Cell-free DNA methylation biomarker for the diagnosis of papillary thyroid carcinoma

甲状腺癌 胎儿游离DNA DNA甲基化 生物标志物 甲基化 医学 甲状腺癌 肿瘤科 病理 癌症研究 甲状腺结节 表观遗传学 内科学 甲状腺 生物 DNA 基因 遗传学 基因表达 怀孕 胎儿 产前诊断
作者
Shubin Hong,Bo Lin,Minjie Xu,Quan Zhang,Zijun Huo,Ming‐Yang Su,Chengcheng Ma,Jinyu Liang,Shuang Yu,Qiye He,Zhixi Su,Yanbing Li,Rui Liu,Zhuming Guo,Weiming Lv,Haipeng Xiao
出处
期刊:EBioMedicine [Elsevier]
卷期号:90: 104497-104497 被引量:3
标识
DOI:10.1016/j.ebiom.2023.104497
摘要

Cell-free DNA (cfDNA) is being explored as biomarker for non-invasive diagnosis of cancer. We aimed to establish a cfDNA-based DNA methylation marker panel to differentially diagnose papillary thyroid carcinoma (PTC) from benign thyroid nodule (BTN).220 PTC- and 188 BTN patients were enrolled. Methylation markers of PTC were identified from patients' tissue and plasma by reduced representation bisulfite sequencing and methylation haplotype analyses. They were combined with PTC markers from literatures and were tested on additional PTC and BTN samples to verify PTC-detecting ability using targeted methylation sequencing. Top markers were developed into ThyMet and were tested in 113 PTC and 88 BTN cases to train and validate a PTC-plasma classifier. Integration of ThyMet and thyroid ultrasonography was explored to improve accuracy.From 859 potential PTC plasma-discriminating markers that include 81 markers identified by us, the top 98 most PTC plasma-discriminating markers were selected for ThyMet. A 6-marker ThyMet classifier for PTC plasma was trained. In validation it achieved an Area Under the Curve (AUC) of 0.828, similar to thyroid ultrasonography (0.833) but at higher specificity (0.722 and 0.625 for ThyMet and ultrasonography, respectively). A combinatorial classifier by them, ThyMet-US, improved AUC to 0.923 (sensitivity = 0.957, specificity = 0.708).The ThyMet classifier improved the specificity of differentiating PTC from BTN over ultrasonography. The combinatorial ThyMet-US classifier may be effective in preoperative diagnosis of PTC.This work was supported by the grants from National Natural Science Foundation of China (82072956 and 81772850).

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
Ziezer完成签到,获得积分10
刚刚
WRWRWR发布了新的文献求助10
刚刚
星辰大海应助swing采纳,获得10
刚刚
无言发布了新的文献求助10
1秒前
Jasper应助牛哥采纳,获得10
1秒前
XCYANG07发布了新的文献求助10
1秒前
苦小厄发布了新的文献求助10
1秒前
棠真发布了新的文献求助10
1秒前
米花发布了新的文献求助10
3秒前
panda完成签到,获得积分20
3秒前
萌兴完成签到 ,获得积分10
4秒前
wyz完成签到,获得积分20
4秒前
周建勇完成签到,获得积分10
4秒前
CodeCraft应助丹丹采纳,获得10
4秒前
桐桐应助forgive采纳,获得10
5秒前
5秒前
5秒前
5秒前
半城烟火完成签到 ,获得积分10
5秒前
随便啦完成签到,获得积分20
6秒前
科研通AI6.3应助小小采纳,获得20
6秒前
7秒前
7秒前
科研通AI2S应助123fhq采纳,获得10
8秒前
呼呼呼完成签到,获得积分10
8秒前
5High_0发布了新的文献求助10
9秒前
9秒前
10秒前
呆呆给呆呆的求助进行了留言
10秒前
田様应助一粒米采纳,获得10
10秒前
gzsy完成签到 ,获得积分10
11秒前
XCYANG07完成签到,获得积分10
12秒前
留欧发布了新的文献求助10
12秒前
糖糖糖唐完成签到,获得积分10
12秒前
13秒前
cui发布了新的文献求助10
13秒前
酷酷复天发布了新的文献求助30
14秒前
想躺平完成签到,获得积分10
14秒前
phy发布了新的文献求助10
14秒前
徐德宏完成签到 ,获得积分10
15秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Modern Epidemiology, Fourth Edition 5000
Handbook of pharmaceutical excipients, Ninth edition 5000
Digital Twins of Advanced Materials Processing 2000
Weaponeering, Fourth Edition – Two Volume SET 2000
Polymorphism and polytypism in crystals 1000
Signals, Systems, and Signal Processing 610
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 纳米技术 有机化学 生物化学 化学工程 物理 计算机科学 复合材料 内科学 催化作用 物理化学 光电子学 电极 冶金 基因 遗传学
热门帖子
关注 科研通微信公众号,转发送积分 6022608
求助须知:如何正确求助?哪些是违规求助? 7643263
关于积分的说明 16169884
捐赠科研通 5170921
什么是DOI,文献DOI怎么找? 2766913
邀请新用户注册赠送积分活动 1750251
关于科研通互助平台的介绍 1636941