Project description:Variability of meningioma growth, even within benign subgroup, makes some remain unaltered, while others grow fast despite an appended radiotherapy. Biomarkers that differentiate between less and more aggressive meningiomas would therefore have a significant clinical benefit in prediction of their biological behaviour. The aim of the study was to identify new candidate miRNAs enabling better and proper prediction of meningioma recurrence. We used miRNA 4.0 array (Applied Biosystems, Foster City, CA, USA) to detect miRNA profiles in 44 primary meningioma patients (19 with subsequent recurrence) and 5 healthy controls. Moreover, miRNA profiles were analyzed in 15 secondary meningiomas.
Project description:DNA methylation from human meningioma samples that were also profiled for spatial heterogeneity analysis. Some samples represent spatially distinct regions, punched using a 2mm core punch from FFPE blocks in a given tumor. Other samples represent serial tumor samples at index treatment and then recurrence.
Project description:BackgroundMicroarray technology has become a widely used tool in the biological sciences. Over the past decade, the number of users has grown exponentially, and with the number of applications and secondary data analyses rapidly increasing, we expect this rate to continue. Various initiatives such as the External RNA Control Consortium (ERCC) and the MicroArray Quality Control (MAQC) project have explored ways to provide standards for the technology. For microarrays to become generally accepted as a reliable technology, statistical methods for assessing quality will be an indispensable component; however, there remains a lack of consensus in both defining and measuring microarray quality.ResultsWe begin by providing a precise definition of microarray quality and reviewing existing Affymetrix GeneChip quality metrics in light of this definition. We show that the best-performing metrics require multiple arrays to be assessed simultaneously. While such multi-array quality metrics are adequate for bench science, as microarrays begin to be used in clinical settings, single-array quality metrics will be indispensable. To this end, we define a single-array version of one of the best multi-array quality metrics and show that this metric performs as well as the best multi-array metrics. We then use this new quality metric to assess the quality of microarry data available via the Gene Expression Omnibus (GEO) using more than 22,000 Affymetrix HGU133a and HGU133plus2 arrays from 809 studies.ConclusionsWe find that approximately 10 percent of these publicly available arrays are of poor quality. Moreover, the quality of microarray measurements varies greatly from hybridization to hybridization, study to study, and lab to lab, with some experiments producing unusable data. Many of the concepts described here are applicable to other high-throughput technologies.
Project description:Comparison of the gene expression profiles with meningiomas of different grading. 24 primary meningioma cultures from surgical specimen were maintained to primary meningioma cultures.
Project description:We performed expression profiling of 24 meningioma and two dura controls analyzing 55000 transcripts including 18300 known genes. We compared expression in meningioma vs. dura, expression of low grade (WHO I) vs. higher-grade (WHO II and WHO IIII) tumors and expression of meningothelial and syncytial meningioma vs. fibroblastic meningioma. Gene expression was analysed in 24 meningioma including eight of each WHO grade and two dura controls analyzing 55000 transcripts including 18300 known genes.
Project description:Affymetrix GeneChip microarrays are the most widely used high-throughput technology to measure gene expression, and a wide variety of preprocessing methods have been developed to transform probe intensities reported by a microarray scanner into gene expression estimates. There have been numerous comparisons of these preprocessing methods, focusing on the most common analyses-detection of differential expression and gene or sample clustering. Recently, more complex multivariate analyses, such as gene co-expression, differential co-expression, gene set analysis and network modeling, are becoming more common; however, the same preprocessing methods are typically applied. In this article, we examine the effect of preprocessing methods on some of these multivariate analyses and provide guidance to the user as to which methods are most appropriate.