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Background: Glioblastoma multiforme (GBM) is known as one of the most fatal forms of cancer. MicroRNAs have been widely implicated in the regulation of mammalian development and pathogenesis. The brain-enriched miR-29 subfamilies are known to be exclusively expressed in the developing brain, and they are aberrantly down-regulated in GBM. This study aims to elucidate the role of miR-29b in GBM development and the feasibility of therapeutic targeting using conjugated nanoparticles. Methods: After confirmation of miR-29b expression levels in GBM tissues by analysis of open source data, the anticancer effect of miR-29b was tested by the introduction of syn-hsa-miR-29b-3p in the A172 GBM cell line. In vitro studies of cell viability and apoptosis and ex vivo study using GBM tissue slice cultures from 3 patients and nanoparticle delivery of miR-29b were performed. Results: We discovered an increase in apoptotic cell populations with the introduction of miR-29b in the GBM cell line. An established human-derived GBM tissue slice culture system confirmed the anticancer effect of miR- 29b-conjugated nanoparticles. Using PCR array, we found that exogenous miR-29b inhibits the expression of COL1A2, COL3A1, COL4A1, ELN, ITGA11, MMP24, and SPARC, which mediates an anticancer effect. Conclusions: miR-29b may serve as a putative therapeutic molecule when its expression is restored using a nanoparticle delivery system in GBM. [ABSTRACT FROM AUTHOR]

Background: Since high-throughput protein-protein interaction (PPI) data has recently become available for humans, there has been a growing interest in combining PPI data with other genomewide data. In particular, the identification of phenotype-related PPI subnetworks using gene expression data has been of great concern. Successful integration for the identification of significant subnetworks requires the use of a search algorithm with a proper scoring method. Here we propose a multivariate analysis of variance (MANOVA)-based scoring method with a greedy search for identifying differentially expressed PPI subnetworks. Results: Given the MANOVA-based scoring method, we performed a greedy search to identify the subnetworks with the maximum scores in the PPI network. Our approach was successfully applied to human microarray datasets. Each identified subnetwork was annotated with the Gene Ontology (GO) term, resulting in the phenotype-related functional pathway or complex. We also compared these results with those of other scoring methods such as t statistic- and mutual information-based scoring methods. The MANOVA-based method produced subnetworks with a larger number of proteins than the other methods. Furthermore, the subnetworks identified by the MANOVA-based method tended to consist of highly correlated proteins. Conclusion: This article proposes a MANOVA-based scoring method to combine PPI data with expression data using a greedy search. This method is recommended for the highly sensitive detection of large subnetworks. [ABSTRACT FROM AUTHOR]