Wang Zengzeng, Zhang Zhihong, Zhang Changwen, Xu Yong
Department of Urology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin 300211, P.R. China.
Department of Urology, Tianjin Beichen Hospital, Tianjin 300400, P.R. China.
Oncol Lett. 2018 Jun;15(6):8491-8499. doi: 10.3892/ol.2018.8398. Epub 2018 Mar 30.
The purpose of the present study was to screen potential pathogenic biomarkers of clear cell renal cell carcinoma (ccRCC) via microarray analysis. The mRNA and microRNA (miRNA) expression profiles of GSE96574 and GSE71302 were downloaded from the Gene Expression Omnibus (GEO) database, as well as the methylation profile of GSE61441. A total of 5 ccRCC tissue samples and 5 normal kidney tissue samples were contained in each profile of GSE96574 and GSE71302, and 46 ccRCC tissue samples and 46 normal kidney tissue samples were involved in GSE61441. The differentially expressed genes (DEGs) and the differentially expressed miRNAs (DEMs) were obtained via limma package in ccRCC tissues compared with normal kidney tissues. The Two Sample t-test and the Beta distribution test were used to identify the differentially methylated sites (DMSs). The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to perform the Gene Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs. The targets of the DEMs were screened with the miRWalk database, and the further combination analyses of DEGs, DEMs and DMSs were conducted. Additionally, reverse transcription PCR (RT-PCR) and methylation-specific PCR (MS-PCR) were performed to detect the mRNA level and methylation status of . The mRNA levels of and were tested by RT-PCR. A total of 2,172 DEGs, 202 DEMs and 2,172 DMSs were identified in RCC samples compared with normal samples. The DEGs were enriched in 1,015 GO terms and 69 KEGG pathways. A total of 10,601 miRNA-gene pairs were identified in at least 5 algorithms of the miRWalk database. A total of 143 overlaps were identified between the DEGs and the differentially methylated genes. Furthermore, the DEGs were involved in 851 miRNA-gene pairs, including 127 pairs in which the target genes were negatively associated with their corresponding DEMs and DMSs. was lowly expressed and highly methylated in ccRCC tissues, while and were highly expressed. The results of the present study indicated that and may be involved in the pathogenesis of ccRCC.
本研究的目的是通过微阵列分析筛选透明细胞肾细胞癌(ccRCC)的潜在致病生物标志物。从基因表达综合数据库(GEO)下载了GSE96574和GSE71302的mRNA和微小RNA(miRNA)表达谱,以及GSE61441的甲基化谱。GSE96574和GSE71302的每个数据集中包含5个ccRCC组织样本和5个正常肾组织样本,GSE61441中涉及46个ccRCC组织样本和46个正常肾组织样本。与正常肾组织相比,通过limma软件包在ccRCC组织中获得差异表达基因(DEG)和差异表达miRNA(DEM)。采用双样本t检验和贝塔分布检验来识别差异甲基化位点(DMS)。利用注释、可视化和综合发现数据库(DAVID)对DEG进行基因本体(GO)术语和京都基因与基因组百科全书(KEGG)通路富集分析。用miRWalk数据库筛选DEM的靶标,并对DEG、DEM和DMS进行进一步的联合分析。此外,进行逆转录PCR(RT-PCR)和甲基化特异性PCR(MS-PCR)检测……的mRNA水平和甲基化状态。通过RT-PCR检测……和……的mRNA水平。与正常样本相比,在RCC样本中鉴定出2172个DEG、202个DEM和2172个DMS。这些DEG在1015个GO术语和69条KEGG通路中富集。在miRWalk数据库的至少5种算法中鉴定出总共10601个miRNA-基因对。在DEG和差异甲基化基因之间总共鉴定出143个重叠。此外,DEG参与了851个miRNA-基因对,其中127对靶基因与其相应的DEM和DMS呈负相关。……在ccRCC组织中低表达且高度甲基化,而……和……高表达。本研究结果表明……和……可能参与ccRCC的发病机制。