Zhang Zong-Xin, Shen Cui-Fen, Zou Wei-Hua, Shou Li-Hong, Zhang Hui-Ying, Jin Wen-Jun
Department of Laboratory, Huzhou Central Hospital, Huzhou, Zhejiang, China.
Asian Pac J Cancer Prev. 2013;14(3):1731-5. doi: 10.7314/apjcp.2013.14.3.1731.
We aimed to identify key genes, pathways and function modules in the development of diffuse large B-cell lymphoma (DLBCL) with microarray data and interaction network analysis.
Microarray data sets for 7 DLBCL samples and 7 normal controls was downloaded from the Gene Expression Omnibus (GEO) database and differentially expressed genes (DEGs) were identified with Student's t-test. KEGG functional enrichment analysis was performed to uncover their biological functions. Three global networks were established for immune system, signaling molecules and interactions and cancer genes. The DEGs were compared with the networks to observe their distributions and determine important key genes, pathways and modules.
A total of 945 DEGs were obtained, 272 up-regulated and 673 down-regulated. KEGG analysis revealed that two groups of pathways were significantly enriched: immune function and signaling molecules and interactions. Following interaction network analysis further confirmed the association of DEGs in immune system, signaling molecules and interactions and cancer genes.
Our study could systemically characterize gene expression changes in DLBCL with microarray technology. A range of key genes, pathways and function modules were revealed. Utility in diagnosis and treatment may be expected with further focused research.
我们旨在通过微阵列数据和相互作用网络分析,确定弥漫性大B细胞淋巴瘤(DLBCL)发展过程中的关键基因、信号通路和功能模块。
从基因表达综合数据库(GEO)下载7个DLBCL样本和7个正常对照的微阵列数据集,采用学生t检验鉴定差异表达基因(DEG)。进行KEGG功能富集分析以揭示其生物学功能。建立了免疫系统、信号分子与相互作用以及癌症基因的三个全局网络。将DEG与这些网络进行比较,观察它们的分布并确定重要的关键基因、信号通路和模块。
共获得945个DEG,其中272个上调,673个下调。KEGG分析显示两组信号通路显著富集:免疫功能以及信号分子与相互作用。相互作用网络分析进一步证实了DEG在免疫系统、信号分子与相互作用以及癌症基因中的关联。
我们的研究可以利用微阵列技术系统地表征DLBCL中的基因表达变化。揭示了一系列关键基因、信号通路和功能模块。进一步深入研究有望在诊断和治疗中发挥作用。