Background Progression of conventional urothelial carcinoma of the bladder to a tumor with unique microscopic features referred to as micropapillary carcinoma is coupled with aggressive clinical behavior signified by a high propensity for metastasis to regional lymph nodes and distant organs resulting in shorter survival. Objective To analyze the expression profile of micropapillary cancer and define its molecular features relevant to clinical behavior. Design, setting, and participants We retrospectively identified 43 patients with micropapillary bladder cancers and a reference set of 89 patients with conventional urothelial carcinomas and performed whole-genome expression mRNA profiling. Outcome, measurements and statistical analysis The tumors were segregated into distinct groups according to hierarchical clustering analyses. In addition, the tumors were classified according to luminal, p53-like, and basal categories using previously described algorithm. We applied IPA and GSEA for pathway analyses. Cox proportional hazards models and Kaplan-Meier methods were used to assess the relationship between survival and molecular subtypes. The expression profile of micropapillary cancer was validated for selected markers by immunohistochemistry on parallel tissue microarrays. Results We show that the striking features of micropapillary cancer are downregulation of miR-296 and activation of chromatin-remodeling complex RUVBL1. In contrast to conventional urothelial carcinomas which, based on their expression, can be equally divided into luminal and basal subtypes, micropapillary cancer is almost exclusively luminal, displaying enrichment of active PPARγ and suppression of p63 target genes. As with conventional luminal urothelial carcinomas, a subset of micropapillary cancers exhibit activation of wild-type p53 downstream genes and represent the most aggressive molecular subtype of the disease, with the shortest survival. Conclusions Micropapillary cancer evolves through the luminal pathway and is characterized by the activation of miR-296 and RUVBL1 target genes. Limitations The involvement of miR-296 and RUVBL1 in the development of micropapillary bladder cancer was identified by the analyses of correlative associations of genome expression profiles and requires mechanistic validation. Patient summary Our observations have important implications for prognosis and for possible future development of more effective therapies for micropapillary bladder cancer.

BackgroundAs suggested by the origin of the word, sphingolipids are mysterious molecules with various roles in antagonistic cellular processes such as autophagy, apoptosis, proliferation and differentiation. Moreover, sphingolipids have recently been recognized as important messengers in cellular signaling pathways. Notably, sphingolipid metabolism disorders have been observed in various pathological conditions such as cancer and neurodegeneration.ResultsThe existing formal models of sphingolipid metabolism focus mainly on de novo ceramide synthesis or are limited to biochemical transformations of particular subspecies. Here, we propose the first comprehensive computational model of sphingolipid metabolism in human tissue. Contrary to the previous approaches, we use a model that reflects cell compartmentalization thereby highlighting the differences among individual organelles.ConclusionsThe model that we present here was validated using recently proposed methods of model analysis, allowing to detect the most sensitive and experimentally non-identifiable parameters and determine the main sources of model variance. Moreover, we demonstrate the usefulness of our model in the study of molecular processes underlying Alzheimer’s disease, which are associated with sphingolipid metabolism.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-015-0176-9) contains supplementary material, which is available to authorized users.

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RNA microarrays and RNA-seq are nowadays standard technologies to study the transcriptional activity of cells. Most studies focus on tracking transcriptional changes caused by specific experimental conditions. Information referring to genes up- and downregulation is evaluated analyzing the behaviour of relatively large population of cells by averaging its properties. However, even assuming perfect sample homogeneity, different subpopulations of cells can exhibit diverse transcriptomic profiles, as they may follow different regulatory/signaling pathways. The purpose of this study is to provide a novel methodological scheme to account for possible internal, functional heterogeneity in homogeneous cell lines, including cancer ones. We propose a novel computational method to infer the proportion between subpopulations of cells that manifest various functional behaviour in a given sample. Our method was validated using two datasets from RNA microarray experiments. Both experiments aimed to examine cell viability in specific experimental conditions. The presented methodology can be easily extended to RNA-seq data as well as other molecular processes. Moreover, it complements standard tools to indicate most important networks from transcriptomic data and in particular could be useful in the analysis of cancer cell lines affected by biologically active compounds or drugs.