Dataset Information

Gene expression profiles of hepatic cell-type specific marker genes in progression of liver fibrosis.

ABSTRACT: AIM:To determine the gene expression profile data for the whole liver during development of dimethylni-trosamine (DMN)-induced hepatic fibrosis. METHODS:Marker genes were identified for different types of hepatic cells, including hepatic stellate cells (HSCs), Kupffer cells (including other inflammatory cells), and hepatocytes, using independent temporal DNA microarray data obtained from isolated hepatic cells. RESULTS:The cell-type analysis of gene expression gave several key results and led to formation of three hypotheses: (1) changes in the expression of HSC-specific marker genes during fibrosis were similar to gene expression data in in vitro cultured HSCs, suggesting a major role of the self-activating characteristics of HSCs in formation of fibrosis; (2) expression of mast cell-specific marker genes reached a peak during liver fibrosis, suggesting a possible role of mast cells in formation of fibrosis; and (3) abnormal expression of hepatocyte-specific marker genes was found across several metabolic pathways during fibrosis, including sulfur-containing amino acid metabolism, fatty acid metabolism, and drug metabolism, suggesting a mechanistic relationship between these abnormalities and symptoms of liver fibrosis. CONCLUSION:Analysis of marker genes for specific hepatic cell types can identify the key aspects of fibrogenesis. Sequential activation of inflammatory cells and the self-supporting properties of HSCs play an important role in development of fibrosis.

SUBMITTER: Takahara Y

PROVIDER: S-EPMC4100637 | biostudies-literature | 2006 Oct

REPOSITORIES: biostudies-literature

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Gene expression profiles of hepatic cell-type specific marker genes in progression of liver fibrosis.

Takahara Yoshiyuki Y Takahashi Mitsuo M Wagatsuma Hiroki H Yokoya Fumihiko F Zhang Qing-Wei QW Yamaguchi Mutsuyo M Aburatani Hiroyuki H Kawada Norifumi N

World journal of gastroenterology 20061001 40

<h4>Aim</h4>To determine the gene expression profile data for the whole liver during development of dimethylni-trosamine (DMN)-induced hepatic fibrosis.<h4>Methods</h4>Marker genes were identified for different types of hepatic cells, including hepatic stellate cells (HSCs), Kupffer cells (including other inflammatory cells), and hepatocytes, using independent temporal DNA microarray data obtained from isolated hepatic cells.<h4>Results</h4>The cell-type analysis of gene expression gave several ...[more]

PMID: 17072980

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Project description:Informative genes from microarray data can be used to construct prediction model and investigate biological mechanisms. Differentially expressed genes, the main targets of most gene selection methods, can be classified as single- and multiple-class specific signature genes. Here, we present a novel gene selection algorithm based on a Group Marker Index (GMI), which is intuitive, of low-computational complexity, and efficient in identification of both types of genes. Most gene selection methods identify only single-class specific signature genes and cannot identify multiple-class specific signature genes easily. Our algorithm can detect de novo certain conditions of multiple-class specificity of a gene and makes use of a novel non-parametric indicator to assess the discrimination ability between classes. Our method is effective even when the sample size is small as well as when the class sizes are significantly different. To compare the effectiveness and robustness we formulate an intuitive template-based method and use four well-known datasets. We demonstrate that our algorithm outperforms the template-based method in difficult cases with unbalanced distribution. Moreover, the multiple-class specific genes are good biomarkers and play important roles in biological pathways. Our literature survey supports that the proposed method identifies unique multiple-class specific marker genes (not reported earlier to be related to cancer) in the Central Nervous System data. It also discovers unique biomarkers indicating the intrinsic difference between subtypes of lung cancer. We also associate the pathway information with the multiple-class specific signature genes and cross-reference to published studies. We find that the identified genes participate in the pathways directly involved in cancer development in leukemia data. Our method gives a promising way to find genes that can involve in pathways of multiple diseases and hence opens up the possibility of using an existing drug on other diseases as well as designing a single drug for multiple diseases.

Dataset Information

Gene expression profiles of hepatic cell-type specific marker genes in progression of liver fibrosis.

Publications

Gene expression profiles of hepatic cell-type specific marker genes in progression of liver fibrosis.

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