Identification of prognosis-related genes and construction of multi-regulatory networks in pancreatic cancer microenvironment by bioinformatics analysis
Identification of prognosis-related genes and construction of multi-regulatory networks in pancreatic cancer microenvironment by bioinformatics analysis
复制标题
通过生物信息学分析识别胰腺癌微环境中预后相关基因并构建多调控网络
DOI：
10.1186/s12935-020-01426-1
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发表时间：
2020
影响因子：
5.8
通讯作者：
Yupei Zhao
中科院分区：
文献类型：
--
作者：
Tong Li;Qiaofei Liu;Ronghua Zhang;Quan Liao;Yupei Zhao
抽象的。背景胰腺癌是最致命的癌症之一，其特征在于肿瘤基质细胞微环境的丰富支持。尽管肿瘤靶向免疫检查点阻断剂的出现为其他癌症患者带来了光明，但由于保护性基质，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，建立多调控网络，以预测患者的预后，改善治疗。方法.我们遵循一种基于挖掘癌症基因组图谱（TCGA）数据库和ESTIMATE算法的策略，以获得免疫评分和基质评分。从TCGA队列中筛选与胰腺癌不良总生存率相关的差异表达基因（DEG）。通过比较具有高与低免疫评分的总体基因表达和随后的Kaplan-Meier分析，提取与TCGA队列中胰腺癌的不良总生存率显著相关的DEG。在利用STRING构建蛋白质-蛋白质相互作用网络并将基因限定在上述DEG内后，利用RAID 2.0、TRRUST v2数据库和度介数分析获得非编码RNA（ncRNA）-关键节点和转录因子（TF）-关键节点。最后，构建了多监管网络，并通过药物库筛选获得对胰腺癌患者有潜在益处的关键药物。结果在这项研究中，我们获得了246个DEG，这些DEG与TCGA队列中胰腺癌的总生存率显著相关。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子调控网络。筛选HCAR 3、PPY、RFWD 2、WSPAR和Amcinonide等预后相关基因和因子。结论本研究构建的多调控网络不仅有利于提高胰腺癌的治疗水平和评估患者预后，而且有利于实施早期诊断和个性化治疗。提示这些因素可能在胰腺癌的发生发展中起重要作用。
Abstract. Background. 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.. Methods. 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 correlated 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 transcription factor (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.. Results. In this study, we obtained 246 DEGs that significantly correlated 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.. Conclusion. 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.