6,331 research outputs found

    Correlated fragile site expression allows the identification of candidate fragile genes involved in immunity and associated with carcinogenesis

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    Common fragile sites (cfs) are specific regions in the human genome that are particularly prone to genomic instability under conditions of replicative stress. Several investigations support the view that common fragile sites play a role in carcinogenesis. We discuss a genome-wide approach based on graph theory and Gene Ontology vocabulary for the functional characterization of common fragile sites and for the identification of genes that contribute to tumour cell biology. CFS were assembled in a network based on a simple measure of correlation among common fragile site patterns of expression. By applying robust measurements to capture in quantitative terms the non triviality of the network, we identified several topological features clearly indicating departure from the Erdos-Renyi random graph model. The most important outcome was the presence of an unexpected large connected component far below the percolation threshold. Most of the best characterized common fragile sites belonged to this connected component. By filtering this connected component with Gene Ontology, statistically significant shared functional features were detected. Common fragile sites were found to be enriched for genes associated to the immune response and to mechanisms involved in tumour progression such as extracellular space remodeling and angiogenesis. Our results support the hypothesis that fragile sites serve a function; we propose that fragility is linked to a coordinated regulation of fragile genes expression.Comment: 18 pages, accepted for publication in BMC Bioinformatic

    Argonaute 2 drives miR-145-5p-dependent gene expression program in breast cancer cells

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    To perform their regulatory functions, microRNAs (miRNAs) must assemble with any of the four mammalian Argonaute (Ago) family of proteins, Ago1–4, into an effector complex known as the RNA-induced silencing complex (RISC). While the mature miRNA guides the RISC complex to its target mRNA, the Ago protein represses mRNA translation. The specific roles of the various Ago members in mediating miRNAs activity, however, haven’t been clearly established. In this study, we investigated the contribution of Ago2, the only human Ago protein endowed with nuclease activity, to the function of tumor-suppressor miR-145-5p in breast cancer (BC). We show that miR-145-5p and Ago2 protein are concomitantly downregulated in BC tissues and that restoration of miR-145-5p expression in BC cells leads to Ago2 protein induction through the loosening of Ago2 mRNA translational repression. Functionally, miR-145-5p exerts its inhibitory activity on cell migration only in presence of Ago2, while, upon Ago2 depletion, we observed increased miR-145/Ago1 complex and enhanced cell motility. Profiling by microarray of miR-145-5p target mRNAs, in BC cells depleted or not of Ago2, revealed that miR-145-5p drives Ago2-dependent and -independent activities. Our results highlight that the Ago2 protein in cancer cells strictly dictates miR-145-5p tumor suppressor activity

    Combined population dynamics and entropy modelling supports patient stratification in chronic myeloid leukemia

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    Modelling the parameters of multistep carcinogenesis is key for a better understanding of cancer progression, biomarker identification and the design of individualized therapies. Using chronic myeloid leukemia (CML) as a paradigm for hierarchical disease evolution we show that combined population dynamic modelling and CML patient biopsy genomic analysis enables patient stratification at unprecedented resolution. Linking CD34+ similarity as a disease progression marker to patientderived gene expression entropy separated established CML progression stages and uncovered additional heterogeneity within disease stages. Importantly, our patient data informed model enables quantitative approximation of individual patients’ disease history within chronic phase (CP) and significantly separates “early” from “late” CP. Our findings provide a novel rationale for personalized and genome-informed disease progression risk assessment that is independent and complementary to conventional measures of CML disease burden and prognosis

    Proteomic profile of KSR1-regulated signalling in response to genotoxic agents in breast cancer

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    Kinase suppressor of Ras 1 (KSR1) has been implicated in tumorigenesis in multiple cancers, including skin, pancreatic and lung carcinomas. However, our recent study revealed a role of KSR1 as a tumour suppressor in breast cancer, the expression of which is potentially correlated with chemotherapy response. Here, we aimed to further elucidate the KSR1-regulated signalling in response to genotoxic agents in breast cancer. Stable isotope labelling by amino acids in cell culture (SILAC) coupled to high-resolution mass spectrometry (MS) was implemented to globally characterise cellular protein levels induced by KSR1 in the presence of doxorubicin or etoposide. The acquired proteomic signature was compared and GO-STRING analysis was subsequently performed to illustrate the activated functional signalling networks. Furthermore, the clinical associations of KSR1 with identified targets and their relevance in chemotherapy response were examined in breast cancer patients. We reveal a comprehensive repertoire of thousands of proteins identified in each dataset and compare the unique proteomic profiles as well as functional connections modulated by KSR1 after doxorubicin (Doxo-KSR1) or etoposide (Etop-KSR1) stimulus. From the up-regulated top hits, several proteins, including STAT1, ISG15 and TAP1 are also found to be positively associated with KSR1 expression in patient samples. Moreover, high KSR1 expression, as well as high abundance of these proteins, is correlated with better survival in breast cancer patients who underwent chemotherapy. In aggregate, our data exemplify a broad functional network conferred by KSR1 with genotoxic agents and highlight its implication in predicting chemotherapy response in breast cancer
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