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DNA 微阵列分析筛选胃癌关键基因。

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

机构信息

Department of gastroenterology, The 6th People's Hospital affiliated to Shanghai Jiaotong University, No, 600 Yishan Road, Shanghai 200233, China.

出版信息

Eur J Med Res. 2013 Oct 4;18(1):37. doi: 10.1186/2047-783X-18-37.

DOI:10.1186/2047-783X-18-37
PMID:24093889
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3852022/
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.

摘要

背景

本研究旨在通过 DNA 微阵列分析比较癌组织样本和健康对照样本,鉴定与胃癌(GC)相关的关键基因和新的潜在治疗靶点。

方法

从基因表达综合数据库中下载微阵列数据集 GSE19804。使用 R 统计软件包进行预处理和差异分析,获得了一些差异表达基因(DEGs)。使用基因表达值进行聚类分析。使用 DAVID 工具对所有 DEGs 进行功能富集分析。选择显著上调和下调的基因,并使用 STRING 和 HitPredict 检索其相互作用物,然后构建网络。对于两个网络中的所有基因,选择 GeneCodis 进行基因功能注释。

结果

共鉴定出 638 个 DEGs,我们发现 SPP1 和 FABP4 分别是明显上调和下调的基因。细胞周期和增殖调节是上调和下调基因中最显著的功能术语。此外,在 SPP1 包含的相互作用网络中发现细胞外基质-受体相互作用是显著的。

结论

获得了一系列与 GC 相关的 DEGs。这些基因不仅为 GC 的发病机制提供了深入的了解,而且可能成为诊断或治疗的生物标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/790e/3852022/a092c95f0d29/2047-783X-18-37-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/790e/3852022/94c2dcaafc56/2047-783X-18-37-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/790e/3852022/cbef504dd406/2047-783X-18-37-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/790e/3852022/a092c95f0d29/2047-783X-18-37-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/790e/3852022/94c2dcaafc56/2047-783X-18-37-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/790e/3852022/cbef504dd406/2047-783X-18-37-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/790e/3852022/a092c95f0d29/2047-783X-18-37-3.jpg

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