Bao Lin, Zhao Ye, Liu ChenChen, Cao Qi, Huang Yu, Chen Ke, Song Zhengshuai
Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
J Cancer. 2020 Jan 16;11(7):1712-1726. doi: 10.7150/jca.38379. eCollection 2020.
To investigate the potential mechanisms contributing to metastasis of clear cell renal cell carcinoma (ccRCC), screen the hub genes, associated pathways of metastatic ccRCC and identify potential biomarkers. The ccRCC metastasis gene expression profile GSE47352 was employed to analyze the differentially expressed genes (DEGs). DAVID was performed to assess Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The protein-protein interaction (PPI) network and modules were constructed. The function pathway, prognostic and diagnostic analysis of these hub genes was picked out to estimate their potential effects on metastasis of ccRCC. A total of 873 DEGs were identified (503 upregulated genes and 370 downregulated genes). Meanwhile, top 20 hub genes were displayed. GO analysis showed that the top 20 hub genes were enriched in regulation of phosphatidylinositol 3-kinase signaling, positive regulation of DNA replication, protein autophosphorylation, protein tyrosine kinase activity, etc. KEGG analysis indicated these hub genes were enriched in the Ras signaling pathway, PI3K-Akt signaling pathway, HIF-1 signaling pathway, Pathways in cancer, etc. The GO and KEGG enrichment analyses for the hub genes disclosed important biological features of metastatic ccRCC. PPI network showed the interaction of top 20 hub genes. Gene Set Enrichment Analysis (GSEA) revealed that some of the hub genes was associated with metastasis, epithelial mesenchymal transition (EMT), hypoxia cancer and adipogenesis of ccRCC. Some top hub genes were distinctive and new discoveries compared with that of the existing associated researches. Our analysis uncovered that changes in signal pathways such as Ras signaling pathway, PI3K-Akt signaling pathway, etc. may be the main signatures of metastatic ccRCC. We identified several candidate biomarkers related with overall survival (OS) and disease-free survival (DFS) of ccRCC patients. Accordingly, they might be novel therapeutic targets and used as potential biomarkers for diagnosis, prognosis of ccRCC.
为了研究促成透明细胞肾细胞癌(ccRCC)转移的潜在机制,筛选枢纽基因、转移性ccRCC的相关通路并鉴定潜在生物标志物。采用ccRCC转移基因表达谱GSE47352分析差异表达基因(DEG)。利用DAVID进行基因本体论(GO)和京都基因与基因组百科全书(KEGG)分析。构建蛋白质-蛋白质相互作用(PPI)网络和模块。挑选出这些枢纽基因的功能通路、预后和诊断分析,以评估它们对ccRCC转移的潜在影响。共鉴定出873个DEG(503个上调基因和370个下调基因)。同时,展示了前20个枢纽基因。GO分析表明,前20个枢纽基因富集于磷脂酰肌醇3-激酶信号传导调控、DNA复制的正调控、蛋白质自磷酸化、蛋白质酪氨酸激酶活性等。KEGG分析表明这些枢纽基因富集于Ras信号通路、PI3K-Akt信号通路、HIF-1信号通路、癌症通路等。对枢纽基因的GO和KEGG富集分析揭示了转移性ccRCC的重要生物学特征。PPI网络显示了前20个枢纽基因的相互作用。基因集富集分析(GSEA)表明,一些枢纽基因与ccRCC的转移、上皮-间质转化(EMT)、缺氧癌症和脂肪生成有关。与现有相关研究相比,一些顶级枢纽基因具有独特性和新发现。我们的分析发现,Ras信号通路、PI3K-Akt信号通路等信号通路的变化可能是转移性ccRCC的主要特征。我们鉴定了几个与ccRCC患者总生存期(OS)和无病生存期(DFS)相关的候选生物标志物。因此,它们可能是新的治疗靶点,并用作ccRCC诊断、预后的潜在生物标志物。