcinoma.

ABSTRACT: The integration of multiple profiling data and the construction of a transcriptional regulatory network may provide additional insights into the molecular mechanisms of hepatocellular carcinoma (HCC). The present study was conducted to investigate the deregulation of genes and the transcriptional regulatory network in HCC.An integrated analysis of HCC gene expression datasets was performed in Gene Expression Omnibus. Functional annotation of the differentially expression genes (DEGs) was conducted. Furthermore, transcription factors (TFs) were identified, and a global transcriptional regulatory network was constructed.An integrated analysis of eight eligible gene expression profiles of HCC led to 1,835 DEGs. Consistent with the fact that the cell cycle is closely related to various tumors, the functional annotation revealed that genes involved in the cell cycle were significantly enriched. A transcriptional regulatory network was constructed using the 62 TFs, which consisted of 872 TF-target interactions between 56 TFs and 672 DEGs in the context of HCC. The top 10 TFs covering the most downstream DEGs were ZNF354C, NFATC2, ARID3A, BRCA1, ZNF263, FOXD1, GATA3, FOXO3, FOXL1, and NR4A2. This network will appeal to future investigators focusing on the development of HCC.The transcriptional regulatory network can provide additional information that is valuable in understanding the underlying molecular mechanism in hepatic tumorigenesis.

SUBMITTER: Chen J

PROVIDER: S-EPMC5221868 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Integrative Analysis of Microarray Data to Reveal Regulation Patterns in the Pathogenesis of Hepatocellular Carcinoma.

Chen Juan J Qian Zhenwen Z Li Fengling F Li Jinzhi J Lu Yi Y

Gut and liver 20170101 1

<h4>Background/aims</h4>The integration of multiple profiling data and the construction of a transcriptional regulatory network may provide additional insights into the molecular mechanisms of hepatocellular carcinoma (HCC). The present study was conducted to investigate the deregulation of genes and the transcriptional regulatory network in HCC.<h4>Methods</h4>An integrated analysis of HCC gene expression datasets was performed in Gene Expression Omnibus. Functional annotation of the differenti ...[more]

PMID: 27458175

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Project description:BackgroundThe broader use of high-throughput technologies has led to improved molecular characterization of hepatocellular carcinoma (HCC).AimTo comprehensively analyze and characterize all publicly available genomic, gene expression, methylation, miRNA and proteomic data in HCC, covering 85 studies and 3355 patient sample profiles, to identify the key dysregulated genes and pathways they affect.MethodsWe collected and curated all well-annotated and publicly available high-throughput datasets from PubMed and Gene Expression Omnibus derived from human HCC tissue. Comprehensive pathway enrichment analysis was performed using pathDIP for each data type (genomic, gene expression, methylation, miRNA and proteomic), and the overlap of pathways was assessed to elucidate pathway dependencies in HCC.ResultsWe identified a total of 8733 abstracts retrieved by the search on PubMed on HCC for the different layers of data on human HCC samples, published until December 2016. The common key dysregulated pathways in HCC tissue across different layers of data included epidermal growth factor (EGFR) and β1-integrin pathways. Genes along these pathways were significantly and consistently dysregulated across the different types of high-throughput data and had prognostic value with respect to overall survival. Using CTD database, estradiol would best modulate and revert these genes appropriately.ConclusionBy analyzing and integrating all available high-throughput genomic, transcriptomic, miRNA, methylation and proteomic data from human HCC tissue, we identified EGFR, β1-integrin and axon guidance as pathway dependencies in HCC. These are master regulators of key pathways in HCC, such as the mTOR, Ras/Raf/MAPK and p53 pathways. The genes implicated in these pathways had prognostic value in HCC, with Netrin and Slit3 being novel proteins of prognostic importance to HCC. Based on this integrative analysis, EGFR, and β1-integrin are master regulators that could serve as potential therapeutic targets in HCC.

Dataset Information

Integrative Analysis of Microarray Data to Reveal Regulation Patterns in the Pathogenesis of Hepatocellular Carcinoma.

Publications

Integrative Analysis of Microarray Data to Reveal Regulation Patterns in the Pathogenesis of Hepatocellular Carcinoma.

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