Department of Urology, Zhuzhou Central Hospital, Zhuzhou 412000, China.
Department of Urology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang 421001, China.
Dis Markers. 2022 May 24;2022:4761803. doi: 10.1155/2022/4761803. eCollection 2022.
Due to a lack of knowledge of the disease process, papillary renal cell carcinoma (PRCC) has a dismal outlook. This research was aimed at uncovering the possible biomarkers and the underlying principles in PRCC using a bioinformatics method.
We searched the Gene Expression Omnibus (GEO) datasets to obtain the GSE11151 and GSE15641 gene expression profiles of PRCC. We used the R package limma to identify the differentially expressed genes (DEGs). The online tool DAVID and ClusterProfiler package in R software were used to analyze Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway dominance, respectively. The STRING database was utilized to construct the PPI network of DEGs. Using the Cytoscape technology, a protein-protein interaction (PPI) network that associated with DEGs was created, and the hub genes were identified using the Cytoscape plug-in CytoHubba. The hub genes were subjected to a Kaplan-Meier analysis to identify their correlations with survival rates.
From the selected datasets, a total of 240 common DEGs were identified in the PRCC, including 50 upregulated genes and 190 downregulated regulated genes. Renal growth, external exosome, binding of heparin, and metabolic processes were all substantially associated with DEGs. The CytoHubba plug-in-based analysis identified the 10 hub genes (, , , , , , , , , and ) from the original PPI network. The higher expression group of was associated with poor outcome in patients with PRCC.
We revealed important genes and proposed biological pathways that may be implicated in the formation of PRCC. EGF might be a predictive biomarker for PRCC and therefore should be investigated as a novel treatment strategy.
由于对疾病过程缺乏了解，乳头状肾细胞癌（PRCC）的预后较差。本研究旨在通过生物信息学方法揭示 PRCC 可能的生物标志物和潜在机制。
我们在基因表达综合数据库（GEO）中搜索，以获取 PRCC 的 GSE11151 和 GSE15641 基因表达谱。我们使用 R 包 limma 来识别差异表达基因（DEGs）。在线工具 DAVID 和 R 软件中的 ClusterProfiler 包分别用于分析基因本体论和京都基因与基因组百科全书（KEGG）通路优势。STRING 数据库用于构建 DEGs 的蛋白质-蛋白质相互作用（PPI）网络。使用 Cytoscape 技术，创建了一个与 DEGs 相关的 PPI 网络，并用 Cytoscape 插件 CytoHubba 识别了枢纽基因。对枢纽基因进行 Kaplan-Meier 分析，以确定它们与存活率的相关性。
从选定的数据集共鉴定出 240 个在 PRCC 中共同的 DEGs，包括 50 个上调基因和 190 个下调基因。肾生长、外泌体、肝素结合和代谢过程均与 DEGs 密切相关。基于 CytoHubba 插件的分析从原始 PPI 网络中识别出 10 个枢纽基因（、、、、、、、、和）。在 PRCC 患者中，高表达的基因与不良预后相关。
我们揭示了可能参与 PRCC 形成的重要基因和提出的生物学途径。EGF 可能是 PRCC 的预测生物标志物，因此应作为一种新的治疗策略进行研究。
