Identifying novel glioma associated pathways based on integrated ‘omics’ data

Microarray represents a high throughput technology for analyzing expression profiles, and thus it has been widely applied in the study of pathogenesis of glioma. However, most of the analyses focused on detecting the differentially expressed genes in glioma. Although it is well accepted that the pathway-derived signatures is more reproducible than that at gene level, few meta-analyses of multiple microarray datasets at system level have been previously performed. In this article, we performed meta-analysis on different published glioma expression profiles and compared the overlapping of signature at gene and pathway level. Pathway enrichment analysis result of GeneGO database and Gene Set Enrichment Analysis (GSEA) showed that 100% and 64% of the similarity was higher than that of genes respectively. Moreover, we integrated other omics data on glioma, such as MicroRNA expression profiles and Chip-Seq data, for further verification. The results showed that the significant signatures of different data sets are more similar at pathway level than at gene level. 12 pathways found by GeneGO database were shared by four stages among several datasets. 5 of these pathways, such as Regulation of epithelial-to-mesenchymal transition (EMT), TGF-beta-dependent induction of EMT via SMADs, were putative novel pathways on glioma and need further experimental verification.