Objective: It is a known fact that numerous complex disorders do not happen in
isolation indicating the plausible set of shared causes common to several different sicknesses.
Hence, analysis of comorbidity can be utilized to explore the association between several
disorders. In this study, we have proposed a network-based computational approach, in which
genes are organized based on the topological characteristics of the constructed Protein-Protein
Interaction Network (PPIN) followed by a network prioritization scheme, to identify distinctive
key genes and biological pathways shared among diseases.
Methods: The proposed approach is initiated from constructed PPIN of any randomly chosen
disease genes in order to infer its associations with other diseases in terms of shared pathways, coexpression,
co-occurrence etc. For this, initially, proteins associated to any disease based on
random choice were identified. Secondly, PPIN is organized through topological analysis to define
hub genes. Finally, using a prioritization algorithm a ranked list of newly predicted
multimorbidity-associated proteins is generated. Using Gene Ontology (GO), cellular pathways
involved in multimorbidity-associated proteins are mined.
Result and Conclusion: The proposed methodology is tested using three disorders, namely
Diabetes, Obesity and blood pressure at an atomic level and the results suggest the comorbidity of
other complex diseases that have associations with the proteins included in the disease of present
study through shared proteins and pathways. For diabetes, we have obtained key genes like
GAPDH, TNF, IL6, AKT1, ALB, TP53, IL10, MAPK3, TLR4 and EGF with key pathways like
P53 pathway, VEGF signaling pathway, Ras Pathway, Interleukin signaling pathway, Endothelin
signaling pathway, Huntington disease etc. Studies on other disorders such as obesity and blood
pressure also revealed promising results.