Huang Yi-Dong, Shan Wei, Zeng Li, Wu Yang
Department of Pediatric Surgery, West China Hospital of Sichuan University, Chengdu, China E-mail :
Asian Pac J Cancer Prev. 2013;14(8):4553-7. doi: 10.7314/apjcp.2013.14.8.4553.
The purpose of this study was to identify genes related to bladder cancer with samples from normal and disease cases by microarray chip.
After downloading the gene expression profile GSE3167 from Gene Expression Omnibus database which includes 50 bladder samples, comprising 9 normal and 41 disease samples, differentially expressed genes were identified with packages in R language. The selected differentially expressed genes were further analyzed using bioinformatics methods. Firstly, molecular functions, biological processes and cell component analysis were researched by software Gestalt. Then, software String was used to search interaction relationships among differentially expressed genes, and hub genes of the network were selected. Finally, by using plugins of software Cytoscape, Mcode and Bingo, module analysis of hub-genes was performed.
A total of 221 genes were identified as differentially expressed by comparing normal and disease bladder samples, and a network as well as the hub gene C1QBP was obtained from the network. The C1QBP module had the closest relationship to production of molecular mediators involved in inflammatory responses.
We obtained differentially expressed genes of bladder cancer by microarray, and both PRDX2 and YWHAZ in the module with hub gene C1QBP were most significantly related to production of molecular mediators involved in inflammatory responses. From knowledge of inflammatory responses and cancer, our results showed that, the hub gene and its module could induce inflammation in bladder cancer. These related genes are candidate bio-markers for bladder cancer diagnosis and might be helpful in designing novel therapies.
本研究旨在通过微阵列芯片,利用正常和疾病样本鉴定与膀胱癌相关的基因。
从基因表达综合数据库下载基因表达谱GSE3167,其中包括50个膀胱样本,9个正常样本和41个疾病样本,使用R语言中的软件包鉴定差异表达基因。对选定的差异表达基因进一步采用生物信息学方法进行分析。首先,通过Gestalt软件研究分子功能、生物学过程和细胞成分分析。然后,使用String软件搜索差异表达基因之间的相互作用关系,并选择网络中的枢纽基因。最后,利用Cytoscape软件的插件Mcode和Bingo对枢纽基因进行模块分析。
通过比较正常和疾病膀胱样本,共鉴定出221个差异表达基因,并从网络中获得一个网络以及枢纽基因C1QBP。C1QBP模块与参与炎症反应的分子介质的产生关系最为密切。
我们通过微阵列获得了膀胱癌的差异表达基因,并且在与枢纽基因C1QBP相关的模块中,PRDX2和YWHAZ与参与炎症反应的分子介质的产生最为显著相关。从炎症反应和癌症的知识来看,我们的结果表明,枢纽基因及其模块可在膀胱癌中诱导炎症。这些相关基因是膀胱癌诊断的候选生物标志物,可能有助于设计新的治疗方法。