Next generation sequencing identifies a highly accurate microRNA panel that distinguishes well-differentiated thyroid cancer from benign thyroid nodules
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ABSTRACT:
PROVIDER: PRJNA477765 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR7419060.fastq.gz | Fastqsanger.gz | |||
| SRR7419070.fastq.gz | Fastqsanger.gz | |||
| SRR7419080.fastq.gz | Fastqsanger.gz | |||
| SRR7419090.fastq.gz | Fastqsanger.gz | |||
| SRR7419100.fastq.gz | Fastqsanger.gz |
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Project description: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.
2018-12-21 | GSE116196 | GEO
Project description:BACKGROUND: Following fine needle aspiration, 15-30% of thyroid nodules are not clearly benign or malignant. These cytologically indeterminate nodules are often referred for diagnostic surgery, though most prove benign. A novel diagnostic test measuring the expression of 167 genes has shown promise in improving pre-operative risk assessment. We evaluated this test in a prospective, multicenter study. METHODS: Over 19 months, we performed a prospective study at 49 clinical sites enrolling 3,789 patients and collecting 4,812 samples from thyroid nodules >1cm requiring evaluation. We obtained 577 cytologically indeterminate aspirates with corresponding histopathology of excised lesions on 413. Central blinded histopathologic review served as the reference (“gold”) standard. After applying inclusion criteria, gene expression classifier results were obtained for 265 indeterminate nodules used in this analysis, and performance was calculated. RESULTS: 85 of 265 indeterminate nodules were malignant. The gene expression classifier correctly identified 78 of 85 as ‘suspicious’ (92% sensitivity, [84%-97%] 95% CI). Specificity was 52%, [44%-59%]. The negative predictive value was 95%, 94%, and 85%, respectively, for aspirates with AUS/FLUS, FN/SFN, or ‘suspicious’ cytology. Analysis of 7 false negative cases revealed 6 with a paucity of thyroid follicular cells, suggesting that insufficient sampling of the nodule had occurred. CONCLUSIONS: Though individualized clinical care is recommended, these data support consideration of a conservative approach for most patients with indeterminate FNA cytology and benign gene expression classifier results. 265 cytologically indetermine samples, 47 cytologically benign and 55 cytologically malignant samples
2012-06-24 | E-GEOD-34289 | biostudies-arrayexpress
Project description:BACKGROUND: Following fine needle aspiration, 15-30% of thyroid nodules are not clearly benign or malignant. These cytologically indeterminate nodules are often referred for diagnostic surgery, though most prove benign. A novel diagnostic test measuring the expression of 167 genes has shown promise in improving pre-operative risk assessment. We evaluated this test in a prospective, multicenter study. METHODS: Over 19 months, we performed a prospective study at 49 clinical sites enrolling 3,789 patients and collecting 4,812 samples from thyroid nodules >1cm requiring evaluation. We obtained 577 cytologically indeterminate aspirates with corresponding histopathology of excised lesions on 413. Central blinded histopathologic review served as the reference (“gold”) standard. After applying inclusion criteria, gene expression classifier results were obtained for 265 indeterminate nodules used in this analysis, and performance was calculated. RESULTS: 85 of 265 indeterminate nodules were malignant. The gene expression classifier correctly identified 78 of 85 as ‘suspicious’ (92% sensitivity, [84%-97%] 95% CI). Specificity was 52%, [44%-59%]. The negative predictive value was 95%, 94%, and 85%, respectively, for aspirates with AUS/FLUS, FN/SFN, or ‘suspicious’ cytology. Analysis of 7 false negative cases revealed 6 with a paucity of thyroid follicular cells, suggesting that insufficient sampling of the nodule had occurred. CONCLUSIONS: Though individualized clinical care is recommended, these data support consideration of a conservative approach for most patients with indeterminate FNA cytology and benign gene expression classifier results.
2012-06-25 | GSE34289 | GEO
Project description:<p><strong>BACKGROUND:</strong> 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.</p><p><strong>METHODS:</strong> 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.</p><p><strong>RESULTS: </strong>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.</p><p><strong>CONCLUSIONS:</strong> 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.</p>
2021-09-01 | MTBLS2935 | MetaboLights
Project description:A serum miRNA combination could be a favourable classifier to differentiate malignant pulmonary nodules from benign pulmonary nodules.
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Project description:Development of a non-invasive, whole blood microarray gene expression signature that accurately distinguishes malignant from benign lung nodules detected by LDCT and the successful transition of that signature to a custom NanoString panel with no loss of accuracy.
2018-12-20 | GSE108375 | GEO
Project description:PURPOSE: Thyroid cancer is frequently difficult to diagnose due to an overlap of cytological features between malignant and benign nodules. This overlap leads to unnecessary removal of the thyroid in patients without cancer. While providing some improvement over cytopathologic diagnostics, molecular methods frequently fail to provide a correct diagnosis for thyroid nodules. These approaches are based on the difference between malignant nodules and normal adjacent thyroid tissue and assume that normal thyroid tissues are the same as benign nodules. However, in contrast to normal thyroid tissues, benign thyroid nodules can contain genetic alterations that can be found in cancerous nodules. PATIENTS AND METHODS: For the development of a new molecular diagnostic test for thyroid cancer, we evaluated DNA methylation in 109 thyroid tissues by using genome wide single base resolution DNA methylation analysis (Reduced Representation Bisulfite Sequencing). The test was validated in the retrospective cohort containing 64 thyroid nodules. RESULTS: By conducting Reduced Representation Bisulfite Sequencing in 109 thyroid specimens, we found significant differences between normal tissue, benign nodules, and cancer. Based on tissue-specific epigenetic signatures for benign and malignant nodules, we developed a new epigenetic approach for thyroid diagnostics. According to the validation cohort, our test has an estimated specificity of 97% (95% CI, 80 to 100), sensitivity of 100% (95% CI, 86 to 100), PPV of 97% (95% CI, 82-100), NPV of 100% (95%, 85 to 100). CONCLUSION: These data show that epigenetic testing can provide outstanding diagnostic accuracy for thyroid nodules by evaluating tissue specific DNA methylation.
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Project description:Next generation sequencing identifies a highly accurate microRNA panel that distinguishes well-differentiated thyroid cancer from benign thyroid nodules
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