Differential expression of transforming growth factor-beta in benign vs. papillary thyroid cancer nodules; a potential diagnostic tool?
Bibliographic record
Abstract
BACKGROUND: Thyroid nodules are common, but only 5% of nodules are found to be malignant. In North America, the incidence of thyroid cancer is increasing. Fine needle aspirate (FNA) biopsy is the diagnostic test of choice. Unfortunately, up to 20% of FNAs are non-diagnostic. A specific molecular marker for thyroid cancer is desirable. Evidence suggests that cell signaling through transforming growth factor beta (TGF-β) is important in the development of thyroid cancer. We sought to compare the expression of TGF-β in malignant and benign thyroid nodules. METHODS: From 2008-present, thyroid nodule tissue from thyroidectomy specimens was prospectively collected and stored at -80°C. RNA extraction and reverse transcription was performed on 47 samples (24 papillary thyroid cancer and 23 benign nodules). Quantitative PCR using SYBR green was performed to detect TGF-β-1 and -2. Resulting CT values were normalized against β-actin. Gene expression was calculated using the 2(-ΔC)T method. RESULTS: A significantly greater expression of TGF-β1 (p < 0.0001) was detected in the group of malignant thyroid nodules compared to benign nodules. There was no difference in the expression of TGF-β2 (p = 0.4735) between the two groups. CONCLUSIONS: In this study, we demonstrated that expression of TGF-β1 but not TGF-β2 is significantly increased in papillary thyroid cancer compared to benign thyroid nodules. This may serve as a potential diagnostic marker for papillary thyroid cancer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".