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 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, co-expression, 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 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. Study on other disorders such as obesity
and blood pressure also revealed promising results.
