Dataset Information

Screening of key genes in gastric cancer with DNA microarray analysis.

ABSTRACT: BACKGROUND: The aim of this study was to identify key genes and novel potential therapeutic targets related to gastric cancer (GC) by comparing cancer tissue samples and healthy control samples using DNA microarray analysis. METHODS: Microarray data set GSE19804 was downloaded from Gene Expression Omnibus. Preprocessing and differential analysis were conducted with of R statistical software packages, and a number of differentially expressed genes (DEGs) were obtained. Cluster analysis was also done with gene expression values. Functional enrichment analysis was performed for all the DEGs with DAVID tools. The significantly up- and downregulated genes were selected out and their interactors were retrieved with STRING and HitPredict, followed by construction of networks. For all the genes in the two networks, GeneCodis was chosen for gene function annotation. RESULTS: A total of 638 DEGs were identified, and we found that SPP1 and FABP4 were the markedly up- and downregulated genes, respectively. Cell cycle and regulation of proliferation were the most significantly overrepresented functional terms in up- and downregulated genes. In addition, extracellular matrix-receptor interaction was found to be significant in the SPP1-included interaction network. CONCLUSIONS: A range of DEGs were obtained for GC. These genes not only provided insights into the pathogenesis of GC but also could develop into biomarkers for diagnosis or treatment.

SUBMITTER: Jin Y

PROVIDER: S-EPMC3852022 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Screening of key genes in gastric cancer with DNA microarray analysis.

Jin Yong Y Da Wei W

European journal of medical research 20131004

<h4>Background</h4>The aim of this study was to identify key genes and novel potential therapeutic targets related to gastric cancer (GC) by comparing cancer tissue samples and healthy control samples using DNA microarray analysis.<h4>Methods</h4>Microarray data set GSE19804 was downloaded from Gene Expression Omnibus. Preprocessing and differential analysis were conducted with of R statistical software packages, and a number of differentially expressed genes (DEGs) were obtained. Cluster analys ...[more]

PMID: 24093889

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Project description:BackgroundGastric cancer (GC) is one of the most common cancers all over the world, causing high mortality. Gastric cancer screening is one of the effective strategies used to reduce mortality. We expect that good biomarkers can be discovered to diagnose and treat gastric cancer as early as possible.MethodsWe download four gene expression profiling datasets of gastric cancer (GSE118916, GSE54129, GSE103236, GSE112369), which were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between gastric cancer and adjacent normal tissues were detected to explore biomarkers that may play an important role in gastric cancer. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of overlap genes were conducted by the Metascape online database; the protein-protein interaction (PPI) network was constructed by the STRING online database, and we screened the hub genes of the PPI network using the Cytoscape software. The survival curve analysis was conducted by km-plotter and the stage plots of hub genes were created by the GEPIA online database. PCR, WB, and immunohistochemistry were used to verify the expression of hub genes. A neural network model was established to quantify the predictors of gastric cancer.ResultsThe relative expression level of cadherin-3 (CDH3), lymphoid enhancer-binding factor 1 (LEF1), and matrix metallopeptidase 7 (MMP7) were significantly higher in gastric samples, compared with the normal groups (p<0.05). Receiver operator characteristic (ROC) curves were constructed to determine the effect of the three genes' expression on gastric cancer, and the AUC was used to determine the degree of confidence: CDH3 (AUC = 0.800, P<0.05, 95% CI =0.857-0.895), LEF1 (AUC=0.620, P<0.05, 95%CI=0.632-0.714), and MMP7 (AUC=0.914, P<0.05, 95%CI=0.714-0.947). The high-risk warning indicator of gastric cancer contained 8<CDH3<15 and 10<expression of LEF1<16.ConclusionsCDH3, LEF1, and MMP7 can be used as candidate biomarkers to construct a neural network model from hub genes, which may be helpful for the early diagnosis of gastric cancer.

Project description:Gastric cancer (GC) is one of the most common types of cancer worldwide. Patients must be identified at an early stage of tumor progression for treatment to be effective. The aim of the present study was to identify potential biomarkers with diagnostic value in patients with GC. To examine potential therapeutic targets for GC, four Gene Expression Omnibus (GEO) datasets were downloaded and screened for differentially expressed genes (DEGs). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were subsequently performed to study the function and pathway enrichment of the identified DEGs. A protein-protein interaction (PPI) network was constructed. The CytoHubba plugin of Cytoscape was used to calculate the degree of connectivity of proteins in the PPI network, and the two genes with the highest degree of connectivity were selected for further analysis. Additionally, the two DEGs with the largest and smallest log Fold Change values were selected. These six key genes were further examined using Oncomine and the Kaplan-Meier plotter platform. A total of 99 upregulated and 172 downregulated genes common to all four GEO datasets were screened. The DEGs were primarily enriched in the Biological Process terms: 'extracellular matrix organization', 'collagen catabolic process' and 'cell adhesion'. These three KEGG pathways were significantly enriched in the categories: 'ECM-receptor interaction', 'protein digestion and absorption', and 'focal adhesion'. Based on Oncomine, expression of ATP4A and ATP4B were downregulated in GC, whereas expression of the other genes were all upregulated. The Kaplan-Meier plotter platform confirmed that upregulated expression of the identified key genes was significantly associated with worse overall survival of patients with GC. The results of the present study suggest that FN1, COL1A1, INHBA and CST1 may be potential biomarkers and therapeutic targets for GC. Additional studies are required to explore the potential value of ATP4A and ATP4B in the treatment of GC.

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Screening of key genes in gastric cancer with DNA microarray analysis.

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Screening of key genes in gastric cancer with DNA microarray analysis.

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