Zhang Ning, Chen Wenxin, Gan Zhilu, Abudurexiti Alimujiang, Hu Xiaogang, Sang Wei
Surgery Department of Urology, The Third People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang.
The Department of Pathology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, PR China.
Medicine (Baltimore). 2020 May 22;99(21):e20470. doi: 10.1097/MD.0000000000020470.
Clear cell renal cell carcinoma (ccRCC) is the most common subtype among renal cancer, and more and more researches find that the occurrence of ccRCC is associated with genetic changes, but the molecular mechanism still remains unclear. The present study aimed to identify aggregation trend of differentially expressed genes (DEGs) in ccRCC, which would be beneficial to the treatment of ccRCC and provide research ideas using a series of bioinformatics approach. Gene ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) analysis were used to get the enrichment trend of DEGs of GSE53757 and GSE16449. Draw Venn Diagram was applied for co-expression of DEGs. Cytoscape with the Retrieval of Interacting Gene (STRING) datasets and Molecular Complex Detection (MCODE) were performed protein-protein interaction (PPI) of DEGs. The Kaplan-Meier Plotter analysis of top 15 upregulated and top 15 downregulated were selected in Gene Expression Profiling Interactive Analysis (GEPIA). Then, the expression level of hub genes between normal renal tissue and different pathological stages of ccRCC tissue, which significantly correlated with overall survival in ccRCC patients, were also analyzed by Ualcan based on The Cancer Genome Atlas (TCGA) database. In this study, we got 167 co-expression DEGs, including 72 upregulated DEGs and 95 downregulated DEGs. We identified 11 hub genes had significantly correlated with overall survival in ccRCC patients. Among them, KIF23, APLN, ADCY1, GREB1, TLR4, IRF8, CXCL1, CXCL2, deserved our attention.
透明细胞肾细胞癌（ccRCC）是肾癌中最常见的亚型，越来越多的研究发现ccRCC的发生与基因变化有关，但其分子机制仍不清楚。本研究旨在确定ccRCC中差异表达基因（DEGs）的聚集趋势，这将有助于ccRCC的治疗，并使用一系列生物信息学方法提供研究思路。基因本体论（GO）和京都基因与基因组百科全书（KEGG）分析用于获取GSE53757和GSE16449的DEGs富集趋势。绘制韦恩图用于DEGs的共表达。使用基因相互作用检索（STRING）数据集和分子复合物检测（MCODE）的Cytoscape软件对DEGs进行蛋白质-蛋白质相互作用（PPI）分析。在基因表达谱交互式分析（GEPIA）中对上调前15位和下调前15位的基因进行Kaplan-Meier Plotter分析。然后，基于癌症基因组图谱（TCGA）数据库，通过Ualcan分析ccRCC患者正常肾组织与不同病理阶段ccRCC组织中与总生存期显著相关的枢纽基因的表达水平。在本研究中，我们获得了167个共表达DEGs，包括72个上调DEGs和95个下调DEGs。我们鉴定出11个与ccRCC患者总生存期显著相关的枢纽基因。其中，KIF23、APLN、ADCY1、GREB1、TLR4 IRF8、CXCL1、CXCL2值得关注。
