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BioMed Research International
Volume 2013 (2013), Article ID 267375, 8 pages
http://dx.doi.org/10.1155/2013/267375
Research Article

Identification of Lung-Cancer-Related Genes with the Shortest Path Approach in a Protein-Protein Interaction Network

1The Key Laboratory of Stem Cell Biology, Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
2Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
3Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
4College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
5Department of Ophthalmology, Shanghai First People's Hospital, Shanghai Jiaotong University, Shanghai 200080, China
6Department of Biomedical Engineering Tianjin University, Tianjin Key Lab of BME Measurement, Tianjin 300072, China
7CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
8Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, NY 10029, USA
9Institute of Systems Biology, Shanghai University, Shanghai 200444, China

Received 12 March 2013; Revised 19 April 2013; Accepted 29 April 2013

Academic Editor: Bing Niu

Copyright © 2013 Bi-Qing Li 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

Lung cancer is one of the leading causes of cancer mortality worldwide. The main types of lung cancer are small cell lung cancer (SCLC) and nonsmall cell lung cancer (NSCLC). In this work, a computational method was proposed for identifying lung-cancer-related genes with a shortest path approach in a protein-protein interaction (PPI) network. Based on the PPI data from STRING, a weighted PPI network was constructed. 54 NSCLC- and 84 SCLC-related genes were retrieved from associated KEGG pathways. Then the shortest paths between each pair of these 54 NSCLC genes and 84 SCLC genes were obtained with Dijkstra’s algorithm. Finally, all the genes on the shortest paths were extracted, and 25 and 38 shortest genes with a permutation value less than 0.05 for NSCLC and SCLC were selected for further analysis. Some of the shortest path genes have been reported to be related to lung cancer. Intriguingly, the candidate genes we identified from the PPI network contained more cancer genes than those identified from the gene expression profiles. Furthermore, these genes possessed more functional similarity with the known cancer genes than those identified from the gene expression profiles. This study proved the efficiency of the proposed method and showed promising results.