Research Article

Screening for feature genes associated with hereditary hemochromatosis and functional analysis with DNA microarrays

Published: December 04, 2013
Genet. Mol. Res. 12 (4) : 6240-6248 DOI: 10.4238/2013.December.4.11

Abstract

The aim of this study was to identify feature genes that are associated with hereditary hemochromatosis (HHC; iron overload) in cardiac and skeletal muscle of mice. First, the expression profile GSE9726 was downloaded from Gene Expression Omnibus database which included 12 samples. Then the differentially expressed genes (DEGs) were identified by R language. Furthermore, the KUPS software was used to identify relationships in interactions among common DEGs in the cardiac and skeletal muscles. We then used the EASE software to obtain enriched pathways in a gene interaction network. Finally, we used the plugins of the Cytoscape software, i.e., Mcode and Bingo, to conduct module analysis. By comparing diseased and normal tissue samples, 5 and 6 genes in the cardiac and skeletal muscles, respectively, were identified as DEGs. We observed that the S100a8 and S100a9 genes were common DEGs in both tissues examined. In addition, we constructed an interaction network with common DEGs and their interacting components, and identified S100a8 and S100a9 as being associated with immune responses. In view of the relationship between the early stage of myelodysplastic syndrome and the immune system, we hypothesize that the expression of the S100a8 and S100a9 genes is a feature that can be used for diagnosis during the early stage of the myelodysplastic syndrome and that the 2 genes could be used as targets in treating this disease

The aim of this study was to identify feature genes that are associated with hereditary hemochromatosis (HHC; iron overload) in cardiac and skeletal muscle of mice. First, the expression profile GSE9726 was downloaded from Gene Expression Omnibus database which included 12 samples. Then the differentially expressed genes (DEGs) were identified by R language. Furthermore, the KUPS software was used to identify relationships in interactions among common DEGs in the cardiac and skeletal muscles. We then used the EASE software to obtain enriched pathways in a gene interaction network. Finally, we used the plugins of the Cytoscape software, i.e., Mcode and Bingo, to conduct module analysis. By comparing diseased and normal tissue samples, 5 and 6 genes in the cardiac and skeletal muscles, respectively, were identified as DEGs. We observed that the S100a8 and S100a9 genes were common DEGs in both tissues examined. In addition, we constructed an interaction network with common DEGs and their interacting components, and identified S100a8 and S100a9 as being associated with immune responses. In view of the relationship between the early stage of myelodysplastic syndrome and the immune system, we hypothesize that the expression of the S100a8 and S100a9 genes is a feature that can be used for diagnosis during the early stage of the myelodysplastic syndrome and that the 2 genes could be used as targets in treating this disease

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