Abstract 5051
Background
A rapid increase in incidence of thyroid cancer has been reported in recent years. However, ambiguities between benign and malignant nodules in cytological features have led to unnecessary thyroidectomy or over-treatment. We aimed to identify DNA methylation markers to accurately classify thyroid nodules, which may be developed into non-invasive screening diagnostics for thyroid cancer to reduce unnecessary thyroid removals.
Methods
Genome-wide DNA methylation profiles of papillary thyroid cancer nodules (tissue, N = 37; plasma, N = 55), benign nodules (tissue, N = 37; plasma, N = 55) and healthy samples (buffy coat, N = 20; plasma, N = 123) were generated by a modified RRBS method. An independent RRBS dataset including 89 malignant and 72 benign nodules was derived from Yim et al. 2019. We adopted Singlera’s MONOD+ assay to interrogate the methylation state of over 4,000,000 CpG sites across more than 200,000 methylation haplotype blocks (MHBs). We identified discriminatory DNA methylation MHBs by Wilcox rank sum test. Using our dataset as a training set, we built several diagnostic models to classify thyroid nodules using machine learning methods. We validated the model by classifying public methylation datasets of thyroid tissues.
Results
We generated an initial list of top 1000 DNA methylation markers and built a classification model by the random forest method. We classified DNA methylation profiles of thyroid nodules published by Yim et al. 2019. Results show that this model achieved a sensitivity of 82%, a specificity of 94.4% and AUC of 0.94 (95% CI, 0.91-0.98). We randomly selected the 2/3 plasma samples as training data to build a prediction model. The model yielded an accuracy of 72% in the validation cohort. We also identified 2392 deferentially methylated regions by comparing benign tissues with malignant tissues. DMR associated genes are highly enriched in many cancer related pathways and MSigDB immunologic signatures, indicating the development of thyroid cancer is closely associated with immune dysfunction.
Conclusions
Our study demonstrates that DNA methylation markers can robustly differentiate thyroid nodules. They are thus promising candidates to develop non-invasive diagnostics for thyroid cancer screening.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The First Affiliated Hospital of Sun Yat-sen University.
Funding
National Natural Science Foundation of China (81772850).
Disclosure
All authors have declared no conflicts of interest.
Resources from the same session
5612 - Evaluation of germ line mutational status among women with triple-negative breast cancer in Russia
Presenter: Elena Shagimardanova
Session: Poster Display session 2
Resources:
Abstract
4142 - Association of derived neutrophil-to-lymphocyte ratio (dNLR) with pathological complete response (pCR) after neoadjuvant chemotherapy (CT)
Presenter: Alberto Ocaña
Session: Poster Display session 2
Resources:
Abstract
1733 - Competing nomogram for late-period breast cancer-specific death in patients with early-stage hormone receptor-positive breast cancer
Presenter: Jianfei Fu
Session: Poster Display session 2
Resources:
Abstract
1978 - A Nomogram to Predict Pathologic Complete Response of Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer Based on Simple Blood Indicators
Presenter: Fanrong Zhang
Session: Poster Display session 2
Resources:
Abstract
3062 - Identification of GSTP1 transferred by extracellular vesicles responsible for adriamycin-resistance in breast cancer cells
Presenter: Sujin Yang
Session: Poster Display session 2
Resources:
Abstract
5274 - Expression of X-linked Inhibitor of Apoptosis Protein (XIAP) and its Association with Clinicopathological Parameters in Invasive Breast Cancers
Presenter: Gayathri Devi
Session: Poster Display session 2
Resources:
Abstract
1324 - The prognostic significance of preoperative tumor marker (CEA, CA15-3) elevation in breast cancer patients
Presenter: Soo Youn Bae
Session: Poster Display session 2
Resources:
Abstract
4877 - Correlation of clinical and pathological features with the tumour microenvironment in DCIS. An institutional experience
Presenter: Ann Eapen
Session: Poster Display session 2
Resources:
Abstract
2471 - Correlation between radiologic complete response (rCR) in contrast-enhanced magnetic resonance imaging (CE-MRI) after neoadjuvant chemotherapy for early breast cancer and pathologic complete response and their impact in recurrence-free survival
Presenter: Ariadna Gasol Cudos
Session: Poster Display session 2
Resources:
Abstract
2632 - Ring-like uptake appearance on dedicated breast positron emission tomography before chemotherapy predicts outcome of neoadjuvant chemotherapy in breast cancer
Presenter: Norio Masumoto
Session: Poster Display session 2
Resources:
Abstract