Since the discovery of leptin in 1994, our vision of adipose tissue as a static organ regulating mainly lipid storage and release has been completely overthrown, and adipose tissue is now seen as an active and integral organ in human physiology. In the past years, extensive research has tremendously given us more insights in the mechanisms and pathways involved not only in normal but also in ‘sick’ adipose tissue, for example, in obesity and lipodystrophy. With growing evidence of a link between obesity and several types of cancer, research focusing on the interaction between adipose tissue and cancer has begun to unravel the interesting but complex multi-lateral communication between the different players. With breast cancer as one of the first cancer types where a positive correlation between obesity and breast cancer incidence and prognosis in post-menopausal women was found, we have focused this review on the paracrine and endocrine role of adipose tissue in breast cancer initiation and progression. As important inter-species differences in adipose tissue occur, we mainly selected human adipose tissue- and breast cancer-based studies with a short reflection on therapeutic possibilities. This review is part of the special issue on “Adiposopathy in Cancer and (Cardio)Metabolic Diseases”.
The rapid expansion of biomedical literature has provoked an increased development of advanced text mining tools to rapidly extract relevant events from the continuously increasing amount of knowledge published periodically in PubMed. However, bioinvestigators are still reluctant to use these tools for two reasons: i) a large volume of events is often extracted upon a query, and this volume is hard to manage, and ii) background events dominate search results and overshadow more pertinent published information, especially for domain experts. In this paper, we propose an approach that incorporates the temporal dimension of published events to the process of information extraction to improve data selection and prioritize more pertinent periodically published knowledge for scientists. Indeed, instead of providing the total knowledge associated with a PubMed query, which is usually a mix of trivial background information and nonbackground information, we propose a method that incorporates time and selects non background and highly relevant biological entities and events published over time for bioinvestigators. Before excluding background events from the total knowledge extracted, a quantification of their amount is also provided. This work is illustrated by a case study regarding Hepcidin gene publications over a decade, a duration that is sufficiently long enough to generate alternative views on the overall data extracted.
In biomedical research, interpretation of microarray data requires confrontation of data and knowledge from heterogeneous resources, either in the biomedical domain or in genomics, as well as restitution and analysis methods adapted to huge amounts of data. We present a combined approach that relies on two components: BioMeKE annotates sets of genes using biomedical GO and UMLS concepts, and GEDAW, a Gene Expression Data Warehouse, uses BioMeKE to enrich experimental results with biomedical concepts, thus performing complex analyses of expression measurements through analysis workflows. The strength of our approach has been demonstrated within the framework of analysis of data resulting from the liver transcriptome study. It allowed new genes potentially associated with liver diseases to be highlighted.