Department of Urology, The Fourth Hospital of Hebei Medical University, No.12 Jiankang Road Shijiazhuang, 050011, Hebei Province, China.
Department of Oncology, Affiliated Xing Tai People Hospital of Hebei Medical University, Xingtai, 054001, Hebei Province, China.
Comput Biol Chem. 2021 Jun;92:107453. doi: 10.1016/j.compbiolchem.2021.107453. Epub 2021 Feb 9.
BACKGROUND: It is estimated that there are 338,000 new renal-cell carcinoma releases every year in the world. Renal cell carcinoma (RCC) is a heterogeneous tumor, of which more than 70% is clear cell renal cell carcinoma (ccRCC). It is estimated that about 30% of new renal-cell carcinoma patients have metastases at the time of diagnosis. However, the pathogenesis of renal clear cell carcinoma has not been elucidated. Therefore, it is necessary to further study the pathogenesis of ccRCC. METHODS: Two expression profiling datasets (GSE68417, GSE71963) were downloaded from the GEO database. Differentially expressed genes (DEGs) between ccRCC and normal tissue samples were identified by GEO2R. Functional enrichment analysis was made by the DAVID tool. Protein-protein interaction (PPI) network was constructed. The hub genes were excavated. The clustering analysis of expression level of hub genes was performed by UCSC (University of California Santa Cruz) Xena database. The hub gene on overall survival rate (OS) in patients with ccRCC was performed by Kaplan-Meier Plotter. Finally, we used the ccRCC renal tissue samples to verify the hub genes. RESULTS: 1182 common DEGs between the two datasets were identified. The results of GO and KEGG analysis revealed that variations in were predominantly enriched in intracellular signaling cascade, oxidation reduction, intrinsic to membrane, integral to membrane, nucleoside binding, purine nucleoside binding, pathways in cancer, focal adhesion, cell adhesion molecules. 10 hub genes ITGAX, CD86, LY86, TLR2, TYROBP, FCGR2A, FCGR2B, PTPRC, ITGB2, ITGAM were identified. FCGR2B and TYROBP were negatively correlated with the overall survival rate in patients with ccRCC (P < 0.05). RT-qPCR analysis showed that the relative expression levels of CD86, FCGR2A, FCGR2B, TYROBP, LY86, and TLR2 were significantly higher in ccRCC samples, compared with the adjacent renal tissue groups. CONCLUSIONS: In summary, bioinformatics technology could be a useful tool to predict the progression of ccRCC. In addition, there are DEGs between ccRCC tumor tissue and normal renal tissue, and these DEGs might be considered as biomarkers for ccRCC.
背景：据估计，全球每年有 33.8 万例新的肾细胞癌患者。肾细胞癌（RCC）是一种异质性肿瘤，其中超过 70%为透明细胞肾细胞癌（ccRCC）。据估计，约 30%的新肾细胞癌患者在诊断时已发生转移。然而，肾透明细胞癌的发病机制尚未阐明。因此，有必要进一步研究 ccRCC 的发病机制。
方法：从 GEO 数据库中下载了两个表达谱数据集（GSE68417、GSE71963）。使用 GEO2R 鉴定 ccRCC 与正常组织样本之间的差异表达基因（DEGs）。使用 DAVID 工具进行功能富集分析。构建蛋白质-蛋白质相互作用（PPI）网络。挖掘枢纽基因。通过 UCSC（加利福尼亚大学圣克鲁兹分校）Xena 数据库对枢纽基因的表达水平进行聚类分析。通过 Kaplan-Meier Plotter 分析 ccRCC 患者总体生存率（OS）中的枢纽基因。最后，我们使用 ccRCC 肾组织样本验证了枢纽基因。
结果：在两个数据集之间鉴定出 1182 个常见的 DEGs。GO 和 KEGG 分析的结果表明，变异主要富集于细胞内信号级联、氧化还原、内在膜、膜整合、核苷结合、嘌呤核苷结合、癌症途径、焦点粘附、细胞粘附分子。鉴定出 10 个枢纽基因 ITGAX、CD86、LY86、TLR2、TYROBP、FCGR2A、FCGR2B、PTPRC、ITGB2、ITGAM。FCGR2B 和 TYROBP 与 ccRCC 患者的总生存率呈负相关（P<0.05）。RT-qPCR 分析显示，与相邻肾组织组相比，ccRCC 样本中 CD86、FCGR2A、FCGR2B、TYROBP、LY86 和 TLR2 的相对表达水平显著升高。
结论：总之，生物信息学技术可以成为预测 ccRCC 进展的有用工具。此外，ccRCC 肿瘤组织与正常肾组织之间存在差异表达基因，这些差异表达基因可能被视为 ccRCC 的生物标志物。
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