Abstract
Disease relationship studies for understanding the pathogenesis of complex diseases, diagnosis, prognosis, and drug development are important. Traditional approaches consider one type of disease data or aggregating multiple types of disease data into a single network, which results in important temporal- or context-related information loss and may distort the actual organization. Therefore, it is necessary to apply multilayer network model to consider multiple types of relationships between diseases and the important interplays between different relationships. Further, modules extracted from multilayer networks are smaller and have more overlap that better capture the actual organization. Here, we constructed a weighted four-layer disease-disease similarity network to characterize the associations at different levels between diseases. Then, a tensor-based computational framework was used to extract Conserved Disease Modules (CDMs) from the four-layer disease network. After filtering, nine significant CDMs were reserved. The statistical significance test proved the significance of the nine CDMs. Comparing with modules got from four single layer networks, CMDs are smaller, better represent the actual relationships, and contain potential disease-disease relationships. KEGG pathways enrichment analysis and literature mining further contributed to confirm that these CDMs are highly reliable. Furthermore, the CDMs can be applied to predict potential drugs for diseases. The molecular docking techniques were used to provide the direct evidence for drugs to treat related disease. Taking Rheumatoid Arthritis (RA) as a case, we found its three potential drugs Carvedilol, Metoprolol, and Ramipril. And many studies have pointed out that Carvedilol and Ramipril have an effect on RA. Overall, the CMDs extracted from multilayer networks provide us with an impressive understanding disease mechanisms from the perspective of multi-layer network and also provide an effective way to predict potential drugs for diseases based on its neighbors in a same CDM.
Highlights
Complex diseases, such as cancers, diabetes mellitus, and cardiovascular disease, are caused by the combined effects of multiple genes, lifestyles and environmental factors (Craig, 2008), which makes it difficult to study and treat diseases
Human Disease Network Based on Protein Interaction Network (PIDN) The protein-protein interaction (PPI) network was got from ref (Menche et al, 2015), which consists of 13,460 genes and 141,296 interactions
In order to get the similarity between diseases based on the PPI network, we combined two datasets got from Online Mendelian Inheritance in Man (OMIM) database (Hamosh et al, 2005) and Genome-Wide Association Studies (GWAS) (Ramos et al, 2014) to get the disease-gene data, which includes 718 diseases and 22,410 genes
Summary
Complex diseases, such as cancers, diabetes mellitus, and cardiovascular disease, are caused by the combined effects of multiple genes, lifestyles and environmental factors (Craig, 2008), which makes it difficult to study and treat diseases. Studying the pathogenesis of diseases is critical to treat diseases because if it is controlled, the disease would be prevented (Last, 2000). Network theory is an available and useful solution for describing and analyzing the relationships between complex diseases (Barabási and Oltvai, 2004). There are many network-based methods proposed to analyze diseases similarity. Zhou et al (2014) constructed a human symptoms-based disease network using large-scale medical bibliographic records and the related Medical Subject Headings (MeSH) (Lowe and Barnett, 1994) metadata from PubMed (Wheeler et al, 2007). Based on protein interactions and functional pathways, Liang et al constructed a human disease network (HPDN) based on pathways to explore the potential relationships between diseases (Yu and Gao, 2017)
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