Abstract
Smoking is the primary cause of lung cancer and is linked to 85% of lung cancer cases. However, how lung cancer develops in patients with smoking history remains unclear. Systems approaches that combine human protein-protein interaction (PPI) networks and gene expression data are superior to traditional methods. We performed these systems to determine the role that smoking plays in lung cancer development and used the support vector machine (SVM) model to predict PPIs. By defining expression variance (EV), we found 520 dynamic proteins (EV>0.4) using data from the Human Protein Reference Database and Gene Expression Omnibus Database, and built 7 dynamic PPI subnetworks of lung cancer in patients with smoking history. We also determined the primary functions of each subnetwork: signal transduction, apoptosis, and cell migration and adhesion for subnetwork A; cell-sustained angiogenesis for subnetwork B; apoptosis for subnetwork C; and, finally, signal transduction and cell replication and proliferation for subnetworks D-G. The probability distribution of the degree of dynamic protein and static protein differed, clearly showing that the dynamic proteins were not the core proteins which widely connected with their neighbor proteins. There were high correlations among the dynamic proteins, suggesting that the dynamic proteins tend to form specific dynamic modules. We also found that the dynamic proteins were only correlated with the expression of selected proteins but not all neighbor proteins when cancer occurred.