Wang Congcong, Guo Jianping, Zhao Xiaoyang, Jia Jia, Xu Wenting, Wan Peng, Sun Changgang
Clinical Medical College, Cheeloo College of Medicine, Shandong University, Jinan 250100, Shandong, China.
Department of Oncology, Zibo Maternal and Children Hospital, Zibo 255000, Shandong, China.
Iran J Public Health. 2021 Nov;50(11):2238-2245. doi: 10.18502/ijph.v50i11.7578.
To address the biomarkers that correlated with the prognosis of patients with PDCA using bioinformatics analysis.
The raw data of genes were obtained from the Gene Expression Omnibus. We screened differently expressed genes (DEGs) by Rstudio. Database for Annotation, Visualization and Intergrated Discovery was used to investigate their biological function by Gene Ontology(GO) and Kyoto Encyclopedia of Genes (KEGG) analysis. Protein-protein interaction of these DEGs were analyzed based on the Search Tool for the Retrieval of Interacting Genes database (STRING) and visualized by Cytoscape. Genes calculated by Cyto-Hubba with degree >10 were identified as hub genes. Then, the identified hub genes were verified by UALCAN online analysis tool to evaluate the prognostic value in PDCA.
Three expression profiles (GSE15471, GSE16515 and GSE32676) were downloaded from GEO database. The three sets of DEGs exhibited an intersection consisting of 223 genes (214 upregulated DEGs and 9 downregulated DEGs). GO analysis showed that the 223 DEGs were significantly enriched in extracellular exosome, plasma membrane and extracellular space. ECM-receptor interaction, PI3K-Akt signaling pathway and Focal adhesion were the most significantly enriched pathway according to KEGG analysis. By combining the results of Cytohubba, 30 hub genes with a high degree of connectivity were picked out. Finally, we candidated 3 biomarkers by UALCAN online survival analysis, including CEP55, ANLN and PRC1.
we identified CEP55, ANLN and PRC1 may be the potential biomarkers and therapeutic targets of PDCA, which used for prognostic assessment and scheme selection.
利用生物信息学分析确定与胰腺导管腺癌（PDCA）患者预后相关的生物标志物。
基因原始数据来自基因表达综合数据库（Gene Expression Omnibus）。我们通过Rstudio筛选差异表达基因（DEGs）。利用注释、可视化和综合发现数据库（Database for Annotation, Visualization and Intergrated Discovery），通过基因本体论（GO）和京都基因与基因组百科全书（KEGG）分析来研究其生物学功能。基于相互作用基因检索工具数据库（Search Tool for the Retrieval of Interacting Genes database, STRING）分析这些DEGs的蛋白质-蛋白质相互作用，并通过Cytoscape进行可视化。通过Cyto-Hubba计算得出度>10的基因被鉴定为枢纽基因。然后，通过UALCAN在线分析工具验证所鉴定的枢纽基因，以评估其在PDCA中的预后价值。
从基因表达综合数据库（GEO）下载了三个表达谱（GSE15471、GSE16515和GSE32676）。这三组DEGs呈现出一个由223个基因组成的交集（214个上调DEGs和9个下调DEGs）。GO分析表明，这223个DEGs在细胞外囊泡、质膜和细胞外空间中显著富集。根据KEGG分析，细胞外基质-受体相互作用、PI3K-Akt信号通路和粘着斑是最显著富集的通路。结合Cytohubba的结果，挑选出30个具有高度连通性的枢纽基因。最后，通过UALCAN在线生存分析确定了3个生物标志物，包括CEP55、ANLN和PRC1。
我们确定CEP55、ANLN和PRC1可能是PDCA的潜在生物标志物和治疗靶点，可用于预后评估和治疗方案选择。
