Zhang Junfeng, Fu Xueliang, Liu Dejun, Yang Minwei, Yang Jianyu, Huo Yanmiao, Liu Wei, Hua Rong, Sun Yongwei, Wang Jian
Department of Biliary-Pancreatic Surgery, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, P.R. China.
Oncol Lett. 2020 Oct;20(4):5. doi: 10.3892/ol.2020.11866. Epub 2020 Jul 15.
Perineural invasion (PNI) is a prominent characteristic of pancreatic ductal adenocarcinoma (PDAC). PNI is associated with tumor progression, local recurrence and neuropathic pain; therefore, the identification of biomarkers associated with PNI may be beneficial in assessing the prognosis for patients with PDAC. Using an model of PNI, five pancreatic cancer cell lines (PANC-1, CFPAC-1, CAPAN-2, SW1990 and ASPC-1) were divided into two groups: High-(comprising PANC-1, CFPAC-1 and CAPAN-2) and low PNI (comprising SW1990 and ASPC-1). Differentially expressed genes (DEGs) between the two groups were identified using the GSE26088 dataset, and were regarded as PNI-associated genes. A total of 445 DEGs associated with PNI (fold change >1.5 or <0.66; P<0.05) were identified, which included 176 up- and 269 downregulated genes. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis and function annotation were performed, and the NetworkAnalyst database was used for protein-protein interaction network analysis to identify hub genes. A total of 20 hub genes (gene degree, ≥6) were identified. PNI was associated with the function 'chemokine signaling pathway'. The DEGs and hub genes were validated using the GSE102238 dataset and clinical tissue microarrays. Fibroblast growth factor 2 (FGF2) and catenin α 2 were demonstrated to be associated with PNI using the GSE102238 dataset. Furthermore, clinical tissue microarray analysis demonstrated that FGF2 was associated with PNI and poor prognosis. The present study provided a potential method for the reliable identification of PNI-associated genes, although further investigation is required to validate these results.
神经周围浸润(PNI)是胰腺导管腺癌(PDAC)的一个显著特征。PNI与肿瘤进展、局部复发和神经性疼痛相关;因此,鉴定与PNI相关的生物标志物可能有助于评估PDAC患者的预后。利用PNI模型,将五种胰腺癌细胞系(PANC-1、CFPAC-1、CAPAN-2、SW1990和ASPC-1)分为两组:高PNI组(包括PANC-1、CFPAC-1和CAPAN-2)和低PNI组(包括SW1990和ASPC-1)。使用GSE26088数据集鉴定两组之间的差异表达基因(DEG),并将其视为与PNI相关的基因。共鉴定出445个与PNI相关的DEG(倍数变化>1.5或<0.66;P<0.05),其中包括176个上调基因和269个下调基因。进行了京都基因与基因组百科全书通路富集分析和功能注释,并使用NetworkAnalyst数据库进行蛋白质-蛋白质相互作用网络分析以鉴定枢纽基因。共鉴定出20个枢纽基因(基因度≥6)。PNI与“趋化因子信号通路”功能相关。使用GSE102238数据集和临床组织微阵列对DEG和枢纽基因进行了验证。使用GSE102238数据集证明成纤维细胞生长因子2(FGF2)和连环蛋白α2与PNI相关。此外,临床组织微阵列分析表明FGF2与PNI和不良预后相关。本研究提供了一种可靠鉴定与PNI相关基因的潜在方法,尽管需要进一步研究来验证这些结果。