Identifying gastric cancer related genes using the shortest path algorithm and protein-protein interaction network

Abstract

Gastric cancer, as one of the leading causes of cancer related deaths worldwide, causes about 800,000 deaths per year. Up to now, the mechanism underlying this disease is still not totally uncovered. Identification of related genes of this disease is an important step which can help to understand the mechanism underlying this disease, thereby designing effective treatments. In this study, some novel gastric cancer related genes were discovered based on the knowledge of known gastric cancer related ones. These genes were searched by applying the shortest path algorithm in protein-protein interaction network. The analysis results suggest that some of them are indeed involved in the biological process of gastric cancer, which indicates that they are the actual gastric cancer related genes with high probability. It is hopeful that the findings in this study may help promote the study of this disease and the methods can provide new insights to study various diseases.

Figure 1

The top 10 GO terms enriched by 144 genes. The x-axis lists GO's ID and name, while the y-axis represents the number of genes that shared the GO term among the 144 genes.

Figure 2

The 8 KEGG pathways enriched by 144 genes. The x-axis lists pathway's ID and name, while the y-axis represents the number of genes that shared the pathway among the 144 genes.