Dataset Information


A Panel of Plasma Exosomal miRNAs as Potential Biomarkers for Differential Diagnosis of Thyroid Nodules.

ABSTRACT: Background: A liquid biopsy using circulating exosomal genetic materials provides new insights for thyroid cancer diagnosis. This study aimed to identify plasma-derived exosomal biomarkers that could be used for early detection of papillary thyroid carcinoma (PTC). Method: Exosomal miRNAs in plasma were isolated from patients with benign thyroid nodules and patients with PTC. Profiling of exosomal miRNA was performed using RNA sequencing (RNA-seq) to identify miRNA candidates and differentiate the benign from malignant. The validation cohort consisted of 30 patients with benign thyroid nodules, 35 PTC patients, and 31 healthy individuals. Real-time PCR was used to quantify the expression of miRNA candidates. The diagnostic potential of the candidates was evaluated by receiver operating characteristic (ROC) curves. Results: After RNA-seq, eight plasma exosomal miRNAs were selected as candidates. Further validation indicated that the levels of exosomal miR-16-2-3p, miR-223-5p, miR-34c-5p, miR-182-5p, miR-223-3p, and miR-146b-5p were significantly lower in nodules compared to healthy controls (p < 0.0001), whereas miR-16-2-3p and miR-223-5p were significantly higher in the PTC cases than in those with benign nodules (p < 0.05). ROC analyses revealed that the above six miRNAs were potent indicators for detection of thyroid nodules. Meanwhile, miR-16-2-3p and miR-223-5p can be utilized for detecting PTC from benign nodules. Additionally, combined miRNA panels showed increased diagnostic sensitivities and specificities compared to single miRNA markers. Conclusion: Six aberrantly expressed plasma exosomal miRNAs may be used as diagnostic biomarkers to differentiate thyroid nodules from healthy individuals. The panel consisting of miR-16-2-3p, miR-223-5p, miR-101-3p, and miR-34c-5p are eligible for discriminating benign from malignant thyroid nodules.


PROVIDER: S-EPMC7248304 | BioStudies | 2020-01-01

REPOSITORIES: biostudies

Similar Datasets

2019-01-01 | S-EPMC6766139 | BioStudies
2015-01-01 | S-EPMC4500410 | BioStudies
2015-01-01 | S-EPMC4560924 | BioStudies
1000-01-01 | S-EPMC6271659 | BioStudies
2017-01-01 | S-EPMC5347999 | BioStudies
2016-01-01 | S-EPMC5021476 | BioStudies
2019-05-01 | GSE130512 | GEO
| S-EPMC7388447 | BioStudies
2018-01-01 | S-EPMC6308293 | BioStudies
2020-01-01 | S-EPMC7412895 | BioStudies