Identification of Novel Kinase-Transcription Factor-mRNA-miRNA Regulatory Network in Nasopharyngeal Carcinoma by Bioinformatics Analysis.
Identification of Novel Kinase-Transcription Factor-mRNA-miRNA Regulatory Network in Nasopharyngeal Carcinoma by Bioinformatics Analysis.
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DOI：
10.2147/ijgm.s327657
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发表时间：
2021
影响因子：
2.3
通讯作者：
Huang X
中科院分区：
文献类型：
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作者：
Gao L;Zhou L;Huang X
鼻咽癌是头颈部最常见的恶性肿瘤之一。本研究旨在利用生物信息学方法研究参与鼻咽癌发生的关键基因和调控网络。从Gene expression Omnibus （GEO）数据库下载5个mRNA和2个miRNA表达数据集。用R软件分析鼻咽癌与正常样本的差异表达基因（DEGs）和miRNAs （dem）。使用WebGestalt工具进行功能富集分析，使用STRING数据库进行蛋白-蛋白相互作用（PPI）网络分析。转录因子（TFs）预测使用TRRUST和转录调控元件数据库（trred）。激酶用X2Kgui进行鉴定。使用miRWalk数据库预测DEGs的mirna。构建激酶- tf - mrna - mirna整合网络，选择枢纽节点。利用GEO和Oncomine数据库中的NPC数据集对中心基因进行验证。最后，利用CMap预测候选小分子药物。共鉴定出122个deg和44个dem。在GO分析中，deg与免疫反应、白细胞活化、内质网应激有关；在KEGG分析中，deg与NF-κB信号通路有关。使用PPI网络分析确定了四个重要模块。随后，预测了26个tf、73个激酶和2499个mirna。预测的mirna与dem交叉引用，并选择7个重叠的mirna。在激酶- tnf - mrna - mirna整合网络中，8个基因（PTGS2、FN1、MMP1、PLAU、MMP3、CD19、BMP2和PIGR）被鉴定为枢纽基因。Hub基因得到了一致的验证结果，表明了我们研究结果的可靠性。最后，预测了6种候选小分子药物（phenoxybenzamine、木犀草素、硫鸟苷、利血平、blebbistatin和喜树碱）。我们发现了deg和一个涉及激酶、tf、mrna和mirna的NPC调控网络，这可能为NPC的发病机制、治疗和预后提供有希望的见解。
Nasopharyngeal carcinoma (NPC) is one of the most common malignant tumors of the head and neck. This study aimed to investigate the crucial genes and regulatory networks involved in the carcinogenesis of NPC using a bioinformatics approach. Five mRNA and two miRNA expression datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and miRNAs (DEMs) between NPC and normal samples were analyzed using R software. The WebGestalt tool was used for functional enrichment analysis, and protein–protein interaction (PPI) network analysis of DEGs was performed using STRING database. Transcription factors (TFs) were predicted using TRRUST and Transcriptional Regulatory Element Database (TRED). Kinases were identified using X2Kgui. The miRNAs of DEGs were predicted using miRWalk database. A kinase–TF–mRNA–miRNA integrated network was constructed, and hub nodes were selected. The hub genes were validated using NPC datasets from the GEO and Oncomine databases. Finally, candidate small-molecule agents were predicted using CMap. A total of 122 DEGs and 44 DEMs were identified. DEGs were associated with the immune response, leukocyte activation, endoplasmic reticulum stress in GO analysis, and the NF-κB signaling pathway in KEGG analysis. Four significant modules were identified using PPI network analysis. Subsequently, 26 TFs, 73 kinases, and 2499 miRNAs were predicted. The predicted miRNAs were cross-referenced with DEMs, and seven overlapping miRNAs were selected. In the kinase–TF–mRNA–miRNA integrated network, eight genes (PTGS2, FN1, MMP1, PLAU, MMP3, CD19, BMP2, and PIGR) were identified as hub genes. Hub genes were validated with consistent results, indicating the reliability of our findings. Finally, six candidate small-molecule agents (phenoxybenzamine, luteolin, thioguanosine, reserpine, blebbistatin, and camptothecin) were predicted. We identified DEGs and an NPC regulatory network involving kinases, TFs, mRNAs, and miRNAs, which might provide promising insight into the pathogenesis, treatment, and prognosis of NPC.