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Screening feature genes of astrocytoma using a combined method of microarray gene expression profiling and bioinformatics analysis.

ABSTRACT: The aim of our study was to find feature genes associated with astrocytoma and correlative gene functions which can distinguish cancer tissue from adjacent non-tumor astrocyte tissues. Gene expression profile GSE15824 was downloaded from Gene Expression Omnibus database which included 8 astrocytoma tissues and 3 adjacent non-tumor astrocyte samples. The raw data were first transformed into probe-level data and the differentially expressed genes (DEGs) between tissues of patients with astrocytoma and normal specimen were identified using T-test in samr package of R. The Database for Annotation, Visualization and Integrated Discovery (DAVID) was applied to analyze the gene ontology (GO) enrichment on gene functions and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Finally, corresponding protein-protein interaction (PPI) networks of DEGs was constructed using the Cytoscape based on the data collected from STRING online datasets. A total of 3072 genes, including 1799 up-regulated genes and 1273 down-regulated genes, were filtered as DEGs, and we learnt that the DEGs including AQP4, PMP2, SRARCL1 and SLC1A2CAMs etc and that AQP4 was most significantly related to cell osmotic pressure. Three feature genes in KEGG pathway are highly enriched in cancer specimen while two genes are in the normal tissues. The discovery of featured genes significantly related to the regulation of cell osmotic pressure, has the potential to use in clinic for diagnosis of astrocytoma in future. In addition, it has a great significance on studying mechanism, distinguishing normal and cancer tissues, and exploring new treatments for astrocytoma. However, further experiments were needed to confirm our result.

SUBMITTER: Cai Y

PROVIDER: S-EPMC4694295 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Screening feature genes of astrocytoma using a combined method of microarray gene expression profiling and bioinformatics analysis.

Cai Yong Y Zhong Xingming X Wang Yiqi Y Yang Jianguo J

International journal of clinical and experimental medicine 20151015 10

The aim of our study was to find feature genes associated with astrocytoma and correlative gene functions which can distinguish cancer tissue from adjacent non-tumor astrocyte tissues. Gene expression profile GSE15824 was downloaded from Gene Expression Omnibus database which included 8 astrocytoma tissues and 3 adjacent non-tumor astrocyte samples. The raw data were first transformed into probe-level data and the differentially expressed genes (DEGs) between tissues of patients with astrocytoma ...[more]

PMID: 26770395

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Project description:BackgroundThe completion of the sequencing of human, mouse and rat genomes and knowledge of cross-species gene homologies enables studies of differential gene expression in animal models. These types of studies have the potential to greatly enhance our understanding of diseases such as liver cancer in humans. Genes co-expressed across multiple species are most likely to have conserved functions. We have used various bioinformatics approaches to examine microarray expression profiles from liver neoplasms that arise in albumin-SV40 transgenic rats to elucidate genes, chromosome aberrations and pathways that might be associated with human liver cancer.ResultsIn this study, we first identified 2223 differentially expressed genes by comparing gene expression profiles for two control, two adenoma and two carcinoma samples using an F-test. These genes were subsequently mapped to the rat chromosomes using a novel visualization tool, the Chromosome Plot. Using the same plot, we further mapped the significant genes to orthologous chromosomal locations in human and mouse. Many genes expressed in rat 1q that are amplified in rat liver cancer map to the human chromosomes 10, 11 and 19 and to the mouse chromosomes 7, 17 and 19, which have been implicated in studies of human and mouse liver cancer. Using Comparative Genomics Microarray Analysis (CGMA), we identified regions of potential aberrations in human. Lastly, a pathway analysis was conducted to predict altered human pathways based on statistical analysis and extrapolation from the rat data. All of the identified pathways have been known to be important in the etiology of human liver cancer, including cell cycle control, cell growth and differentiation, apoptosis, transcriptional regulation, and protein metabolism.ConclusionThe study demonstrates that the hepatic gene expression profiles from the albumin-SV40 transgenic rat model revealed genes, pathways and chromosome alterations consistent with experimental and clinical research in human liver cancer. The bioinformatics tools presented in this paper are essential for cross species extrapolation and mapping of microarray data, its analysis and interpretation.

Dataset Information

Screening feature genes of astrocytoma using a combined method of microarray gene expression profiling and bioinformatics analysis.

Publications

Screening feature genes of astrocytoma using a combined method of microarray gene expression profiling and bioinformatics analysis.

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