Gastric Cancer Associated Genes Identified by an Integrative Analysis of Gene Expression Data
Joint Authors
Jiang, Zhi
Jiang, Bing
Li, Shuwen
Shao, Ping
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-01-23
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
Gastric cancer is one of the most severe complex diseases with high morbidity and mortality in the world.
The molecular mechanisms and risk factors for this disease are still not clear since the cancer heterogeneity caused by different genetic and environmental factors.
With more and more expression data accumulated nowadays, we can perform integrative analysis for these data to understand the complexity of gastric cancer and to identify consensus players for the heterogeneous cancer.
In the present work, we screened the published gene expression data and analyzed them with integrative tool, combined with pathway and gene ontology enrichment investigation.
We identified several consensus differentially expressed genes and these genes were further confirmed with literature mining; at last, two genes, that is, immunoglobulin J chain and C-X-C motif chemokine ligand 17, were screened as novel gastric cancer associated genes.
Experimental validation is proposed to further confirm this finding.
American Psychological Association (APA)
Jiang, Bing& Li, Shuwen& Jiang, Zhi& Shao, Ping. 2017. Gastric Cancer Associated Genes Identified by an Integrative Analysis of Gene Expression Data. BioMed Research International،Vol. 2017, no. 2017, pp.1-7.
https://search.emarefa.net/detail/BIM-1138483
Modern Language Association (MLA)
Jiang, Bing…[et al.]. Gastric Cancer Associated Genes Identified by an Integrative Analysis of Gene Expression Data. BioMed Research International No. 2017 (2017), pp.1-7.
https://search.emarefa.net/detail/BIM-1138483
American Medical Association (AMA)
Jiang, Bing& Li, Shuwen& Jiang, Zhi& Shao, Ping. Gastric Cancer Associated Genes Identified by an Integrative Analysis of Gene Expression Data. BioMed Research International. 2017. Vol. 2017, no. 2017, pp.1-7.
https://search.emarefa.net/detail/BIM-1138483
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1138483