Transcriptome Complexity Disentangled: A Regulatory Molecules Approach

Amir Asiaee

doi:10.3390/ijms26062510

2025-03-11

DOI：10.3390/ijms26062510

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摘要

En 中文

转录因子（TFs）和微RNA（miRNAs）是基因表达、细胞状态和生物学过程的基本调控因子。本研究探讨了少量TFs和miRNAs能否准确预测全基因组范围的基因表达。我们分析了来自癌症基因组图谱的31种癌症类型的8895个样本，并使用无监督学习识别出28个miRNA和28个TF聚类。这些聚类的中心点能够以92.8%的准确率区分组织来源，表明其生物学相关性。我们开发了组织无关和组织相关的模型，利用56个选定的中心点miRNA和TFs预测20,000个基因的表达。通过整合组织特异性信息，组织相关模型达到了R2为0.70。尽管仅测量了转录组的1/400，预测准确性与1000个标志基因达到的准确度相当。这表明转录组具有固有的低维结构，可由少数调控分子捕获。我们的方法可能实现更经济的转录组检测和对低质量样本的分析，同时为miRNAs/TFs与其他机制高度调控的基因提供见解。然而，模型的可移植性受到数据集差异的影响，尤其是在miRNA分布方面。总体而言，本研究展示了生物学引导方法在稳健转录组表示方面的潜力。

Keyword:

transcriptome representation

transcription factors (TFs)

microRNAs (miRNAs)

low-dimensional structure

tissue-aware modeling

期刊

International Journal of Molecular Sciences

IF:

4.9

论文数:

2.0W

被引数:

44.5W

机构

Washington Univ

学者数:

2.4K

论文数: 1.3K

被引数: 439

Med Coll Georgia

学者数:

论文数: 161

被引数: 4

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