An In silico method to identify key proteins involved in the development of gastric cancer

Gastric cancer is the first most common cancer death in Iran. There have been many efforts in finding the most effective proteins in this cancer. Using the proteins identified in gastric cancer combined with advanced computational tools in analyzing biological networks, we have developed a rational method in order to identify candidate proteins associated with this cancer.
In this study, The analytical procedure is performed with quantitative view. At the beginning we extracted the available studied proteins using a comprehensive literature search, Important proteins in gastric cancer were extracted from the available scientific articles, then the protein interaction databases were searched using Cytoscape MiMI plugin in order to draw the interaction network. Using the Centiscape plugin more key nodes were identified and the Degree, Stress, Betweenness and Closeness were examined. We next added the available expression data and recalculated the parameters.
Our beginning list was 72 known proteins involved in gastric cancer, after creating a comprehensive network using the earlier mentioned tools and databases a network with 1673 nodes was created. Examining the GO term using the BINGO plugin most of the proteins were involved in the regulatory processes (65007, 50789, and 50794). Results in the network core index showed that HNF4A and TAF1 were the major key proteins in the protein interaction network which can be greatly involved in the development of gastric cancer.
The proteins identified in this study can be used as diagnostic markers and therapeutic targets used in gastric cancer. The process presented in this study can be used to identify key targets in other diseases as well.