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Identification of differentially-expressed genes in intestinal gastric cancer by microarray analysis.

ABSTRACT: Gastric cancer (GC) is one of the most frequent malignant tumors. In order to systematically characterize the cellular and molecular mechanisms of intestinal GC development, in this study, we used 22K oligonucleotide microarrays and bioinformatics analysis to evaluate the gene expression profiles of GC in 45 tissue samples, including 20 intestinal GC tissue samples, 20 normal appearing tissues (NATs) adjacent to tumors and 5 noncancerous gastric mucosa tissue samples. These profiles allowed us to explore the transcriptional characteristics of GC and determine the change patterns in gene expression that may be of clinical significance. 1519 and 1255 differentially-expressed genes (DEGs) were identified in intestinal GC tissues and NATs, respectively, as determined by Bayesian analysis (P<0.001). These genes were associated with diverse functions such as mucosa secretion, metabolism, proliferation, signaling and development, which occur at different stages of GC development.

SUBMITTER: Zang S

PROVIDER: S-EPMC4411479 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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Identification of differentially-expressed genes in intestinal gastric cancer by microarray analysis.

Zang Shizhu S Guo Ruifang R Xing Rui R Zhang Liang L Li Wenmei W Zhao Min M Fang Jingyuan J Hu Fulian F Kang Bin B Ren Yonghong Y Zhuang Yonglong Y Liu Siqi S Wang Rong R Li Xianghong X Yu Yingyan Y Cheng Jing J Lu Youyong Y

Genomics, proteomics & bioinformatics 20141211 6

Gastric cancer (GC) is one of the most frequent malignant tumors. In order to systematically characterize the cellular and molecular mechanisms of intestinal GC development, in this study, we used 22K oligonucleotide microarrays and bioinformatics analysis to evaluate the gene expression profiles of GC in 45 tissue samples, including 20 intestinal GC tissue samples, 20 normal appearing tissues (NATs) adjacent to tumors and 5 noncancerous gastric mucosa tissue samples. These profiles allowed us t ...[more]

PMID: 25500430

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Project description:BackgroundMicroarrays have been widely used for the analysis of gene expression and several commercial platforms are available. The combined use of multiple platforms can overcome the inherent biases of each approach, and may represent an alternative that is complementary to RT-PCR for identification of the more robust changes in gene expression profiles. In this paper, we combined statistical and functional analysis for the cross platform validation of two oligonucleotide-based technologies, Affymetrix (AFFX) and Applied Biosystems (ABI), and for the identification of differentially expressed genes.ResultsIn this study, we analysed differentially expressed genes after treatment of an ovarian carcinoma cell line with a cell cycle inhibitor. Treated versus control RNA was analysed for expression of 16425 genes represented on both platforms. We assessed reproducibility between replicates for each platform using CAT plots, and we found it high for both, with better scores for AFFX. We then applied integrative correlation analysis to assess reproducibility of gene expression patterns across studies, bypassing the need for normalizing expression measurements across platforms. We identified 930 genes as differentially expressed on AFFX and 908 on ABI, with approximately 80% common to both platforms. Despite the different absolute values, the range of intensities of the differentially expressed genes detected by each platform was similar. ABI showed a slightly higher dynamic range in FC values, which might be associated with its detection system. 62/66 genes identified as differentially expressed by Microarray were confirmed by RT-PCR.ConclusionIn this study we present a cross-platform validation of two oligonucleotide-based technologies, AFFX and ABI. We found good reproducibility between replicates, and showed that both platforms can be used to select differentially expressed genes with substantial agreement. Pathway analysis of the affected functions identified themes well in agreement with those expected for a cell cycle inhibitor, suggesting that this procedure is appropriate to facilitate the identification of biologically relevant signatures associated with compound treatment. The high rate of confirmation found for both common and platform-specific genes suggests that the combination of platforms may overcome biases related to probe design and technical features, thereby accelerating the identification of trustworthy differentially expressed genes.

Project description:Background: Gastric cancer (GC) has a high mortality rate in cancer-related deaths worldwide. Currently, the pathogenesis of gastric cancer progression remains unclear. Here, we identified several vital candidate genes related to gastric cancer development and revealed the potential pathogenic mechanisms using integrated bioinformatics analysis. Methods: Two microarray datasets from Gene Expression Omnibus (GEO) database integrated. Limma package was used to analyze differentially expressed genes (DEGs) between GC and matched normal specimens. DAVID was utilized to conduct Gene ontology (GO) and KEGG enrichment analysis. The relative expression of OLFM4, IGF2BP3, CLDN1 and MMP1were analyzed based on TCGA database provided by UALCAN. Western blot and quantitative real time PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines, respectively. Results: We downloaded the expression profiles of GSE103236 and GSE118897 from the Gene Expression Omnibus (GEO) database. Two integrated microarray datasets were used to obtain differentially expressed genes (DEGs), and bioinformatics methods were used for in-depth analysis. After gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments analysis, we identified 61 DEGs in common, of which the expression of 34 genes were elevated and 27 genes were decreased. GO analysis displayed that the biological functions of DEGs mainly focused on negative regulation of growth, fatty acid binding, cellular response to zinc ion and calcium-independent cell-cell adhesion. KEGG pathway analysis demonstrated that these DEGs mainly related to the Wnt and tumor signaling pathway. Interestingly, we found 4 genes were most significantly upregulated in the DEGs, which were OLFM4, IGF2BP3, CLDN1 and MMP1. Then, we confirmed the upregulation of these genes in STAD based on sample types. In the final, western blot and qRT-PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines. Conclusion: In our study, using integrated bioinformatics to screen DEGs in gastric cancer could benefit us for understanding the pathogenic mechanism underlying gastric cancer progression. Meanwhile, we also identified four significantly upregulated genes in DEGs from both two datasets, which might be used as the biomarkers for early diagnosis and prevention of gastric cancer.

Dataset Information

Identification of differentially-expressed genes in intestinal gastric cancer by microarray analysis.

Publications

Identification of differentially-expressed genes in intestinal gastric cancer by microarray analysis.

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