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Noninvasive detection of brain gliomas using plasma cell‐free DNA 5‐hydroxymethylcytosine sequencing

胶质瘤 胎儿游离DNA DNA 5-羟甲基胞嘧啶 脑细胞 DNA测序 生物 计算生物学 化学 癌症研究 基因 细胞生物学 基因表达 遗传学 DNA甲基化 怀孕 产前诊断 胎儿 生物化学
作者
Shuxin Zhang,Jun Zhang,Xinlei Hu,Senlin Yin,Yunbo Yuan,Lin Xia,Feng Cao,Xiaoqin Yan,Ziyue Yan,Qing Mao,Dan Xie,Yan‐Hui Liu
出处
期刊:International Journal of Cancer [Wiley]
卷期号:152 (8): 1707-1718 被引量:9
标识
DOI:10.1002/ijc.34401
摘要

Liquid biopsy techniques based on deep sequencing of plasma cell-free DNA (cfDNA) could detect the low-frequency somatic mutations and provide an accurate diagnosis for many cancers. However, for brain gliomas, reliable performance of these techniques currently requires obtaining cfDNA from patients' cerebral spinal fluid, which is cumbersome and risky. Here we report a liquid biopsy method based on sequencing of plasma cfDNA fragments carrying 5-hydroxymethylcytosine (5hmC) using selective chemical labeling (hMe-Seal). We first constructed a dataset including 180 glioma patients and 229 non-glioma controls. We found marked concordance between cfDNA hydroxymethylome and the aberrant transcriptome of the underlying gliomas. Functional analysis also revealed overrepresentation of the differentially hydroxymethylated genes (DhmGs) in oncogenic and neural pathways. After splitting our dataset into training and test cohort, we showed that a penalized logistic model constructed with training set DhmGs could distinguish glioma patients from healthy controls in both our test set (AUC = 0.962) and an independent dataset (AUC = 0.930) consisting of 111 gliomas and 111 controls. Additionally, the DhmGs between gliomas with mutant and wild-type isocitrate dehydrogenase (IDH) could be used to train a cfDNA predictor of the IDH mutation status of the underlying tumor (AUC = 0.816), and patients with predicted IDH mutant gliomas had significantly better outcome (P = .01). These results indicate that our plasma cfDNA 5hmC sequencing method could obtain glioma-specific signals, which may be used to noninvasively detect these patients and predict the aggressiveness of their tumors.
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