Li Tong, Liu Qiaofei, Zhang Ronghua, Liao Quan, Zhao Yupei
Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730 China.
Cancer Cell Int. 2020 Jul 25;20:341. doi: 10.1186/s12935-020-01426-1. eCollection 2020.
As one of the most lethal cancers, pancreatic cancer has been characterized by abundant supportive tumor-stromal cell microenvironment. Although the advent of tumor-targeted immune checkpoint blockers has brought light to patients with other cancers, its clinical efficacy in pancreatic cancer has been greatly limited due to the protective stroma. Thus, it is urgent to find potential new targets and establish multi-regulatory networks to predict patient prognosis and improve treatment.
We followed a strategy based on mining the Cancer Genome Atlas (TCGA) database and ESTIMATE algorithm to obtain the immune scores and stromal scores. Differentially expressed genes (DEGs) associated with poor overall survival of pancreatic cancer were screened from a TCGA cohort. By comparing global gene expression with high vs. low immune scores and subsequent Kaplan-Meier analysis, DEGs that significantly correlate with poor overall survival of pancreatic cancer in TCGA cohort were extracted. After constructing the protein-protein interaction network using STRING and limiting the genes within the above DEGs, we utilized RAID 2.0, TRRUST v2 database and degree and betweenness analysis to obtain non-coding RNA (ncRNA)-pivotal nodes and TF-pivotal nodes. Finally, multi-regulatory networks have been constructed and pivotal drugs with potential benefit for pancreatic cancer patients were obtained by screening in the DrugBank.
In this study, we obtained 246 DEGs that significantly correlate with poor overall survival of pancreatic cancer in the TCGA cohort. With the advent of 38 ncRNA-pivotal nodes and 7 TF-pivotal nodes, the multi-factor regulatory networks were constructed based on the above pivotal nodes. Prognosis-related genes and factors such as HCAR3, PPY, RFWD2, WSPAR and Amcinonide were screened and investigated.
The multi-regulatory networks constructed in this study are not only beneficial to improve treatment and evaluate patient prognosis with pancreatic cancer, but also favorable for implementing early diagnosis and personalized treatment. It is suggested that these factors may play an essential role in the progression of pancreatic cancer.
作为最致命的癌症之一，胰腺癌的特征是具有丰富的支持性肿瘤基质细胞微环境。尽管肿瘤靶向免疫检查点阻滞剂的出现给其他癌症患者带来了希望，但由于保护性基质的存在，其在胰腺癌中的临床疗效受到极大限制。因此，迫切需要寻找潜在的新靶点并建立多调控网络，以预测患者预后并改善治疗效果。
我们采用基于挖掘癌症基因组图谱（TCGA）数据库和ESTIMATE算法的策略来获得免疫评分和基质评分。从TCGA队列中筛选出与胰腺癌总体生存率差相关的差异表达基因（DEG）。通过比较高免疫评分与低免疫评分的全局基因表达并进行后续的Kaplan-Meier分析，提取出与TCGA队列中胰腺癌总体生存率差显著相关的DEG。使用STRING构建蛋白质-蛋白质相互作用网络并将基因限制在上述DEG范围内后，我们利用RAID 2.0、TRRUST v2数据库以及度和介数分析来获得非编码RNA（ncRNA）关键节点和转录因子（TF）关键节点。最后，构建了多调控网络，并通过在DrugBank中筛选获得了对胰腺癌患者可能有益的关键药物。
在本研究中，我们在TCGA队列中获得了246个与胰腺癌总体生存率差显著相关的DEG。随着38个ncRNA关键节点和7个TF关键节点的出现，基于上述关键节点构建了多因素调控网络。筛选并研究了与预后相关的基因和因素，如HCAR3、PPY、RFWD2、WSPAR和氨氯地平。
本研究构建的多调控网络不仅有利于改善胰腺癌的治疗和评估患者预后，还有利于实现早期诊断和个性化治疗。提示这些因素可能在胰腺癌的进展中起重要作用。
