EBioMedicine (Apr 2023)

Cell-free DNA methylation biomarker for the diagnosis of papillary thyroid carcinomaResearch in context

  • Shubin Hong,
  • Bo Lin,
  • Minjie Xu,
  • Quan Zhang,
  • Zijun Huo,
  • Mingyang Su,
  • Chengcheng Ma,
  • Jinyu Liang,
  • Shuang Yu,
  • Qiye He,
  • Zhixi Su,
  • Yanbing Li,
  • Rui Liu,
  • Zhuming Guo,
  • Weiming Lv,
  • Haipeng Xiao

Journal volume & issue
Vol. 90
p. 104497

Abstract

Read online

Summary: Background: 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). Methods: 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. Findings: 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). Interpretation: 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. Funding: This work was supported by the grants from National Natural Science Foundation of China (82072956 and 81772850).

Keywords