title: A novel integrative network approach to understand the interplay between cardiovascular disease and other complex disorders.
abstract: There is accumulating evidence that the proteins encoded by the genes associated with a common disorder interact with each other, participate in similar pathways and share GO terms. It has been anticipated that the functional modules in a disease related functional linkage network can be integrated with bibliomics to reveal association with other complex disorders. In this study, the cardiovascular disease functional linkage network (CFN) containing 1536 nodes and 3345 interactions was constructed using proteins encoded by 234 genes associated with the disease. Integration of CFN with bibliomics showed that 227 out of 566 functional modules are significantly associated with one or more diseases. Analysis of functional modules revealed the possible regulatory roles of SP1 and CXCL12 in the pathogenesis of cardiovascular disease (CVD) and modulation of their activities may be considered as potential therapeutic tools. The integration of CFN with bibliomics also indicated significant relations of CVD with other complex disorders. In a stratified map the members of 227 functional modules and 58 diseases in 15 disease classes were combined. In this map, leprosy, listeria monocytogenes, myasthenia, hemorrhagic diathesis and Protein S deficiency, which were not previously reported to be associated with CVD, showed significant associations. Several cancers arising from epithelial cells were also found to be linked to other diseases through hub proteins, VEGFA and PTGS2.
authors.affiliation: Rensselaer Nanotechnology Center, Rensselaer Polytechnic Institute, Troy, NY12180, USA. rended3@rpi.edu
authors.forename: Deniz
authors.initials: D
authors.lastname: Rende
authors.forename: Nihat
authors.initials: N
authors.lastname: Baysal
authors.forename: Betul
authors.initials: B
authors.lastname: Kirdar
mesh_terms: D002318:Cardiovascular Diseases / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D053263:Gene Regulatory Networks; D005796:Genes; D006801:Humans; D015398:Signal Transduction / Q000235:genetics*