Gastric Adenocarcinoma: A Multimodal Approach.
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ABSTRACT:
SUBMITTER: Quadri HS
PROVIDER: S-EPMC5540948 | biostudies-other | 2017
REPOSITORIES: biostudies-other
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Project description:In the present study, gene expression profiles were analyzed to identify the molecular mechanisms underlying gastric cardia adenocarcinoma (GCA) and gastric non-cardia adenocarcinoma (GNCA). A gene expression dataset (accession number GSE29272) was downloaded from Gene Expression Omnibus, and consisted of 62 GCA samples and 62 normal controls, as well as 72 GNCA samples and 72 normal controls. The two groups of differentially-expressed genes (DEGs) were compared to obtain common and unique DEGs. A differential analysis was performed using the Linear Models for Microarray Data package in R. Functional enrichment analysis was conducted for the DEGs using the Database for Annotation, Visualization and Integrated Discovery. Protein-protein interaction (PPI) networks were constructed for the DEGs with information from the Search Tool for the Retrieval of Interacting Genes. Subnetworks were extracted from the whole network with Cytoscape. Compared with the control, 284 and 268 genes were differentially-expressed in GCA and GNCA, respectively, of which 194 DEGs were common between GCA and GNCA. Common DEGs [e.g., claudin (CLDN)7, CLDN4 and CLDN3] were associated with cell adhesion and digestion. GCA-unique DEGs [e.g., MAD1 mitotic arrest deficient like 1, cyclin (CCN)B1, CCNB2 and CCNE1] were associated with the cell cycle and the regulation of cell proliferation, while GNCA-unique DEGs (e.g., GATA binding protein 6 and hyaluronoglucosaminidase 1) were implicated in cell death. A PPI network with 141 nodes and 446 edges were obtained, from which two subnetworks were extracted. Genes [e.g., fibronectin 1, collagen type I ?2 chain (COL1A2) and COL1A1] from the two subnetworks were implicated in extracellular matrix organization. These common DEGs could advance our understanding of the etiology of gastric cancer, while the unique DEGs in GCA and GNCA could better define the properties of specific cancers and provide potential biomarkers for diagnosis, prognosis or therapy.
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