泛癌症综合分析揭示了以增强子甲基化缺失为特征的转录失调网络的共同结构。

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS PLoS Computational Biology Pub Date : 2024-11-18 DOI:10.1371/journal.pcbi.1012565
Jørgen Ankill, Zhi Zhao, Xavier Tekpli, Elin H Kure, Vessela N Kristensen, Anthony Mathelier, Thomas Fleischer
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引用次数: 0

摘要

DNA甲基化异常是导致癌症基因表达失调的原因之一。然而,这些改变的精确调控作用和临床影响仍未完全明了。在这项研究中,我们进行了表达-甲基化定量性状位点(emQTL)分析,以确定与CpG去甲基化相关的癌症驱动转录网络。通过分析《癌症基因组图谱》(The Cancer Genome Atlas)中的 33 种癌症类型,我们发现并证实了 CpG 甲基化与基因表达(emQTL)之间的顺式和反式显著相关性,包括跨癌症类型和癌症类型内部的相关性。emQTL的双向网络分析揭示了CpGs群和基因群,它们与癌变过程中的重要生物学过程有关,包括增殖、新陈代谢和激素信号转导。这些双方位群落的特征是特定转录因子结合区(TFBR)中增强子甲基化的缺失,CpGs通过染色质环路与上调基因拓扑相连。惩罚性 Cox 回归分析表明,泛癌症 emQTL 对许多癌症类型的预后有显著影响。综上所述,我们的泛癌症综合分析揭示了一个共同的结构,在这个结构中,标志性的癌症驱动功能受到增强子甲基化缺失的影响,并可能受到表观遗传学的调控。
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Integrative pan-cancer analysis reveals a common architecture of dysregulated transcriptional networks characterized by loss of enhancer methylation.

Aberrant DNA methylation contributes to gene expression deregulation in cancer. However, these alterations' precise regulatory role and clinical implications are still not fully understood. In this study, we performed expression-methylation Quantitative Trait Loci (emQTL) analysis to identify deregulated cancer-driving transcriptional networks linked to CpG demethylation pan-cancer. By analyzing 33 cancer types from The Cancer Genome Atlas, we identified and confirmed significant correlations between CpG methylation and gene expression (emQTL) in cis and trans, both across and within cancer types. Bipartite network analysis of the emQTL revealed groups of CpGs and genes related to important biological processes involved in carcinogenesis including proliferation, metabolism and hormone-signaling. These bipartite communities were characterized by loss of enhancer methylation in specific transcription factor binding regions (TFBRs) and the CpGs were topologically linked to upregulated genes through chromatin loops. Penalized Cox regression analysis showed a significant prognostic impact of the pan-cancer emQTL in many cancer types. Taken together, our integrative pan-cancer analysis reveals a common architecture where hallmark cancer-driving functions are affected by the loss of enhancer methylation and may be epigenetically regulated.

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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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