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BioMed Research International
Volume 2014 (2014), Article ID 891945, 8 pages
http://dx.doi.org/10.1155/2014/891945
Research Article

A Graphic Method for Identification of Novel Glioma Related Genes

1Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, China
2State Key Laboratory of Medical Genomics, Institute of Health Sciences, Shanghai Jiaotong University School of Medicine and Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200025, China
3Endoscopy Center, China-Japan Union Hospital of Jilin University, Changchun 130033, China
4Institute of Systems Biology, Shanghai University, Shanghai 200444, China
5CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China

Received 18 April 2014; Revised 25 May 2014; Accepted 28 May 2014; Published 23 June 2014

Academic Editor: Tao Huang

Copyright © 2014 Yu-Fei Gao 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

Glioma, as the most common and lethal intracranial tumor, is a serious disease that causes many deaths every year. Good comprehension of the mechanism underlying this disease is very helpful to design effective treatments. However, up to now, the knowledge of this disease is still limited. It is an important step to understand the mechanism underlying this disease by uncovering its related genes. In this study, a graphic method was proposed to identify novel glioma related genes based on known glioma related genes. A weighted graph was constructed according to the protein-protein interaction information retrieved from STRING and the well-known shortest path algorithm was employed to discover novel genes. The following analysis suggests that some of them are related to the biological process of glioma, proving that our method was effective in identifying novel glioma related genes. We hope that the proposed method would be applied to study other diseases and provide useful information to medical workers, thereby designing effective treatments of different diseases.