Scholarlink

<p>Detection of cancer at early stage can significantly improve the five-year survival rate of patients. Bisulfite-based methylation detection can cause DNA damage, especially in high GC content regions which was associated with the development of cancers. Loss of aberrant methylated CpG sites in cfDNA will lead to the undetectability of certain circulating tumor DNA (ctDNA), consequently may affect the cancer detection. Our study uses enzymatic method to detect whole genome abnormal methylation regions in esophageal squamous cell carcinoma (ESCC). We also provide a pretrained neural network, hybrid of BERT and CNN (BCNN), to identify ctDNA robustly. Maximum posterior probability is utilized to estimate the fraction of ESCC-derived ctDNA in plasma for predicting the risk of ESCC cancer. Our results analysis indicated that enzyme-based whole-genome methylation sequencing retained more longer cfDNA and detected more CpG sites than bisulfite-based method in both gDNA and cfDNA. Enrichment analysis of differentially methylated regions (DMR) showed that top five pathways were associated with ESCC. Compared to traditional models, our BCNN demonstrates the best performance in identifying ctDNA (AUC = 0.970). By estimating the fraction of plasma ctDNA, our BCNN exhibits high accuracy in ESCC detection even at ultralow sequencing depths (AUC = 0.946). Specifically, in the validation cohort, when the specificity is 93.75%, 7 out of 8 early-stage ESCC (TNM Stage I) were identified as positive in our preliminary results. In conclusion, our preliminary results reinforce the idea of employing BCNN as a novel strategy for ESCC early detection in clinical practice. However, to be applied in clinical, further validation with a larger sample size is necessary.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Improved circulating tumor DNA identification for detection of esophageal squamous cell carcinoma by enzymatic methyl sequencing and hybrid neural network

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月26日

查看全文
  • Jiangfeng Shen,
  • Yuqi Ren,
  • Ziyong Mao,
  • Qiuxiang Hu,
  • Yilong Wan,
  • Jiangcun Du,
  • Yanting Lai,
  • Shengxiang Shao,
  • Liuqing Zhang,
  • Hao Wu,
  • Jiaxi Li,
  • Sheng Ju,
  • Xin Tong,
  • Jun Zhao,
  • Lei Cao,
  • Deyi Xiong,
  • ChengCheng Xu,
  • Jun-Chi Xu,
  • Dong Jiang

摘要

Detection of cancer at early stage can significantly improve the five-year survival rate of patients. Bisulfite-based methylation detection can cause DNA damage, especially in high GC content regions which was associated with the development of cancers. Loss of aberrant methylated CpG sites in cfDNA will lead to the undetectability of certain circulating tumor DNA (ctDNA), consequently may affect the cancer detection. Our study uses enzymatic method to detect whole genome abnormal methylation regions in esophageal squamous cell carcinoma (ESCC). We also provide a pretrained neural network, hybrid of BERT and CNN (BCNN), to identify ctDNA robustly. Maximum posterior probability is utilized to estimate the fraction of ESCC-derived ctDNA in plasma for predicting the risk of ESCC cancer. Our results analysis indicated that enzyme-based whole-genome methylation sequencing retained more longer cfDNA and detected more CpG sites than bisulfite-based method in both gDNA and cfDNA. Enrichment analysis of differentially methylated regions (DMR) showed that top five pathways were associated with ESCC. Compared to traditional models, our BCNN demonstrates the best performance in identifying ctDNA (AUC = 0.970). By estimating the fraction of plasma ctDNA, our BCNN exhibits high accuracy in ESCC detection even at ultralow sequencing depths (AUC = 0.946). Specifically, in the validation cohort, when the specificity is 93.75%, 7 out of 8 early-stage ESCC (TNM Stage I) were identified as positive in our preliminary results. In conclusion, our preliminary results reinforce the idea of employing BCNN as a novel strategy for ESCC early detection in clinical practice. However, to be applied in clinical, further validation with a larger sample size is necessary.