Circulating MicroRNA Profiles as Potential Biomarkers for Diagnosis of Papillary Thyroid Carcinoma
Bibliographic record
Abstract
CONTEXT: There are no known effective and reliable biomarkers to distinguish benign thyroid nodules from papillary thyroid carcinomas (PTC). Previous studies have indicated that serum microRNA (miRNA) profiles may be diagnostic and/or prognostic markers for numerous other cancers. OBJECTIVE: We studied circulating miRNA profiles in patients with PTC or benign nodules and healthy controls to identify serum miRNA that may be useful as markers for PTC. DESIGN, SETTING, AND PARTICIPANTS: Genome-wide serum miRNA expression profiles were determined using Solexa sequencing followed by extensive quantitative RT-PCR validation in 245 subjects (106 patients with PTC, 95 patients with benign nodules, and 44 healthy controls). A panel of miRNA was used to assess the expression of specific miRNA in the sera and thyroid tissues of patients with PTC or benign nodules. RESULTS: The expression of serum let-7e, miR-151-5p, and miR-222 was significantly increased in PTC cases relative to benign cases and healthy controls. Receiver operating characteristic curve analyses indicated that use of these three miRNA had a high diagnostic sensitivity and specificity for PTC. Serum let-7e, miR-151-5p, and miR-222 levels were found to be well correlated with certain clinicopathological variables, such as nodal status, tumor size, multifocal lesion status, and Tumor-Node-Metastasis stage. Expression of serum miR-151-5p and miR-222 in a subset of PTC patients decreased significantly after tumor excision. Increased expression of miR-151-5p and miR-222 was also found in the tissue of PTC patients. CONCLUSIONS: Our study demonstrates that serum miRNA profiles may be used as novel and minimally invasive diagnostic markers for PTC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".