Secretome-Based Identification and Characterization of Potential Biomarkers in Thyroid Cancer
Bibliographic record
Abstract
In search of thyroid cancer biomarkers, proteins secreted by thyroid cancer cell lines, papillary-derived TPC-1 and anaplastic-derived CAL62, were analyzed using liquid chromatography-tandem mass spectrometry. Of 46 high-confidence identifications, 6 proteins were considered for verification in thyroid cancer patients' tissue and blood. The localization of two proteins, nucleolin and prothymosin-α (PTMA), was confirmed in TPC-1 and CAL62 cells by confocal microscopy and immunohistochemically in xenografts of TPC-1 cells in NOD/SCID/γ mice and human thyroid cancers (48 tissues). Increased nuclear and cytoplasmic expression of PTMA was observed in anaplastic compared to papillary and poorly differentiated carcinomas. Nuclear expression of nucleolin was observed in all subtypes of thyroid carcinomas, along with faint cytoplasmic expression in anaplastic cancers. Importantly, PTMA, nucleolin, clusterin, cysteine-rich angiogenic inducer 61, enolase 1, and biotinidase were detected in thyroid cancer patients' sera, warranting future analysis to confirm their potential as blood-based thyroid cancer markers. In conclusion, we demonstrated the potential of secretome analysis of thyroid cancer cell lines to identify novel proteins that can be independently verified in cell lines, xenografts, tumor tissues, and blood samples of thyroid cancer patients. These observations support their potential utility as minimally invasive biomarkers for thyroid carcinomas and their application in management of these diseases upon future validation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".