Jin Dandan, Jiao Yujie, Ji Jie, Jiang Wei, Ni Wenkai, Wu Yingcheng, Ni Runzhou, Lu Cuihua, Qu Lishuai, Ni Hongbing, Liu Jinxia, Xu Weisong, Xiao MingBing
Department of Gastroenterology, Affiliated Hospital of Nantong University, Nantong, China.
Clinical Medicine, Medical College, Nantong University, Nantong, China.
PeerJ. 2020 Jun 15;8:e9301. doi: 10.7717/peerj.9301. eCollection 2020.
Pancreatic cancer is one of the most common malignant cancers worldwide. Currently, the pathogenesis of pancreatic cancer remains unclear; thus, it is necessary to explore its precise molecular mechanisms.
To identify candidate genes involved in the tumorigenesis and proliferation of pancreatic cancer, the microarray datasets GSE32676, GSE15471 and GSE71989 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between Pancreatic ductal adenocarcinoma (PDAC) and nonmalignant samples were screened by GEO2R. The Database for Annotation Visualization and Integrated Discovery (DAVID) online tool was used to obtain a synthetic set of functional annotation information for the DEGs. A PPI network of the DEGs was established using the Search Tool for the Retrieval of Interacting Genes (STRING) database, and a combination of more than 0.4 was considered statistically significant for the PPI. Subsequently, we visualized the PPI network using Cytoscape. Functional module analysis was then performed using Molecular Complex Detection (MCODE). Genes with a degree ≥10 were chosen as hub genes, and pathways of the hub genes were visualized using ClueGO and CluePedia. Additionally, GenCLiP 2.0 was used to explore interactions of hub genes. The Literature Mining Gene Networks module was applied to explore the cocitation of hub genes. The Cytoscape plugin iRegulon was employed to analyze transcription factors regulating the hub genes. Furthermore, the expression levels of the 13 hub genes in pancreatic cancer tissues and normal samples were validated using the Gene Expression Profiling Interactive Analysis (GEPIA) platform. Moreover, overall survival and disease-free survival analyses according to the expression of hub genes were performed using Kaplan-Meier curve analysis in the cBioPortal online platform. The relationship between expression level and tumor grade was analyzed using the online database Oncomine. Lastly, the eight snap-frozen tumorous and adjacent noncancerous adjacent tissues of pancreatic cancer patients used to detect the CDK1 and CEP55 protein levels by western blot.
Altogether, the DEGs and hub genes identified in this work can help uncover the molecular mechanisms underlying the tumorigenesis of pancreatic cancer and provide potential targets for the diagnosis and treatment of this disease.
胰腺癌是全球最常见的恶性肿瘤之一。目前，胰腺癌的发病机制仍不清楚；因此，有必要探索其精确的分子机制。
为了鉴定参与胰腺癌发生和增殖的候选基因，从基因表达综合数据库（GEO）下载了微阵列数据集GSE32676、GSE15471和GSE71989。通过GEO2R筛选胰腺导管腺癌（PDAC）与非恶性样本之间的差异表达基因（DEG）。利用在线工具注释可视化与综合发现数据库（DAVID）获取DEG的一组综合功能注释信息。使用相互作用基因检索工具（STRING）数据库建立DEG的蛋白质-蛋白质相互作用（PPI）网络，PPI中大于0.4的组合被认为具有统计学意义。随后，我们使用Cytoscape可视化PPI网络。然后使用分子复合物检测（MCODE）进行功能模块分析。选择度≥10的基因作为枢纽基因，并使用ClueGO和CluePedia可视化枢纽基因的通路。此外，使用GenCLiP 2.0探索枢纽基因的相互作用。应用文献挖掘基因网络模块探索枢纽基因的共引情况。使用Cytoscape插件iRegulon分析调控枢纽基因的转录因子。此外，利用基因表达谱交互式分析（GEPIA）平台验证13个枢纽基因在胰腺癌组织和正常样本中的表达水平。此外，在cBioPortal在线平台上使用Kaplan-Meier曲线分析根据枢纽基因的表达进行总生存和无病生存分析。使用在线数据库Oncomine分析表达水平与肿瘤分级之间的关系。最后，收集8例胰腺癌患者的速冻肿瘤组织和相邻非癌旁组织，通过蛋白质免疫印迹法检测细胞周期蛋白依赖性激酶1（CDK1）和中心体蛋白55（CEP55）的蛋白水平。
总之，本研究中鉴定的DEG和枢纽基因有助于揭示胰腺癌发生的分子机制，并为该疾病的诊断和治疗提供潜在靶点。
