Integrative Analysis of Microarray Data to Reveal Regulation Patterns in the Pathogenesis of Hepatocellular Carcinoma
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Background/Aims: 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. Methods: 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. Results: 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. Conclusions: The transcriptional regulatory network can provide additional information that is valuable in understanding the underlying molecular mechanism in hepatic tumorigenesis.
Abstract
Background/Aims: 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. Methods: 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. Results: 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. Conclusions: The transcriptional regulatory network can provide additional information that is valuable in understanding the underlying molecular mechanism in hepatic tumorigenesis.
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