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DNA methylation profiling in thyroid cancer in Chinese population

ABSTRACT: Around 20-30% of Thyroid cancers are difficult to accurately diagnose with fine needle aspiration (FNA) due to their shared features with benign nodules. Although there are molecular tools to assist the diagnosis, such as the Afirma Gene Expression Classifier and ThyroSeqv3, which are the most widely used in the United States, however, these tests all show low positive predictive value and would lead to unnecessary removal of the thyroid in non-cancer patients. In addition, the prediction model of these tests is trained mainly based on the American population. Therefore, it is required to develop a highly accurate test for thyroid nodule diagnostic which could not only increase the PPV, and reduce the unnecessary surgeries, but also is based on Chinese population datasets. To overcome the potential overfitting issues of the prediction model, here we provide a function-guided DNA methylation biomarker selection method through multi-omics datasets. To develop a novel DNA methylation based diagnostic test for thyroid cancer, we characterized the genome-wide DNA methylation pattern in 49 malignant and benign fresh thyroid tissues by using Reduced Representation Bisulfite Sequencing (RRBS) analysis on a single nucleotide level. And we found a group of differentiated DNA methylation sites that could be used to distinguish thyroid cancer and benign tissues.

ORGANISM(S): Homo sapiens

PROVIDER: GSE233560 | GEO | 2025/05/26

REPOSITORIES: GEO

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Project description:BACKGROUND: Novel biomarkers are urgently needed to distinguish between benign and malignant thyroid nodules and detect thyroid cancer in the early stage. The associations between serum IgG N-glycosylation and thyroid cancer risk have been revealed. We aimed to explore the potential of IgG N-glycan traits as biomarkers in the differential diagnosis of thyroid cancer.METHODS: Plasma IgG N-glycome analysis was applied to a discovery cohort followed by independent validation. IgG N-glycan profiles were obtained using a robust quantitative strategy based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. IgG N-glycans were relatively quantified, and classification performance was evaluated based on directly detected and derived glycan traits.RESULTS: Four directly detected glycans were significantly changed in thyroid cancer patients compared to that in non-cancer controls. Derived glycan traits and a classification glycol-panel were generated based on the directly detected glycan traits. In the discovery cohort, derived trait BN (bisecting type neutral N-glycans) and the glyco-panel showed potential in distinguishing between thyroid cancer and non-cancer controls with AUCs of 0.920 and 0.917, respectively. The diagnostic potential was further validated. Derived trait BN and the glycol-panel displayed “accurate” performance (AUC>0.8) in discriminating thyroid cancer from benign thyroid nodules and healthy controls in the validation cohort. Meanwhile, derived trait BN and the glycol-panel also showed diagnostic potential in detecting early-stage thyroid cancer.CONCLUSIONS: IgG N-glycome analysis revealed N-glycomic differences that allow classification of thyroid cancer from non-cancer controls. Our results suggested that derived trait BN and the classification glyco-panel rather than single N-glycans may serve as candidate biomarkers for further validation.

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DNA methylation profiling in thyroid cancer in Chinese population

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