MicroRNA and transcription factor co-regulatory network analysis reveals miR-19 inhibits CYLD in T-cell acute lymphoblastic leukemia.

T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive hematological malignancy. The understanding of its gene expression regulation and molecular mechanisms still remains elusive. Started from experimentally verified T-ALL-related miRNAs and genes, we obtained 120 feed-forward loops (FFLs) among T-ALL-related genes, miRNAs and TFs through combining target prediction. Afterwards, a T-ALL miRNA and TF co-regulatory network was constructed, and its significance was tested by statistical methods. Four miRNAs in the miR-17–92 cluster and four important genes (CYLD, HOXA9, BCL2L11 and RUNX1) were found as hubs in the network. Particularly, we found that miR-19 was highly expressed in T-ALL patients and cell lines. Ectopic expression of miR-19 represses CYLD expression, while miR-19 inhibitor treatment induces CYLD protein expression and decreases NF-κB expression in the downstream signaling pathway. Thus, miR-19, CYLD and NF-κB form a regulatory FFL, which provides new clues for sustained activation of NF-κB in T-ALL. Taken together, we provided the first miRNA-TF co-regulatory network in T-ALL and proposed a model to demonstrate the roles of miR-19 and CYLD in the T-cell leukemogenesis. This study may provide potential therapeutic targets for T-ALL and shed light on combining bioinformatics with experiments in the research of complex diseases.

T细胞急性淋巴细胞白血病（T - ALL）是一种侵袭性的血液系统恶性肿瘤。对其基因表达调控和分子机制的了解仍然难以捉摸。从经过实验验证的与T - ALL相关的微小RNA（miRNA）和基因出发，我们通过结合靶点预测获得了120个在T - ALL相关基因、miRNA和转录因子（TF）之间的前馈环路（FFL）。之后，构建了一个T - ALL的miRNA和TF共同调控网络，并通过统计方法检验了其显著性。在miR - 17 - 92簇中的4种miRNA以及4个重要基因（CYLD、HOXA9、BCL2L11和RUNX1）被发现是该网络中的关键节点。特别地，我们发现miR - 19在T - ALL患者和细胞系中高表达。miR - 19的异位表达抑制CYLD表达，而miR - 19抑制剂处理诱导CYLD蛋白表达，并降低下游信号通路中的NF - κB表达。因此，miR - 19、CYLD和NF - κB形成一个调控性的前馈环路，这为T - ALL中NF - κB的持续激活提供了新的线索。综上所述，我们提供了T - ALL中的首个miRNA - TF共同调控网络，并提出了一个模型来展示miR - 19和CYLD在T细胞白血病发生中的作用。这项研究可能为T - ALL提供潜在的治疗靶点，并为在复杂疾病研究中将生物信息学与实验相结合提供启示。