Cihan Mert, Anyaegbunam Uchenna Alex, Albrecht Steffen, Andrade-Navarro Miguel A, Sprang Maximilian
Institute of Organismic and Molecular Evolution, Faculty of Biology, Johannes Gutenberg University Mainz, 55128 Mainz, Germany.
Department of General Practice and Primary Health Care, Faculty of Medical and Health Sciences (FMHS), The University of Auckland, Auckland 1023, New Zealand.
Int J Mol Sci. 2025 Jun 16;26(12):5757. doi: 10.3390/ijms26125757.
This study explores the genetic regulators of microRNAs (miRNAs) using a set of machine learning models to predict miRNA expression levels from gene expression data. Employing machine learning, we accurately predicted the expression of 353 human miRNAs (R > 0.5), revealing robust miRNA-gene regulatory relationships. By analyzing the coefficients of these predictive models, we identified genetic regulators for each miRNA and highlighted the multifactorial nature of miRNA regulation. Further network analysis uncovered that miRNAs with higher predictive accuracy are more densely connected to their top predictive genes, reflecting strong regulatory control within miRNA-gene networks. To refine these insights, we filtered the miRNA-gene interaction networks to identify miRNAs specifically associated with enriched pathways, such as synaptic function and cardiovascular processes. From this pathway-centric analysis, we present a curated list of miRNAs and their genetic regulators, pinpointing their activity within distinct biological contexts. Additionally, our study provides a comprehensive set of metrics and coefficients for the genes most predictive of miRNA expression, along with a filtered subnetwork of miRNAs linked to specific pathways and phenotypes. By integrating miRNA expression predictors with network analysis and pathway enrichment, this work advances our understanding of miRNA regulatory mechanisms and their roles across distinct biological systems. Our approach enables researchers to train custom models using TCGA data and predict miRNA expression from gene expression inputs.
本研究使用一组机器学习模型从基因表达数据预测微小RNA（miRNA）表达水平，探索miRNA的基因调控因子。通过机器学习，我们准确预测了353个人类miRNA的表达（R>0.5），揭示了强大的miRNA-基因调控关系。通过分析这些预测模型的系数，我们确定了每个miRNA的基因调控因子，并强调了miRNA调控的多因素性质。进一步的网络分析发现，预测准确性较高的miRNA与其顶级预测基因的连接更为密集，反映了miRNA-基因网络内强大的调控控制。为了完善这些见解，我们对miRNA-基因相互作用网络进行了筛选，以确定与富集途径（如突触功能和心血管过程）特异性相关的miRNA。通过这种以途径为中心的分析，我们列出了一份经过整理的miRNA及其基因调控因子清单，确定了它们在不同生物学背景下的活性。此外，我们的研究为最能预测miRNA表达的基因提供了一套全面的指标和系数，以及一个与特定途径和表型相关的miRNA筛选子网。通过将miRNA表达预测因子与网络分析和途径富集相结合，这项工作推进了我们对miRNA调控机制及其在不同生物系统中作用的理解。我们的方法使研究人员能够使用TCGA数据训练定制模型，并从基因表达输入预测miRNA表达。
