High density micro-RNA (miRNA) arrays, fluorescent-reporter miRNA assay and Northern miRNA dot-blot analysis show that a brain-enriched miRNA-128 is significantly down-regulated in glioblastoma multiforme (GBM) and in GBM cell lines when compared to age-matched controls. The down-regulation of miRNA-128 was found to inversely correlate with WHO tumor grade. Three bioinformatics-verified miRNA-128 targets, angiopoietin-related growth factor protein 5 (ARP5; ANGPTL6), a transcription suppressor that promotes stem cell renewal and inhibits the expression of known tumor suppressor genes involved in senescence and differentiation, Bmi-1, and a transcription factor critical for the control of cell-cycle progression, E2F-3a, were found to be up-regulated. Addition of exogenous miRNA-128 to CRL-1690 and CRL-2610 GBM cell lines (a) restored 'homeostatic' ARP5 (ANGPTL6), Bmi-1 and E2F-3a expression, and (b) significantly decreased the proliferation of CRL-1690 and CRL-2610 cell lines. Our data suggests that down-regulation of miRNA-128 may contribute to glioma and GBM, in part, by coordinately up-regulating ARP5 (ANGPTL6), Bmi-1 and E2F-3a, resulting in the proliferation of undifferentiated GBM cells.
In life threatening diseases, such as cancer, where the effective diagnosis includes annotation, early detection, distinction, and prediction, data mining and statistical approaches offer the promise for precise, accurate, and functionally robust analysis of gene expression data. The computational extraction of derived patterns from microarray gene expression is a non-trivial task that involves sophisticated algorithm design and analysis for specific domain discovery. In this paper, we have proposed a formal approach for feature extraction by first applying feature selection heuristics based on the statistical impurity measures, the Gini Index, Max Minority, and the Twoing Rule and obtaining the top 100-400 genes. We then analyze the associative dependencies between the genes and assign weights to the genes based on their degree of participation in the rules. Consequently, we present a weighted Jaccard and vector cosine similarity measure to compute the similarity between the discovered rules. Finally, we group the rules by applying hierarchical clustering. To demonstrate the usability and efficiency of the concept of our technique, we applied it to three publicly available, multiclass cancer gene expression datasets and performed a biomedical literature search to support the effectiveness of our results.
Abstract:In life threatening diseases, such as cancer, where the effective diagnosis includes annotation, early detection, distinction, and prediction, data mining and statistical approaches offer the promise for precise, accurate, and functionally robust analysis of gene expression data. The computational extraction of derived patterns from microarray gene expression is a non-trivial task that involves sophisticated algorithm design and analysis for specific domain discovery. In this paper, we have proposed a formal approach for feature extraction by first applying feature selection heuristics based on the statistical impurity measures, the Gini Index, Max Minority, and the Twoing Rule and obtaining the top 100-400 genes. We then analyze the associative dependencies between the genes and assign weights to the genes based on their degree of participation in the rules. Consequently, we present a weighted Jaccard and vector cosine similarity measure to compute the similarity between the discovered rules. Finally, we group the rules by applying hierarchical clustering. To demonstrate the usability and efficiency of the concept of our technique, we applied it to three publicly available, multiclass cancer gene expression datasets and performed a biomedical literature search to support the effectiveness of our results.
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