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BioMed Research International
Volume 2014 (2014), Article ID 371397, 9 pages
http://dx.doi.org/10.1155/2014/371397
Research Article

Identifying Gastric Cancer Related Genes Using the Shortest Path Algorithm and Protein-Protein Interaction Network

1Colorectal Surgery Department, China-Japan Union Hospital of Jilin University, Changchun 130033, China
2State Key Laboratory of Medical Genomics, Institute of Health Sciences, Chinese Academy of Sciences, Shanghai Jiao Tong University School of Medicine and Shanghai Institutes for Biological Sciences, Shanghai 200025, China
3Breast and Thyroid Surgery Department, The Second Hospital of Jilin University, Changchun 130041, China
4Colorectal Surgery Department, The Second Hospital of Jilin University, Changchun 130041, China

Received 29 December 2013; Accepted 3 February 2014; Published 5 March 2014

Academic Editor: Tao Huang

Copyright © 2014 Yang Jiang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.