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Meta-analysis of differentially expressed genes in osteosarcoma based on gene expression data.

ABSTRACT: BACKGROUND: To uncover the genes involved in the development of osteosarcoma (OS), we performed a meta-analysis of OS microarray data to identify differentially expressed genes (DEGs) and biological functions associated with gene expression changes between OS and normal control (NC) tissues. METHODS: We used publicly available GEO datasets of OS to perform a meta-analysis. We performed Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Protein-Protein interaction (PPI) networks analysis. RESULTS: Eight GEO datasets, including 240 samples of OS and 35 samples of controls, were available for the meta-analysis. We identified 979 DEGs across the studies between OS and NC tissues (472 up-regulated and 507 down-regulated). We found GO terms for molecular functions significantly enriched in protein binding (GO: 0005515, P = 3.83E-60) and calcium ion binding (GO: 0005509, P?=?3.79E-13), while for biological processes, the enriched GO terms were cell adhesion (GO:0007155, P?=?2.26E-19) and negative regulation of apoptotic process (GO: 0043066, P?=?3.24E-15), and for cellular component, the enriched GO terms were cytoplasm (GO: 0005737, P?=?9.18E-63) and extracellular region (GO: 0005576, P?=?2.28E-47). The most significant pathway in our KEGG analysis was Focal adhesion (P?=?5.70E-15). Furthermore, ECM-receptor interaction (P?=?1.27E-13) and Cell cycle (P?=?4.53E-11) are found to be highly enriched. PPI network analysis indicated that the significant hub proteins containing PTBP2 (Degree?=?33), RGS4 (Degree?=?15) and FXYD6 (Degree?=?13). CONCLUSIONS: Our meta-analysis detected DEGs and biological functions associated with gene expression changes between OS and NC tissues, guiding further identification and treatment for OS.

SUBMITTER: Yang Z

PROVIDER: S-EPMC4109777 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Meta-analysis of differentially expressed genes in osteosarcoma based on gene expression data.

Yang Zuozhang Z Chen Yongbin Y Fu Yu Y Yang Yihao Y Zhang Ya Y Chen Yanjin Y Li Dongqi D

BMC medical genetics 20140714

<h4>Background</h4>To uncover the genes involved in the development of osteosarcoma (OS), we performed a meta-analysis of OS microarray data to identify differentially expressed genes (DEGs) and biological functions associated with gene expression changes between OS and normal control (NC) tissues.<h4>Methods</h4>We used publicly available GEO datasets of OS to perform a meta-analysis. We performed Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway ana ...[more]

PMID: 25023069

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Project description:BackgroundTo identify differentially expressed genes (DEGs) from microarray data, users of the Affymetrix GeneChip system need to select both a preprocessing algorithm to obtain expression-level measurements and a way of ranking genes to obtain the most plausible candidates. We recently recommended suitable combinations of a preprocessing algorithm and gene ranking method that can be used to identify DEGs with a higher level of sensitivity and specificity. However, in addition to these recommendations, researchers also want to know which combinations enhance reproducibility.ResultsWe compared eight conventional methods for ranking genes: weighted average difference (WAD), average difference (AD), fold change (FC), rank products (RP), moderated t statistic (modT), significance analysis of microarrays (samT), shrinkage t statistic (shrinkT), and intensity-based moderated t statistic (ibmT) with six preprocessing algorithms (PLIER, VSN, FARMS, multi-mgMOS (mmgMOS), MBEI, and GCRMA). A total of 36 real experimental datasets was evaluated on the basis of the area under the receiver operating characteristic curve (AUC) as a measure for both sensitivity and specificity. We found that the RP method performed well for VSN-, FARMS-, MBEI-, and GCRMA-preprocessed data, and the WAD method performed well for mmgMOS-preprocessed data. Our analysis of the MicroArray Quality Control (MAQC) project's datasets showed that the FC-based gene ranking methods (WAD, AD, FC, and RP) had a higher level of reproducibility: The percentages of overlapping genes (POGs) across different sites for the FC-based methods were higher overall than those for the t-statistic-based methods (modT, samT, shrinkT, and ibmT). In particular, POG values for WAD were the highest overall among the FC-based methods irrespective of the choice of preprocessing algorithm.ConclusionOur results demonstrate that to increase sensitivity, specificity, and reproducibility in microarray analyses, we need to select suitable combinations of preprocessing algorithms and gene ranking methods. We recommend the use of FC-based methods, in particular RP or WAD.

Dataset Information

Meta-analysis of differentially expressed genes in osteosarcoma based on gene expression data.

Publications

Meta-analysis of differentially expressed genes in osteosarcoma based on gene expression data.

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