Dataset Information

Next generation sequencing identifies a highly accurate microRNA panel that distinguishes well-differentiated thyroid cancer from benign thyroid nodules

ABSTRACT: Background: Fine needle aspiration biopsy (FNAB) is the gold-standard procedure for diagnosing malignant thyroid nodules. Indeterminate cytology is identified in 10-40% of cases and molecular testing may guide management in this setting. Current commercial options are expensive, and are either sensitive or specific. The aim of this study was to utilize next generation sequencing (NGS) technology to identify informative diversities in the microRNA (miRNA) expression profile of benign versus malignant thyroid nodules. Methods: Ex-vivo FNAB samples were obtained from thyroid specimens of patients that underwent thyroidectomy at a referral center. miRNA levels were determined using NGS and multiplexing technologies. Statistical analyses identified differences between normal and malignant samples and miRNA expression profiles that associate with malignancy were established. The accuracy of the miRNA signature in predicting histological malignancy was validated using a group of patient specimens with indeterminate cytology results. Results: 274 samples were obtained from 102 patients undergoing thyroidectomy. Of these samples, 71% were benign and 29% were malignant. Nineteen miRNAs were identified as statistically different between benign and malignant samples and were used to classify 35 additional nodules with indeterminate cytology (validation). The miRNA panel’s sensitivity, specificity, negative and positive predictive values and overall accuracy were 91%, 100%, 87%, 100% and 94%, respectively. Conclusion: Using NGS technology we identified a panel of 19 miRNAs that may be utilized to distinguish benign from malignant thyroid nodules with indeterminate cytology. Impact: Our panel may classify indeterminate thyroid nodules at higher accuracy than commercially available molecular tests.

ORGANISM(S): Homo sapiens

PROVIDER: GSE116196 | GEO | 2018/12/21

REPOSITORIES: GEO

ACCESS DATA

Similar Datasets

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.

Dataset Information

Next generation sequencing identifies a highly accurate microRNA panel that distinguishes well-differentiated thyroid cancer from benign thyroid nodules

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure