Das Sarmistha, Rai Shesh N
Biostatistics and Informatics Shared Resource, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA.
Cancer Data Science Center, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA.
Noncoding RNA. 2024 Aug 15;10(4):45. doi: 10.3390/ncrna10040045.
Gene regulation is crucial for cellular function and homeostasis. It involves diverse mechanisms controlling the production of specific gene products and contributing to tissue-specific variations in gene expression. The dysregulation of genes leads to disease, emphasizing the need to understand these mechanisms. Computational methods have jointly studied transcription factors (TFs), microRNA (miRNA), and messenger RNA (mRNA) to investigate gene regulatory networks. However, there remains a knowledge gap in comprehending gene regulatory networks. On the other hand, super-enhancers (SEs) have been implicated in miRNA biogenesis and function in recent experimental studies, in addition to their pivotal roles in cell identity and disease progression. However, statistical/computational methodologies harnessing the potential of SEs in deciphering gene regulation networks remain notably absent. However, to understand the effect of miRNA on mRNA, existing statistical/computational methods could be updated, or novel methods could be developed by accounting for SEs in the model. In this review, we categorize existing computational methods that utilize TF and miRNA data to understand gene regulatory networks into three broad areas and explore the challenges of integrating enhancers/SEs. The three areas include unraveling indirect regulatory networks, identifying network motifs, and enriching pathway identification by dissecting gene regulators. We hypothesize that addressing these challenges will enhance our understanding of gene regulation, aiding in the identification of therapeutic targets and disease biomarkers. We believe that constructing statistical/computational models that dissect the role of SEs in predicting the effect of miRNA on gene regulation is crucial for tackling these challenges.
基因调控对于细胞功能和内稳态至关重要。它涉及多种机制，控制特定基因产物的产生，并导致基因表达的组织特异性差异。基因失调会导致疾病，这凸显了理解这些机制的必要性。计算方法联合研究转录因子（TFs）、微小RNA（miRNA）和信使RNA（mRNA），以研究基因调控网络。然而，在理解基因调控网络方面仍存在知识空白。另一方面，除了在细胞身份和疾病进展中的关键作用外，超级增强子（SEs）在最近的实验研究中还与miRNA的生物发生和功能有关。然而，利用SEs潜力来破译基因调控网络的统计/计算方法仍然明显缺乏。然而，为了理解miRNA对mRNA的影响，可以更新现有的统计/计算方法，或者通过在模型中考虑SEs来开发新方法。在本综述中，我们将利用TF和miRNA数据来理解基因调控网络的现有计算方法分为三大类，并探讨整合增强子/SEs的挑战。这三个领域包括揭示间接调控网络、识别网络基序以及通过剖析基因调控因子来丰富通路识别。我们假设解决这些挑战将增强我们对基因调控的理解，有助于识别治疗靶点和疾病生物标志物。我们认为，构建剖析SEs在预测miRNA对基因调控影响中作用的统计/计算模型对于应对这些挑战至关重要。
