Time course analysis based on gene expression profile and identification of target molecules for colorectal cancer

Cancer cell international(2016)

引用 26|浏览11
暂无评分
摘要
Background The study aimed to investigate the expression changes of genes in colorectal cancer (CRC) and screen the potential molecular targets. Methods The GSE37178 of mRNA expression profile including the CRC samples extracted by surgical resection and the paired normal samples was downloaded from Gene Expression Omnibus database. The genes whose expressions were changed at four different time points were screened and clustered using Mfuzz package. Then DAVID was used to perform the functional and pathway enrichment analysis for genes in different clusters. The protein–protein interaction (PPI) networks were constructed for genes in the clusters according to the STRING database. Furthermore, the related-transcription factors (TFs) and microRNAs (miRNAs) were obtained based on the resources in databases and then were combined with the PPI networks in each cluster to construct the integrated network containing genes, TFs and miRNAs. Results As a result, 314 genes were clustered into four groups. Genes in cluster 1 and cluster 2 showed a decreasing trend, while genes in cluster 3 and cluster 4 presented an increasing trend. Then 18 TFs (e.g., TCF4, MEF2C and FOS) and 18 miRNAs (e.g., miR-382, miR-217, miR-1184, miR-326 and miR-330-5p) were identified and three integrated networks for cluster 1, 3, and 4 were constructed. Conclusions The results implied that expression of PITX2, VSNL1 , TCF4 , MEF2C and FOS are time-related and associated with CRC development, accompanied by several miRNAs including miR-382, miR-217, miR-21, miR-1184, miR-326 and miR-330-5p. All of them might be used as potential diagnostic or therapeutic target molecules for CRC.
更多
查看译文
关键词
Cluster,Colorectal cancer,MicroRNA,Target,Transcription factor
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要