BackgroundXenografts have been shown to provide a suitable source of tumor tissue for molecular analysis in the absence of primary tumor material. We utilized ES xenograft series for integrated microarray analyses to identify novel biomarkers.MethodMicroarray technology (array comparative genomic hybridization (aCGH) and micro RNA arrays) was used to screen and identify copy number changes and differentially expressed miRNAs of 34 and 14 passages, respectively. Incubated cells used for xenografting (Passage 0) were considered to represent the primary tumor. Four important differentially expressed miRNAs (miR-31, miR-31*, miR-145, miR-106) were selected for further validation by real time polymerase chain reaction (RT-PCR). Integrated analysis of aCGH and miRNA data was performed on 14 xenograft passages by bioinformatic methods.ResultsThe most frequent losses and gains of DNA copy number were detected at 9p21.3, 16q and at 8, 15, 17q21.32-qter, 1q21.1-qter, respectively. The presence of these alterations was consistent in all tumor passages. aCGH profiles of xenograft passages of each series resembled their corresponding primary tumors (passage 0). MiR-21, miR-31, miR-31*, miR-106b, miR-145, miR-150*, miR-371-5p, miR-557 and miR-598 showed recurrently altered expression. These miRNAS were predicted to regulate many ES-associated genes, such as genes of the IGF1 pathway, EWSR1, FLI1 and their fusion gene (EWS-FLI1). Twenty differentially expressed miRNAs were pinpointed in regions carrying altered copy numbers.ConclusionIn the present study, ES xenografts were successfully applied for integrated microarray analyses. Our findings showed expression changes of miRNAs that were predicted to regulate many ES associated genes, such as IGF1 pathway genes, FLI1, EWSR1, and the EWS-FLI1 fusion genes.
Kaposi's sarcoma (KS) is a mesenchymal tumor, caused by Human herpesvirus 8 (HHV8) with molecular and cytogenetic changes poorly understood. To gain further insight on the underlying molecular changes in KS, we performed microRNA (miRNA) microarray analysis of 17 Kaposi's sarcoma specimens. Three normal skin specimens were used as controls. The most significant differentially expressed miRNA were confirmed by quantitative reverse transcriptase polymerase chain reaction (RT-PCR). We detected in KS versus normal skin 185 differentially expressed miRNAs, 76 were upregulated and 109 were downregulated. The most significantly downregulated miRNAs were miR-99a, miR-200 family, miR-199b-5p, miR-100 and miR-335, whereas kshv-miR-K12-4-3p, kshv-miR-K12-1, kshv-miR-K12-2, kshv-miR-K12-4-5p and kshv-miR-K12-8 were significantly upregulated. High expression levels of kshv-miR-K12-1 (p = 0.004) and kshv-miR-K12-4-3p (p = 0.001) was confirmed by RT-PCR. The predicted target genes for differentially expressed miRNAs included genes which are involved in a variety of cellular processes such as angiogenesis (i.e. THBS1) and apoptosis (i.e. CASP3, MCL1), suggesting a role for these miRNAs in Kaposi's sarcoma pathogenesis.
Multi-task learning, learning of a set of tasks together, can improve performance in the individual learning tasks. Gaussian process models have been applied to learning a set of tasks on different data sets, by constructing joint priors for functions underlying the tasks. In these previous Gaussian process models, the setting has been symmetric in the sense that all the tasks have been assumed to be equally important, whereas in settings such as transfer learning the goal is asymmetric, to enhance performance in a target task given the other tasks. We propose a focused Gaussian process model which introduces an "explaining away" model for each of the additional tasks to model their non-related variation, in order to focus the transfer to the task-of-interest. This focusing helps reduce the key problem of negative transfer, which may cause performance to even decrease if the tasks are not related closely enough. In experiments, our model improves performance compared to single-task learning, symmetric multi-task learning using hierarchical Dirichlet processes, transfer learning based on predictive structure learning, and symmetric multi-task learning with Gaussian processes.
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