肺大细胞神经内分泌癌中新型转录因子-微小RNA-信使核糖核酸共调控网络的鉴定
Identification of novel transcription factor-microRNA-mRNA co-regulatory networks in pulmonary large-cell neuroendocrine carcinoma.
作者信息
Cai Cunliang, Zeng Qianli, Zhou Guiliang, Mu Xiangdong
机构信息
Department of Respiratory and Critical Care Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.
The South China Center for Innovative Pharmaceuticals, Guangzhou, China.
出版信息
Ann Transl Med. 2021 Jan;9(2):133. doi: 10.21037/atm-20-7759.
BACKGROUND
Large cell neuroendocrine carcinoma (LCNEC) of the lung is a rare neuroendocrine neoplasm. Previous studies have shown that microRNAs (miRNAs) are widely involved in tumor regulation through targeting critical genes. However, it is unclear which miRNAs play vital roles in the pathogenesis of LCNEC, and how they interact with transcription factors (TFs) to regulate cancer-related genes.
METHODS
To determine the novel TF-miRNA-target gene feed-forward loop (FFL) model of LCNEC, we integrated multi-omics data from Gene Expression Omnibus (GEO), Transcriptional Regulatory Relationships Unraveled by Sentence-Based Text Mining (TRRUST), Transcriptional Regulatory Element Database (TRED), and The experimentally validated microRNA-target interactions database (miRTarBase database). First, expression profile datasets for mRNAs (GSE1037) and miRNAs (GSE19945) were downloaded from the GEO database. Overlapping differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were identified through integrative analysis. The target genes of the FFL were obtained from the miRTarBase database, and the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were performed on the target genes. Then, we screened for key miRNAs in the FFL and performed gene regulatory network analysis based on key miRNAs. Finally, the TF-miRNA-target gene FFLs were constructed by the hypergeometric test.
RESULTS
A total of 343 DEGs and 60 DEMs were identified in LCNEC tissues compared to normal tissues, including 210 down-regulated and 133 up-regulated genes, and 29 down-regulated and 31 up-regulated miRNAs. Finally, the regulatory network of TF-miRNA-target gene was established. The key regulatory network modules included ETS1-miR195-CD36, TAOK1-miR7-1-3P-GRIA1, E2F3-miR195-CD36, and TEAD1-miR30A-CTHRC1.
CONCLUSIONS
We constructed the TF-miRNA-target gene regulatory network, which is helpful for understanding the complex LCNEC regulatory mechanisms.
背景
肺大细胞神经内分泌癌（LCNEC）是一种罕见的神经内分泌肿瘤。先前的研究表明，微小RNA（miRNA）通过靶向关键基因广泛参与肿瘤调控。然而，尚不清楚哪些miRNA在LCNEC的发病机制中起关键作用，以及它们如何与转录因子（TF）相互作用以调节癌症相关基因。
方法
为了确定LCNEC的新型TF-miRNA-靶基因前馈环（FFL）模型，我们整合了来自基因表达综合数据库（GEO）、基于句子的文本挖掘揭示的转录调控关系（TRRUST）、转录调控元件数据库（TRED）和实验验证的微小RNA-靶标相互作用数据库（miRTarBase数据库）的多组学数据。首先，从GEO数据库下载mRNA（GSE1037）和miRNA（GSE19945）的表达谱数据集。通过综合分析鉴定重叠的差异表达基因（DEG）和差异表达miRNA（DEM）。FFL的靶基因从miRTarBase数据库中获得，并对靶基因进行基因本体（GO）和京都基因与基因组百科全书（KEGG）功能富集分析。然后，我们在FFL中筛选关键miRNA，并基于关键miRNA进行基因调控网络分析。最后，通过超几何检验构建TF-miRNA-靶基因FFL。
结果
与正常组织相比，在LCNEC组织中总共鉴定出343个DEG和60个DEM，包括210个下调基因和133个上调基因，以及29个下调miRNA和31个上调miRNA。最后，建立了TF-miRNA-靶基因的调控网络。关键调控网络模块包括ETS1-miR195-CD36、TAOK1-miR7-1-3P-GRIA1、E2F3-miR195-CD36和TEAD1-miR30A-CTHRC1。
结论
我们构建了TF-miRNA-靶基因调控网络，这有助于理解复杂的LCNEC调控机制。
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