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利用血浆 cfDNA 的 WGS 数据进行多癌种早期检测（MCED）的甲基化状态预测。

Prediction of methylation status using WGS data of plasma cfDNA for multi-cancer early detection (MCED).

机构信息

Shenzhen Rapha Biotechnology Incorporate, Shenzhen, 518118, China.

Department of Liver Disease, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, 518033, China.

出版信息

Clin Epigenetics. 2024 Feb 27;16(1):34. doi: 10.1186/s13148-024-01646-6.

DOI:10.1186/s13148-024-01646-6
PMID:38414068
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10898085/
Abstract

BACKGROUND

Cell-free DNA (cfDNA) contains a large amount of molecular information that can be used for multi-cancer early detection (MCED), including changes in epigenetic status of cfDNA, such as cfDNA fragmentation profile. The fragmentation of cfDNA is non-random and may be related to cfDNA methylation. This study provides clinical evidence for the feasibility of inferring cfDNA methylation levels based on cfDNA fragmentation patterns. We performed whole-genome bisulfite sequencing and whole-genome sequencing (WGS) on both healthy individuals and cancer patients. Using the information of whole-genome methylation levels, we investigated cytosine-phosphate-guanine (CpG) cleavage profile and validated the method of predicting the methylation level of individual CpG sites using WGS data.

RESULTS

We conducted CpG cleavage profile biomarker analysis on data from both healthy individuals and cancer patients. We obtained unique or shared potential biomarkers for each group and built models accordingly. The modeling results proved the feasibility to predict the methylation status of single CpG sites in cfDNA using cleavage profile model from WGS data.

CONCLUSION

By combining cfDNA cleavage profile of CpG sites with machine learning algorithms, we have identified specific CpG cleavage profile as biomarkers to predict the methylation status of individual CpG sites. Therefore, methylation profile, a widely used epigenetic biomarker, can be obtained from a single WGS assay for MCED.

摘要

背景

无细胞 DNA（cfDNA）含有大量的分子信息，可用于多种癌症早期检测（MCED），包括 cfDNA 表观遗传状态的变化，如 cfDNA 片段化谱。cfDNA 的片段化是非随机的，可能与 cfDNA 甲基化有关。本研究为基于 cfDNA 片段化模式推断 cfDNA 甲基化水平的可行性提供了临床证据。我们对健康个体和癌症患者进行了全基因组亚硫酸氢盐测序和全基因组测序（WGS）。利用全基因组甲基化水平的信息，我们研究了胞嘧啶-磷酸-鸟嘌呤（CpG）切割谱，并验证了使用 WGS 数据预测单个 CpG 位点甲基化水平的方法。

结果

我们对健康个体和癌症患者的数据进行了 CpG 切割谱生物标志物分析。我们获得了每个组的独特或共享的潜在生物标志物，并相应地建立了模型。建模结果证明了使用 WGS 数据的切割谱模型预测 cfDNA 中单个 CpG 位点甲基化状态的可行性。

结论

通过将 CpG 位点的 cfDNA 切割谱与机器学习算法相结合，我们已经确定了特定的 CpG 切割谱作为生物标志物，以预测单个 CpG 位点的甲基化状态。因此，甲基化谱作为一种广泛使用的表观遗传生物标志物，可以从单个 WGS 检测中获得，用于 MCED。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e700/10898085/b92e980cb068/13148_2024_1646_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e700/10898085/9dfef298dd44/13148_2024_1646_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e700/10898085/01e71e2c96e9/13148_2024_1646_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e700/10898085/3c590b507f23/13148_2024_1646_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e700/10898085/b92e980cb068/13148_2024_1646_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e700/10898085/9dfef298dd44/13148_2024_1646_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e700/10898085/d4ab9219d983/13148_2024_1646_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e700/10898085/8502dac804c4/13148_2024_1646_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e700/10898085/6c88c4aefdd7/13148_2024_1646_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e700/10898085/01e71e2c96e9/13148_2024_1646_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e700/10898085/3c590b507f23/13148_2024_1646_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e700/10898085/b92e980cb068/13148_2024_1646_Fig7_HTML.jpg

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