TITLE: Large networks of disease-disease interactions at the medical and molecular level.
ABSTRACT: Biomedicine is confronting significant challenges for the handling and analysis of large data sets, among them a particularly relevant one is the interaction between diseases, i.e. disease comorbidities. Comorbidities are an important medical and social problem that demands an interpretation of the underlying physiological causes, as a necessary step to progress in its management and control. As a first step in this direction, we have analysed two complementary data sets, one composed by a large collection of expression data (RNAseq data 72 human diseases analysed by 107 studies, including a total number of 4.267 samples, from the GREIN platform) the other one base on medical records from three different medical systems (Blumenau, Brazil; Catalonia, Spain; and Indianapolis, United States) or from previous publications (Hidalgo et al. 2009 and Jensen et al. 2014). With this information, we have constructed two disease-disease interaction networks, one reflecting real-world medical associations and the other the similarities of the molecular profiles of patients of different diseases. Interestingly, the two networks have striking similarities in terms of their organization and characteristics. More importantly, we can show for the first time that most disease interactions have a counterpart at the molecular level. This molecular relationship can be translated into detailed molecular basis of specific disease interactions. I will discuss the implications of these results for the interpretation of diseases interactions at the level of specific genes and pathways, as well as the potential consequences for the management of disease comorbidities.
Based on the work of: Beatriz Urda-García, Jon Sanchez, Rosalba Lepore at BSC.