Kuerban Subinuer, Chen Hangyu, Chen Long, Zhang Lei, Li Xuehui, Zhen Baixin, Xiao Hong, Chen Yingzhu, Zhou Haitao, Liang Zhen, Xu Guobing, Tao Yicun, Lin Jian, Kang Xiaozheng
School of Pharmacy, Xinjiang Medical University, Urumqi, 830017, China.
Department of Pharmacy, Peking University Third Hospital, Beijing, 100191, China.
World J Surg Oncol. 2025 Mar 15;23(1):90. doi: 10.1186/s12957-025-03747-9.
A blood-based approach to monitor metastasis and recurrence of esophageal squamous cell carcinoma (ESCC) remains undeveloped. This study aimed to establish a dependable model utilizing cfDNA 5-hydroxymethylcytosines (5hmC) signatures to detect these conditions in ESCC.
The 5hmC-Seal technique was employed to generate comprehensive 5hmC profiles from the plasma cell-free DNA (cfDNA) of 122 ESCC patients, classified into 72 with metastasis, 50 without metastasis, 30 with recurrence, and 92 without recurrence. Initial steps involved identifying distinct hydroxymethylation signatures linked to metastasis and recurrence. Machine learning algorithms were then utilized to construct predictive models.
The study confirmed that 5hmC-based markers are predictive of metastasis and recurrence among ESCC patients. The analysis of 14 5hmC biomarkers revealed a sensitivity of 88.90% and a specificity of 84.00% (AUC = 0.922) in differentiating patients with ESCC metastasis from those without in the validation cohort. Similarly, 11 5hmC biomarkers showed a sensitivity of 93.30% and a specificity of 89.10% (AUC = 0.936) in identifying recurrent versus non-recurrent ESCC cases. Additionally, a wp-score for metastasis and recurrence, derived from the 5hmC marker, prognosticated patient outcomes.
The findings indicate that 5hmC markers from cfDNA serve as effective epigenetic indicators for the non-invasive detection of ESCC metastasis and recurrence.
基于血液监测食管鳞状细胞癌（ESCC）转移和复发的方法尚未得到充分发展。本研究旨在建立一种可靠的模型，利用游离DNA（cfDNA）的5-羟甲基胞嘧啶（5hmC）特征来检测ESCC中的这些情况。
采用5hmC-Seal技术，从122例ESCC患者的血浆游离DNA（cfDNA）中生成全面的5hmC图谱，这些患者分为72例有转移、50例无转移、30例有复发和92例无复发。最初的步骤包括识别与转移和复发相关的独特羟甲基化特征。然后利用机器学习算法构建预测模型。
该研究证实，基于5hmC的标志物可预测ESCC患者的转移和复发。对14个5hmC生物标志物的分析显示，在验证队列中，区分有ESCC转移和无转移患者时，灵敏度为88.90%，特异性为84.00%（AUC = 0.922）。同样，11个5hmC生物标志物在识别复发性与非复发性ESCC病例时，灵敏度为93.30%，特异性为89.10%（AUC = 0.936）。此外，从5hmC标志物得出的转移和复发的wp评分可预测患者的预后。
研究结果表明，来自cfDNA的5hmC标志物可作为非侵入性检测ESCC转移和复发的有效表观遗传指标。
