Secretome proteins as candidate biomarkers for aggressive thyroid carcinomas
Bibliographic record
Abstract
Using proteomics in tandem with bioinformatics, the secretomes of nonaggressive and aggressive thyroid carcinoma (TC) cell lines were analyzed to detect potential biomarkers for tumor aggressiveness. A panel of nine proteins, activated leukocyte cell adhesion molecule (ALCAM/CD166), tyrosine-protein kinase receptor (AXL), amyloid beta A4 protein, amyloid-like protein 2, heterogeneous nuclear ribonucleoprotein K, phosphoglycerate kinase 1, pyruvate kinase isozyme M2, phosphatase 2A inhibitor (SET), and protein kinase C inhibitor protein 1 (14-3-3 zeta) was chosen to confirm their expression in TC patients' sera and tissues. Increased presurgical circulating levels of ALCAM were associated with aggressive tumors (p = 0.04) and presence of lymph node metastasis (p = 0.018). Increased serum AXL levels were associated with extrathyroidal extension (p = 0.027). Furthermore, differential expression of amyloid beta A4 protein, AXL, heterogeneous nuclear ribonucleoprotein K, phosphoglycerate kinase 1, pyruvate kinase muscle isozyme M2, and SET was observed in TC tissues compared to benign nodules. Decreased nuclear expression of AXL can detect malignancy with 90% specificity and 100% sensitivity (AUC = 0.995, p < 0.001). In conclusion, some of these proteins show potential for future development as serum and/or tissue-based biomarkers for TC and warrant further investigation in a large cohort of patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".