In summary, using ICod, we determined relationships among five diseases (prostate cancer, breast cancer, colon cancer, T2DM, and obesity), three pathological conditions (inflammation, insulin resistance, and immune response), and the essential gene network. As expected from pathological symptoms in complex diseases sharing common phenotypic signals including inflammation, the results of ICod support our knowledge at the network level. The pathological condition network is closely associated with various disease networks. Our findings are the first attempt at uncovering the differences in topological role between each disease- and symptom-related network within the essential gene network using analysis of attack tolerance. Although PPIs of pathological conditions significantly overlapped with disease PPIs, the patterns of collapsing the essential network by removing the condition-related nodes were clearly distinct from attacks on disease-related nodes. While our network analysis covered partial sets of human diseases and symptoms, our conceptual approach successfully modeled functional roles of pathological states in disease etiology and maintenance of the essential network. Typical pathological symptoms, such as inflammation and immune responses, are widely spread mechanisms behind complex diseases with subtle impacts that can cause severe dysfunctions of the essential network.
Since our network model focused topological similarity, our method suggested network relationships between disease pairs, or disease-pathological symptoms without causal understandings and functional significance. To address network related functional impact (i.e., complete node removal and partial mutation), Zhong et al. attempted computational and experimental validation using Yeast-Two-Hybrid system (Y2H) . In our previous study, we analyzed gene expression patterns in diet induced obese mice . Interestingly, diet-induced obese mice displayed differentially expressed genes, which were related inflammation, immune response and insulin resistance as we suggested our network similarity analysis. While our previous work suggested enriched functional signatures under induced obesity condition without node-removal effect, significantly depict obesity derived pathological phenotype in time-resolving frame. Based on theses attempts, we suggest an approach combining Zhong et al’ s Y2H and time-resolving frame of ours for further functional understanding. Owing to the utilization of model organisms, Zhang et al. and our mouse data analysis give us limited understandings to depict underlying mechanisms of human diseases. Thus, as we conducted in our previous attempt , large-scale human cohort based analysis might shed light shared genetic and functional features, which lead disease comorbidity.
While cancers have shown high mortality rates , obesity and T2DM have low attributes for viability issues. In stark contrast with our expectation, topological roles for the interactions within the essential gene network are homogeneous between lethal diseases (cancers) and other chronic diseases. Therefore, further study is necessary on the associations between disease mortality and aspects of network structure, such as “bottleneckness” . In addition, our keyword-based approach to preparing disease and pathological symptom related genes were introduced for the proof-of-concept. Thus, it is necessary for advanced validation of disease related genes using various approaches, such as scrutinizing gene expression databases .
As shown in our network analysis of disease PPI similarity and US Medicare data, disease onset and comorbidity are closely associated with the breakage of common functional modules. Complex diseases and pathological conditions share molecular mechanisms such as PPIs, whereas mortalities are heterogeneous. We distinguished network models of complex diseases and pathological conditions using our analysis of attack tolerance of the essential network to find the different impacts on mortality issues.
One of our contributions is computing quantitative degree of overlapping between disease PPIs involving across similarity among diseases and related clinical manifestations considering connectivity of compared PPI pairs (Figure 2). Since ICod utilized public repository of PPI networks and list of disease related genes, our method can be a streamlined route to visualize similarity between diseases and pathological phenotypes, which are associated disease comorbidity . In addition, our network-based disease similarity might present drug targets related various diseases as presented by Suthram et al.. For example, ICod remarks a probability for the repositioning of drugs related pathological symptoms, such as inflammation, for the therapy of PPI overlapped diseases including obesity; The anti-inflammation drug, amlexanox, elevate energy expenditure and produce weigh loss in mice .