Zhao Rui, Wang Yao, Zhang Muchun, Gu Xinquan, Wang Weihua, Tan Jiufeng, Wei Xin, Jin Ning
Department of Urology, China-Japan Union Hospital of Jilin University, Changchun, Jilin 130033, P.R. China.
Oncol Lett. 2017 Nov;14(5):5361-5369. doi: 10.3892/ol.2017.6879. Epub 2017 Sep 4.
The aim of the present study was to analyze potential therapy targets for prostate cancer using integrated analysis of two gene expression profiles. First, gene expression profiles GSE38241 and GSE3933 were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between prostate cancer and normal control samples were identified using the Linear Models for Microarray Data package. Pathway enrichment analysis of DEGs was performed using Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes. Furthermore, protein-protein interaction (PPI) networks of DEGs were constructed, on the basis of the Search Tool for the Retrieval of Interacting Genes/Proteins database. The Molecular Complex Detection was utilized to perform module analysis of the PPI networks. In addition, transcriptional regulatory networks were constructed on the basis of the associations between transcription factors (TFs) and target genes. A total of 529 DEGs were identified, including 129 upregulated genes that were primarily associated with to the cell cycle. Additionally, 400 downregulated genes were identified, which were principally enriched in the pathways associated with vascular smooth muscle contraction and focal adhesion. Cell Division Cycle Associated 8, Cell Division Cycle 45, Ubiquitin Conjugating Enzyme E2 C and Thymidine Kinase 1 were identified as hub genes in the upregulated sub-network. Furthermore, the upregulated TF , and the downregulated TF Early Growth Response 1, were identified to be critical in the transcriptional regulatory networks. The identified DEGs and TFs may have critical roles in the progression of prostate cancer, and may be used as target molecules for treating prostate cancer.
本研究的目的是通过对两个基因表达谱进行综合分析,来分析前列腺癌的潜在治疗靶点。首先,从基因表达综合数据库下载基因表达谱GSE38241和GSE3933。使用微阵列数据线性模型软件包鉴定前列腺癌与正常对照样本之间的差异表达基因(DEG)。使用基因本体论和京都基因与基因组百科全书对DEG进行通路富集分析。此外,基于相互作用基因/蛋白质检索工具数据库构建DEG的蛋白质-蛋白质相互作用(PPI)网络。利用分子复合物检测对PPI网络进行模块分析。另外,基于转录因子(TF)与靶基因之间的关联构建转录调控网络。共鉴定出529个DEG,包括129个上调基因,这些基因主要与细胞周期相关。此外,鉴定出400个下调基因,这些基因主要富集于与血管平滑肌收缩和粘着斑相关的通路中。细胞分裂周期相关蛋白8、细胞分裂周期蛋白45、泛素结合酶E2 C和胸苷激酶1被鉴定为上调子网络中的枢纽基因。此外,上调的TF和下调的TF早期生长反应因子1在转录调控网络中被鉴定为关键因子。所鉴定的DEG和TF可能在前列腺癌进展中起关键作用,并可作为治疗前列腺癌的靶分子。