模拟引导的泛癌症分析确定了CpG岛超甲基化异质性的新调节因子。

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Xianglin Zhang, Wei Zhang, Jinyi Zhang, Xiuhong Lyu, Haoran Pan, Tianwei Jia, Ting Wang, Xiaowo Wang, Haiyang Guo
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引用次数: 0

摘要

CpG岛高甲基化是癌症的一个标志,在不同的肿瘤中表现出很大的异质性,这为癌症诊断和治疗带来了机遇和挑战。虽然这种异质性为患者分层预测临床结果和个性化治疗提供了可能,但它使早期检测的强大生物标志物的开发变得复杂。了解驱动这种异质性的机制对于推进生物标志物的设计至关重要。在这里,基于模拟的分析表明,肿瘤纯度和低外显突变样本的高患病率显著模糊了CpG岛超甲基化的阴性调节因子的识别,而不是阳性调节因子,限制了对异质性来源的全面理解。通过解决这些混杂因素,我们确定了DNA甲基化维持受损,如全球低甲基化水平所示,是已知调节因子中CpG岛高甲基化变异性的主要因素。这一发现得到了来自癌症基因组图谱(TCGA)泛癌症图谱、癌症药物敏感性基因组学(GDSC1000)癌细胞系的数据集的综合分析的支持,并使用新开发的方法MeHist (https://github.com/vhang072/MeHist)对两个独立的亚硫酸氢盐全基因组测序队列进行了外等位基因分析。此外,我们评估了10种癌症类型中广泛使用的高甲基化生物标志物,发现246种癌症中有65种(26.4%)受到甲基化维持受损的显著影响。结合低甲基化和高甲基化标记提高了癌症检测的稳健性,这在多个无浆细胞DNA数据集中得到了验证。总之,我们的研究结果强调了模拟引导的综合分析在减轻混杂效应方面的价值,并确定受损的DNA甲基化维持是CpG岛高甲基化异质性的关键调节因子。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Simulation-guided pan-cancer analysis identifies a novel regulator of CpG island hypermethylation heterogeneity.

CpG island hypermethylation, a hallmark of cancer, exhibits substantial heterogeneity across tumors, presenting both opportunities and challenges for cancer diagnostics and therapeutics. While this heterogeneity offers potential for patient stratification to predict clinical outcomes and personalize treatments, it complicates the development of robust biomarkers for early detection. Understanding the mechanisms driving this heterogeneity is essential for advancing biomarker design. Here, simulation-based analyses demonstrate that tumor purity and the high prevalence of low epi-mutation samples significantly obscure the identification of negative, rather than positive, regulators of CpG island hypermethylation, limiting a comprehensive understanding of heterogeneity sources. By addressing these confounders, we identify impaired DNA methylation maintenance, as indicated by global hypomethylation levels, as the primary contributor to CpG island hypermethylation variability among known regulators. This finding is supported by integrative analyses of datasets from The Cancer Genome Atlas (TCGA) Pan-Cancer Atlas, Genomics of Drug Sensitivity in Cancer (GDSC1000) cancer cell lines, and epi-allele analyses of two independent whole-genome bisulfite sequencing cohorts, using a newly developed method, MeHist (https://github.com/vhang072/MeHist). Furthermore, we assess widely used hypermethylation biomarkers across ten cancer types and find that 65 out of 246 (26.4%) are significantly influenced by impaired methylation maintenance. Incorporating hypomethylation and hypermethylation markers improves the robustness of cancer detection, as validated across multiple plasma cell-free DNA datasets. In summary, our findings highlight the value of simulation-guided integrative analysis in mitigating confounding effects and identify impaired DNA methylation maintenance as a key regulator of CpG island hypermethylation heterogeneity.

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来源期刊
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
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