OBJECTIVEDespite intensive medical treatment, patients with glioblastoma (grade IV glioma [GBM]) have a low 5-year survival rate of 5.5%. In this study, the authors tried to improve currently used therapies by identification of a therapeutic target, IGFBP3, for glioma treatment.METHODSIGFBP3 RNA expression in 135 patients newly diagnosed with glioma was correlated with clinicopathological factors. Immunohistochemical analysis was performed to determine IGFBP3 protein expression in glioma specimens. The effect of IGFBP3 depletion on cell proliferation was examined using IGFBP3 knockdown glioma cells. Intracranial infusion of IGFBP3 siRNAs was performed to evaluate the effect of IGFBP3 depletion in mouse intracranial xenograft models.RESULTSWe demonstrated higher IGFBP3 expression in GBM than in tumor margin and grade II glioma. IGFBP3 expression was not only positively correlated with tumor grades but also associated with tumor histology and IDH1/2 mutation status. Additionally, higher IGFBP3 expression predicted shorter overall survival in glioma and GBM proneural subgroup patients. In vitro cell culture studies suggested IGFBP3 knockdown suppressed cell proliferation and induced cell cycle G2/M arrest as well as apoptosis in glioma cells. Also, accumulation of DNA double-strand breaks and γH2AX was observed in IGFBP3 knockdown cells. IGFBP3 knockdown delayed in vivo tumor growth in mouse subcutaneous xenograft models. Furthermore, convection-enhanced delivery of IGFBP3 siRNA to mouse brain suppressed intracranial tumor growth and prolonged survival of tumor-bearing mice.CONCLUSIONSOur findings suggest IGFBP3 predicts poor outcome of glioma patients and is a potential therapeutic target for which depletion of its expression suppresses tumor growth through inducing apoptosis and accumulation of DNA damage in glioma cells.

Next-generation sequencing (NGS) technologies-based transcriptomic profiling method often called RNA-seq has been widely used to study global gene expression, alternative exon usage, new exon discovery, novel transcriptional isoforms and genomic sequence variations. However, this technique also poses many biological and informatics challenges to extracting meaningful biological information. The RNA-seq data analysis is built on the foundation of high quality initial genome localization and alignment information for RNA-seq sequences. Toward this goal, we have developed RNASEQR to accurately and effectively map millions of RNA-seq sequences. We have systematically compared RNASEQR with four of the most widely used tools using a simulated data set created from the Consensus CDS project and two experimental RNA-seq data sets generated from a human glioblastoma patient. Our results showed that RNASEQR yields more accurate estimates for gene expression, complete gene structures and new transcript isoforms, as well as more accurate detection of single nucleotide variants (SNVs). RNASEQR analyzes raw data from RNA-seq experiments effectively and outputs results in a manner that is compatible with a wide variety of specialized downstream analyses on desktop computers.