Activated Leukocyte Cell Adhesion Molecule Is a Marker for Thyroid Carcinoma Aggressiveness and Disease-Free Survival
Bibliographic record
Abstract
BACKGROUND: Currently, there are no protein biomarkers for aggressive subtypes of thyroid carcinomas (TC) in clinical use that would allow for early detection and patient management. We hypothesized that activated leukocyte cell adhesion molecule (ALCAM or CD166) expression in thyroid tissues will reveal ALCAM to be a potential diagnostic and/or prognostic marker for TC aggressiveness. METHODS: Forty-five benign and 158 malignant thyroid tissues were analyzed for ALCAM expression using immunohistochemistry. ALCAM expression was correlated with different subtypes and clinicopathological features of TC, as well as patient disease-free survival. RESULTS: Combined membranous and cytoplasmic (total) expression of ALCAM was significantly reduced in patients with poorly/undifferentiated (aggressive) TC as compared to well-differentiated (nonaggressive) tumors (p<0.001; area-under-curve=0.865, sensitivity=82%, specificity=74%). The decreased ALCAM expression in TC correlated significantly with extrathyroidal extension, distant metastasis, and TC histotype. Notably, Kaplan-Meier survival analysis for follow-up data of 134 patients revealed significantly reduced disease-free survival for patients with TC with decreased ALCAM membranous, cytoplasmic, and total expression. Median survival of patients with decreased cytoplasmic ALCAM expression was 6 years, as compared to 13.7 years for patients with higher ALCAM expression (p<0.001). CONCLUSION: ALCAM has the potential to serve as a diagnostic and prognostic biomarker for aggressive TC. This protein can be taken forward for analysis in sera of patients with TC to determine its applicability as a minimally invasive serum biomarker for TC aggressiveness and patient disease-free survival.
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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) | 0.000 | 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 | 0.000 |
| 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".