Publications by authors named "Kathrin Poos"

Purpose: A low mutation rate seems to be a general feature of pediatric cancers, in particular in oncofusion gene-driven tumors. Genetically, Ewing sarcoma is defined by balanced chromosomal EWS/ETS translocations, which give rise to oncogenic chimeric proteins (EWS-ETS). Other contributing somatic mutations involved in disease development have only been observed at low frequency.

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Osteosarcoma (OS), a bone tumor, exhibit a complex karyotype. On the genomic level a highly variable degree of alterations in nearly all chromosomal regions and between individual tumors is observable. This hampers the identification of common drivers in OS biology.

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Osteosarcoma (OS) is the most common primary bone cancer exhibiting high genomic instability. This genomic instability affects multiple genes and microRNAs to a varying extent depending on patient and tumor subtype. Massive research is ongoing to identify genes including their gene products and microRNAs that correlate with disease progression and might be used as biomarkers for OS.

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Osteosarcomas (OS) are complex bone tumors with various genomic alterations. These alterations affect the expression and function of several genes due to drastic changes in the underlying gene regulatory network. However, we know little about critical gene regulators and their functional consequences on the pathogenesis of OS.

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A whole chromosome arm loss of 16q belongs to the most frequent and earliest chromosomal alterations in invasive and in situ breast cancers of all common subtypes. Besides E-cadherin, several putative tumour suppressor genes residing on 16q in breast cancer have been investigated. However, the significance of these findings has remained unclear.

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Heterosis is a well-known phenomenon but the underlying molecular mechanisms are not yet established. To contribute to the understanding of heterosis at the molecular level, we analyzed genome-wide gene expression profile data of Arabidopsis thaliana in a systems biological approach. We used partial correlations to estimate the global interaction structure of regulatory networks.

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