title: 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.
authors.affiliation: Colorectal Surgery Department, China-Japan Union Hospital of Jilin University, Changchun 130033, China.
authors.forename: Yang
authors.identifier: 0000-0002-1901-9778
authors.initials: Y
authors.lastname: Jiang
authors.affiliation: State 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.
authors.forename: Yang
authors.identifier: 0000-0002-8304-1531
authors.initials: Y
authors.lastname: Shu
authors.affiliation: Breast and Thyroid Surgery Department, The Second Hospital of Jilin University, Changchun 130041, China.
authors.forename: Ying
authors.initials: Y
authors.lastname: Shi
authors.affiliation: Colorectal Surgery Department, China-Japan Union Hospital of Jilin University, Changchun 130033, China.
authors.forename: Li-Peng
authors.initials: LP
authors.lastname: Li
authors.affiliation: State 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.
authors.forename: Fei
authors.identifier: 0000-0002-9681-6367
authors.initials: F
authors.lastname: Yuan
authors.affiliation: Colorectal Surgery Department, The Second Hospital of Jilin University, Changchun 130041, China.
authors.forename: Hui
authors.initials: H
authors.lastname: Ren
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D057225:Data Mining / Q000379:methods; D030562:Databases, Protein; D006801:Humans; D008954:Models, Biological; D009363:Neoplasm Proteins / Q000378:metabolism*; D025941:Protein Interaction Mapping / Q000379:methods*; D015398:Signal Transduction; D013274:Stomach Neoplasms / Q000378:metabolism*
chemical_list: D009363:Neoplasm Proteins
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