BackgroundGene set analysis is moving towards considering pathway topology as a crucial feature. Pathway elements are complex entities such as protein complexes, gene family members and chemical compounds. The conversion of pathway topology to a gene/protein networks (where nodes are a simple element like a gene/protein) is a critical and challenging task that enables topology-based gene set analyses.Unfortunately, currently available R/Bioconductor packages provide pathway networks only from single databases. They do not propagate signals through chemical compounds and do not differentiate between complexes and gene families.ResultsHere we present , a Bioconductor package addressing these issues. Pathway information from four different databases is interpreted following specific biologically-driven rules that allow the reconstruction of gene-gene networks taking into account protein complexes, gene families and sensibly removing chemical compounds from the final graphs. The resulting networks represent a uniform resource for pathway analyses. Indeed, graphite provides easy access to three recently proposed topological methods. The package is available as part of the Bioconductor software suite.Conclusions is an innovative package able to gather and make easily available the contents of the four major pathway databases. In the field of topological analysis acts as a provider of biological information by reducing the pathway complexity considering the biological meaning of the pathway elements.

Nerina and Mario Mattioli Foundation, Cariplo Foundation (Grant Number 2010-0744), and the Italian Association for Cancer Research.

Purpose: Epithelial ovarian cancer (EOC) is one of the most lethal gynecologic diseases, with survival rate virtually unchanged for the past 30 years. EOC comprises different histotypes with molecular and clinical heterogeneity, but up till now the present gold standard platinum-based treatment has been conducted without any patient stratification. The aim of the present study is to generate microRNA (miRNA) profiles characteristic of each stage I EOC histotype, to identify subtype-specific biomarkers to improve our understanding underlying the tumor mechanisms.Experimental Design: A collection of 257 snap-frozen stage I EOC tumor biopsies was gathered together from three tumor tissue collections and stratified into independent training (n ¼ 183) and validation sets (n ¼ 74). Microarray and quantitative real-time PCR (qRT-PCR) were used to generate and validate the histotype-specific markers. A novel dedicated resampling inferential strategy was developed and applied to identify the highest reproducible results. mRNA and miRNA profiles were integrated to identify novel regulatory circuits.Results: Robust miRNA markers for clear cell and mucinous histotypes were found. Specifically, the clear cell histotype is characterized by a five-fold (log scale) higher expression of miR-30a and miR-30a à , whereas mucinous histotype has five-fold (log scale) higher levels of miR-192/194. Furthermore, a mucinous-specific regulatory loop involving miR-192/194 cluster and a differential regulation of E2F3 in clear cell histotype were identified. Conclusions: Our findings showed that stage I EOC histotypes have their own characteristic miRNA expression and specific regulatory circuits. Clin Cancer Res; 19(15); 4114-23. Ó2013 AACR.

Stage I epithelial ovarian cancer (EOC) represents about 10% of all EOCs and is characterized by good prognosis with fewer than 20% of patients relapsing. As it occurs less frequently than advanced-stage EOC, its molecular features have not been thoroughly investigated. We have demonstrated that in stage I EOC can predict patients' outcome. In the present study, we analyzed the expression of long non-coding RNAs (lncRNA) to enable potential definition of a non-coding transcriptional signature with prognostic relevance for stage I EOC. 202 snap-frozen stage I EOC tumor biopsies, 47 of which relapsed, were gathered together from three independent tumor tissue collections and subdivided into a training set ( = 73) and a validation set ( = 129). Median follow up was 9 years. LncRNAs' expression profiles were correlated in univariate and multivariate analysis with overall survival (OS) and progression-free survival (PFS). The expression of -, and was associated in univariate and multivariate analyses with relapse and poor outcome in both training and validation sets ( < 0.001). Using the expression profiles of -, and simultaneously, it was possible to stratify patients into high and low risk. The OS for high- and low-risk individuals are 36 and 123 months, respectively (OR, 15.55; 95% confidence interval, 3.81-63.36). We have identified a non-coding transcriptional signature predictor of survival and biomarker of relapse for stage I EOC. .