Detecting pathway relationship in the context of human protein-protein interaction network and its application to Parkinson's disease

In human physiological conditions like complex diseases, a large number of genes/proteins, as well as their interactions, are involved. Thus, detecting the biochemical pathways enriched in these genes/proteins and identifying the pathway relationships is critical to understand the molecular mechanisms underlying a disease and can also be valuable in selecting the potential molecular targets for further exploration. In this study, we proposed a method to measure the relationship between pathways based on their distribution in the human PPI network. By representing each pathway as a gene module in the PPI network, a distance was calculated to measure the closeness of two pathways. For the pathways in the KEGG database, a total of 2143 pathway pairs with close connections were identified. Additional evaluations indicated the pathway relationship built via such approach was consistent with available evidence. Further, based on the genes and pathways potentially associated with the pathogenesis of Parkinson's disease (PD), we analyzed the pathway relationship and identified the major pathways related to this disorder via the new method. Also, by analyzing the pathway interaction network constructed by the identified major pathways, we explored the potential pathway targets that may be important in the etiology and development of PD. In summary, we proposed an approach to measure the relationship between pathways, which could provide a more systematic profile on pathways involved in a phenotype, and may also help to improve the result of pathway enrichment analysis.