GCGene: a gene resource for gastric cancer with literature evidence
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Min Zhao1, Luming Chen2, Yining Liu1 and Hong Qu2
1 School of Engineering, Faculty of Science, Health, Education and Engineering, University of The Sunshine Coast, Maroochydore DC, Queensland, Australia
2 Center for Bioinformatics, State Key Laboratory of Protein and Plant Gene Research, College of Life Sciences, Peking University, Beijing, P.R. China
Min Zhao, email:
Hong Qu, email:
Keywords: gastric cancer, database, cancer genomics, functional analysis
Received: December 23, 2015 Accepted: April 16, 2016 Published: April 26, 2016
Gastric cancer (GC) is the fifth most common cancer and third leading cause of cancer-related deaths worldwide. Its lethality primarily stems from a lack of detection strategies for early stages of GC and a lack of noninvasive detection strategies for advanced stages. The development of early diagnostic biomarkers largely depends on understanding the biological pathways and regulatory mechanisms associated with putative GC genes. Unfortunately, the GC-implicated genes that have been identified thus far are scattered among thousands of published studies, and no systematic summary is available, which hinders the development of a large-scale genetic screen. To provide a publically accessible resource tool to meet this need, we constructed a literature-based database GCGene (Gastric Cancer Gene database) with comprehensive annotations supported by a user-friendly website. In the current release, we have collected 1,815 unique human genes including 1,678 protein-coding and 137 non-coding genes curated from extensive examination of 3,142 PubMed abstracts. The resulting database has a convenient web-based interface to facilitate both textual and sequence-based searches. All curated genes in GCGene are downloadable for advanced bioinformatics data mining. Gene prioritization was performed to rank the relative relevance of these genes in GC development. The 100 top-ranked genes are highly mutated according to the cohort of published studies we reviewed. By conducting a network analysis of these top-ranked GC-associated genes in the human interactome, we were able to identify strong links between 8 highly connected genes with low expression and patient survival time. GCGene is freely available to academic users at http://gcgene.bioinfo-minzhao.org/.
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