Guo Leilei, Song Chunhua, Wang Peng, Dai Liping, Zhang Jianying, Wang Kaijuan
Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan 450001, P.R. China.
Mol Med Rep. 2015 Nov;12(5):7139-45. doi: 10.3892/mmr.2015.4242. Epub 2015 Aug 24.
The aim of the present study was to explore key molecular pathways contributing to gastric cancer (GC) and to construct an interaction network between significant pathways and potential biomarkers. Publicly available gene expression profiles of GSE29272 for GC, and data for the corresponding normal tissue, were downloaded from Gene Expression Omnibus. Pre‑processing and differential analysis were performed with R statistical software packages, and a number of differentially expressed genes (DEGs) were obtained. A functional enrichment analysis was performed for all the DEGs with a BiNGO plug‑in in Cytoscape. Their correlation was analyzed in order to construct a network. The modularity analysis and pathway identification operations were used to identify graph clusters and associated pathways. The underlying molecular mechanisms involving these DEGs were also assessed by data mining. A total of 249 DEGs, which were markedly upregulated and downregulated, were identified. The extracellular region contained the most significantly over‑represented functional terms, with respect to upregulated and downregulated genes, and the closest topological matches were identified for taste transduction and regulation of autophagy. In addition, extracellular matrix‑receptor interactions were identified as the most relevant pathway associated with the progression of GC. The genes for fibronectin 1, secreted phosphoprotein 1, collagen type 4 variant α‑1/2 and thrombospondin 1, which are involved in the pathways, may be considered as potential therapeutic targets for GC. A series of associations between candidate genes and key pathways were also identified for GC, and their correlation may provide novel insights into the pathogenesis of GC.
本研究的目的是探索导致胃癌（GC）的关键分子途径，并构建重要途径与潜在生物标志物之间的相互作用网络。从基因表达综合数据库下载了公开可用的GC基因表达谱GSE29272以及相应正常组织的数据。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（DEG）。使用Cytoscape中的BiNGO插件对所有DEG进行功能富集分析。分析它们的相关性以构建网络。使用模块性分析和途径识别操作来识别图簇和相关途径。还通过数据挖掘评估了涉及这些DEG的潜在分子机制。共鉴定出249个明显上调和下调的DEG。就上调和下调基因而言，细胞外区域包含最显著过度表达的功能术语，并且鉴定出味觉转导和自噬调节的最接近拓扑匹配。此外，细胞外基质-受体相互作用被确定为与GC进展最相关的途径。参与这些途径的纤连蛋白1、分泌性磷蛋白1、4型胶原变体α-1/2和血小板反应蛋白1的基因可被视为GC的潜在治疗靶点。还为GC鉴定了一系列候选基因与关键途径之间的关联，它们的相关性可能为GC的发病机制提供新的见解。
