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• W2085335793 abstract "The aim of the study was to define specific genetic profile in Ta and T1 urinary bladder carcinoma patients with and without recurrence by gene expression microarrays. Eleven patients with the time to recurrence shorter than one year (patients with recurrence) and 11 patients with time to recurrence longer than 4 years (patients without recurrence) were enrolled. Data from microarrays were subjected to a panel of statistical analyses to identify bladder cancer recurrence-associated gene signatures. Initial screening using the GeneSpring and Bioconductor software tools revealed a putative set 47 genes differing in gene expression in both groups. After the validation, 33 genes manifested significant differences between both groups. The significant expression was observed in the group of patients without recurrence by 30 genes of which the highest differences were detected by ANXA1, ARHGEF4, FLJ32252, GNE, NINJ1, PRICKLE1, PSAT1, RNASE1, SPTAN1, SYNGR1, TNFSF15, TSPAN1, and WDR34. These genes code for signal transduction, vascular remodeling and vascular endothelial growth inhibition mainly. In the group with recurrence, 3 genes had significant differences, the highest differences were identified by two genes (PLOD2 and WDR72). Loci of genes with significant changes of gene expression were located on characteristic chromosomes for bladder cancer: 7 loci on chromosome 9, 8 loci on chromosomes 1, 2, 3, 12,14,15,16, and 22. We have selected and validated 15 genes that are differentially expressed in superficial bladder cancer. We hope that this cohort of genes will serve as a promising pool of candidate biomarkers for early stage bladder cancer. Our results indicate that it may be possible to identify patients with a low and high risk of disease recurrence at an early stage using a molecular profile." @default.
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• W2085335793 date "2013-01-01" @default.
• W2085335793 modified "2023-09-29" @default.
• W2085335793 title "Prediction of recurrence in low and intermediate risk non-muscle invasive bladder cancer by real-time quantitative PCR analysis: cDNA microarray results" @default.
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• W2085335793 doi "https://doi.org/10.4149/neo_2013_0391" @default.
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