Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi, China.
Sci Rep. 2020 Jan 21;10(1):852. doi: 10.1038/s41598-020-57834-w.
Recent studies have revealed that feed-forward loops (FFLs) as regulatory motifs have synergistic roles in cellular systems and their disruption may cause diseases including cancer. FFLs may include two regulators such as transcription factors (TFs) and microRNAs (miRNAs). In this study, we extensively investigated TF and miRNA regulation pairs, their FFLs, and TF-miRNA mediated regulatory networks in two major types of testicular germ cell tumors (TGCT): seminoma (SE) and non-seminoma (NSE). Specifically, we identified differentially expressed mRNA genes and miRNAs in 103 tumors using the transcriptomic data from The Cancer Genome Atlas. Next, we determined significantly correlated TF-gene/miRNA and miRNA-gene/TF pairs with regulation direction. Subsequently, we determined 288 and 664 dysregulated TF-miRNA-gene FFLs in SE and NSE, respectively. By constructing dysregulated FFL networks, we found that many hub nodes (12 out of 30 for SE and 8 out of 32 for NSE) in the top ranked FFLs could predict subtype-classification (Random Forest classifier, average accuracy ≥90%). These hub molecules were validated by an independent dataset. Our network analysis pinpointed several SE-specific dysregulated miRNAs (miR-200c-3p, miR-25-3p, and miR-302a-3p) and genes (EPHA2, JUN, KLF4, PLXDC2, RND3, SPI1, and TIMP3) and NSE-specific dysregulated miRNAs (miR-367-3p, miR-519d-3p, and miR-96-5p) and genes (NR2F1 and NR2F2). This study is the first systematic investigation of TF and miRNA regulation and their co-regulation in two major TGCT subtypes.
最近的研究表明,前馈环(FFL)作为调节基序在细胞系统中具有协同作用,其破坏可能导致包括癌症在内的疾病。FFL 可能包括两个调节剂,如转录因子(TF)和 microRNA(miRNA)。在这项研究中,我们广泛研究了两种主要类型的睾丸生殖细胞肿瘤(TGCT):精原细胞瘤(SE)和非精原细胞瘤(NSE)中的 TF 和 miRNA 调节对、它们的 FFL 和 TF-miRNA 介导的调节网络。具体来说,我们使用来自癌症基因组图谱的转录组数据,鉴定了 103 个肿瘤中的差异表达 mRNA 基因和 miRNA。接下来,我们确定了具有调节方向的显著相关 TF-基因/miRNA 和 miRNA-基因/TF 对。随后,我们分别确定了 SE 和 NSE 中 288 和 664 个失调的 TF-miRNA-基因 FFL。通过构建失调的 FFL 网络,我们发现顶级 FFL 中许多枢纽节点(SE 中有 30 个中的 12 个,NSE 中有 32 个中的 8 个)可以预测亚型分类(随机森林分类器,平均准确性≥90%)。这些枢纽分子通过独立数据集得到了验证。我们的网络分析指出了一些 SE 特异性失调的 miRNA(miR-200c-3p、miR-25-3p 和 miR-302a-3p)和基因(EPHA2、JUN、KLF4、PLXDC2、RND3、SPI1 和 TIMP3)以及 NSE 特异性失调的 miRNA(miR-367-3p、miR-519d-3p 和 miR-96-5p)和基因(NR2F1 和 NR2F2)。这项研究是对两种主要 TGCT 亚型中 TF 和 miRNA 调节及其共同调节的首次系统研究。